/* cmp 8.7.9 18 April 2024
   Copyright (C) 2007-24 David Roodman

   This program is free software: you can redistribute it and/or modify
   it under the terms of the GNU General Public License as published by
   the Free Software Foundation, either version 3 of the License, or
   (at your option) any later version.

   This program is distributed in the hope that it will be useful,
   but WITHOUT ANY WARRANTY; without even the implied warranty of
   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   GNU General Public License for more details.

   You should have received a copy of the GNU General Public License
   along with this program. If not, see <http://www.gnu.org/licenses/>. */

local cmp_cont 1
local cmp_left 2
local cmp_right 3
local cmp_probit 4
local cmp_oprobit 5
local cmp_mprobit 6
local cmp_int 7
local cmp_probity1 8
local cmp_frac 10
local mprobit_ind_base 20
local roprobit_ind_base 40

mata
mata clear
mata set matastrict on
mata set mataoptimize on
mata set matalnum off

struct smatrix {
	real matrix M
}

struct ssmatrix {
	struct smatrix colvector M
}

struct mprobit_group {
	real scalar d, out // dimension - 1; eq of chosen alternative
	real rowvector in, res // eqs of remaining alternatives; indices in ECens to hold relative differences
}

struct scores {
	real rowvector ThetaScores, CutScores  // in nonhierarchical models, vectors specifying relevant cols of master score matrix, S
	struct smatrix rowvector TScores, SigScores, GammaScores // SigScores only used at top level, to refer to cols of S. In hierarchical models, TScores[L] holds base Sig scores
}

struct scorescol {
	struct scores colvector M
}

struct subview {  // info associated with subsets of data defined by given combinations of indicator values
  real matrix EUncens
  pointer (real matrix) scalar pECens, pF, pEt, pFt
  real colvector Fi  // temporary var used in lf1(); store here in case setcol(pX, Fi...) leads to pX=&Fi and Fi should be preserved
	struct smatrix colvector theta, y, Lt, Ut, yL
	struct smatrix matrix dOmega_dGamma
	struct scorescol rowvector Scores  // one col for each level, one col for each draw
	real matrix Yi
	real colvector subsample, SubsampleInds, one2N
	real scalar GHKStart, GHKStartTrunc  // starting indexes in ghk2() point structure
	real scalar d_uncens, d_cens, d2_cens, d_two_cens, d_oprobit, d_trunc, d_frac, NFracCombs, N
	real scalar NumCuts  // number of cuts in ordered probit eqs relevant for *these* observations
	real colvector vNumCuts // number of cuts per eq for the eq for *these* observations
	real matrix dSig_dLTSig  // derivative of Sig w.r.t. its lower triangle
	real scalar bounded  // d_oprobit? d_one_cens+1..d_cens:J(1,0,0)
	real scalar N_perm
	real colvector CensLTInds // indexes of lower triangle of a vectorized square matrix of dimension d_cens
	real colvector WeightProduct
	real rowvector TheseInds // user-provided indicator values
	real rowvector uncens, two_cens, oprobit, cens, cens_nonrobase, trunc, one2d_trunc, frac, censnonfrac
	real rowvector cens_uncens // one_cens, oprobit, uncens
	real rowvector SigIndsUncens // Indexes, within the vectorized upper triangle of Sig, entries for the eqs uncens at these obs
	real rowvector SigIndsTrunc // Ditto for trunc obs
	real rowvector SigIndsCensUncens // Permutes vectorized upper triangle of Sig to order corresponding to cens eqs first
	real rowvector CutInds // Indexes, within full list of oprobit cuts, of those relevant for the equations in these observations
  real rowvector NotBaseEq  // indicators of which eqs are not mprobit or roprobit base eqs
	real matrix QSig   // correction factor for trial cov matrix reflecting scores of passed "error" (XB,-XB,Y-XB, or XB-Y) w.r.t XB, and relative differencing
	real matrix Sig     // Sig, reflecting that correction
	real matrix Omega   // invGamma * Sig * invGamma' in Gamma models. Slips into place of "Sig".
	real matrix QE     // correction factors for dlnL/dE
	real matrix QEinvGamma, invGammaQSigD
	real scalar dCensNonrobase
	real matrix J_d_uncens_d_cens_0, J_d_cens_d_0, J_d2_cens_d2_0, J_N_1_0
	// used in computations. Store here to avoid repeatedly destroying and reallocating with J():
	real matrix dphi_dE, dPhi_dE, dPhi_dSig, dPhi_dcuts, dPhi_dF, dPhi_dpF, dPhi_dEt, dphi_dSig, dPhi_dSigt, dPhi_dpE_dSig, _dPhi_dpE_dSig, _dPhi_dpF_dSig, dPhi_dpF_dSig, EDE
	struct smatrix colvector dPhi_dpE, dPhi_dpSig
	struct ssmatrix colvector XU
	struct smatrix colvector id // for each level, colvector of observation indexes that explodes group-level data to obs-level data, within this view
	pointer (real rowvector) colvector roprobit_QE // for each roprobit permutation, matrix that effects roprobit differencing of ECens columns
	pointer (real rowvector) colvector roprobit_Q_Sig // ditto for vech() of Sigma of censored E columns
	struct mprobit_group colvector mprobit
	real matrix halfDmatrix
	real matrix FracCombs
	struct smatrix rowvector frac_QE, frac_QSig, yProd // all products of frac prob y's

	pointer (struct subview scalar) scalar next
}

struct RE { // info associated with given level of model. Top level also holds various globals as an alternative to storing them as separate externals, references to which are slow
	real scalar R // number of draws. (*REs)[l].R = NumREDraws[l+1]
	real scalar d, d2 // number of RCs and REs, corresponding triangular number
	real rowvector one2d, one2R, J1R0, JN12
  pointer(real matrix) colvector JN1pQuadX
	real scalar HasRC
	real matrix J_N_NEq_0
	real rowvector REInds // indexes, within vector of effects, of random effects
	struct smatrix colvector RCInds // for each equation, indexes of equation's set of random-coefficient effects within vector of effects
	real rowvector Eqs // indexes of equations in this level--for upper levels, ones with REs or RCs
	real rowvector REEqs // indexes of equations, within Eqs, with REs (as distinct from RCs)
	real rowvector GammaEqs // indexes of equations in this level that have REs or RCs or depend on them indirectly through Gamma
	real scalar NEq // number of equations
	real rowvector NEff // number of effects/coefficients by equation, one entry for each eq that has any effects
	struct smatrix colvector X // NEq-vector of data matrices for variables with random coefficients
	struct smatrix rowvector U // draws/observation-vector of N_g x d sets of draws
	pointer(real matrix) matrix pXU // draws/observation x d matrix of matrices of X, U products; coefficients on these, elements of T, set contribution to simulated error
	struct smatrix matrix TotalEffect // matrices of, for each draw set and equation, total simulated effects at this level: RE + RC*(RC vars)
	real matrix Sig, T, invGamma
	real matrix D // derivative of vech(Sig) w.r.t lnsigs and atanhrhos
	real matrix dSigdParams // derivative of sig, vech(rho) vector w.r.t. vector of actual sig, rho parameters, reflecting "exchangeable" and "independent" options
	real scalar NSigParams
  real scalar N // number of groups at this level
	real colvector one2N, J_N_1_0, J_N_0_0
	real matrix IDRanges // id ranges for each group in data set, as returned by panelsetup()
	real colvector IDRangeLengths // lengths of those ranges
	real matrix IDRangesGroup // N x 1, id ranges for each group's subgroups in the next level down
  struct smatrix rowvector Subscript
	real matrix id // group id var
	real rowvector sig, rho // vector of error variances only, and atanhrho's
	real scalar covAcross // cross- and within-eq covariance type: unstructured, exchangeable, independent; indexed by *included* equations at this level
	real colvector covWithin, FixedSigs
	real matrix FixedRhos
	struct smatrix colvector theta
	real colvector Weights // weights at this level, one obs per group, renormalized if pweights or aweights
	real colvector ToAdapt // by group, state of adaptation attempt for this iteration. 2 = ordinary adaptation needed; 1 = adaptation needed having been reset because of divergence; 0 = converged
	real scalar lnNumREDraws
	real scalar lnLlimits
	real matrix lnLByDraw // lnLByDraw acculumulates sums of them at next level up, by draw
	pointer (real matrix) scalar plnL // lnL holds latest likelihoods at this level, points to 1e-6 lnf return arg at top level
	real colvector QuadW, QuadX // quadrature weights
	struct smatrix colvector QuadMean, QuadSD // by group, estimated RE/RC mean and variance, for adaptive quadrature
	real rowvector lnnormaldenQuadX
	transmorphic scalar QuadXAdapt // asarray("real", l), one set of adaptive shifters per multi-level draw combination; first index is always 1, to prevent vector length 0
	real scalar AdaptivePhaseThisIter, AdaptiveShift
  real matrix Rho
  real colvector RCk // number of X vars in each random coefficient
}

class cmp_model {
	pointer (struct RE scalar) scalar REs, base
	pointer (struct subview scalar) scalar subviews
	struct smatrix colvector y, Lt, Ut, yL
	real matrix Theta // individual theta's in one matrix
	real scalar d, L, _todo, ghkDraws, ghkScramble, REScramble, REAnti, NumRoprobitGroups, MaxCuts, NSimEff
	real matrix MprobitGroupInds, RoprobitGroupInds
	real colvector NumREDraws
	real rowvector NonbaseCases
	real scalar reverse
	string scalar ghkType, REType
	real matrix Gamma
  pointer (real matrix) scalar pOmega
	real matrix dSig_dT // derivative of vech(Sig) w.r.t vech(cholesky(Sig))
	real colvector WeightProduct // obs-level product of weights at all levels, for weighting scores
	transmorphic ghk2DrawSet
	real scalar ghkAnti, NumCuts, HasGamma, SigXform, d_cens
	real matrix Eqs, GammaId, NumEff
	real colvector vNumCuts
	real matrix cuts
	real colvector G // number of Gamma params in each eq
	pointer(real matrix) colvector GammaIndByEq // d x 1 vector of pointers to rowvectors indicating which columns of Gamma, for the given row, are real model parameters
	real matrix GammaInd // same information in a 2-col matrix, each row the coordinates in Gamma of a real parameter
	struct smatrix matrix dOmega_dGamma
	real rowvector trunceqs, intregeqs
	real scalar Quadrature, AdaptivePhaseThisEst, WillAdapt, QuadTol, QuadIter, Adapted, AdaptNextTime
	real rowvector Lastb
	real scalar LastlnLLastIter, LastlnLThisIter, LastIter
	real matrix Idd
	real rowvector vKd, vIKI, vLd
	real matrix indicators
	real matrix S0 // empty, pre-allocated matrix to build score matrix S
	struct scores scalar Scores // column indices in S corresponding to different parameter groups
	string rowvector indVars, LtVars, UtVars, yLVars
	real rowvector ThisDraw
	real scalar h // if computing 2nd derivatives most recent h used
	struct smatrix colvector X // NEq-vector of data matrices--needed only in gfX() estimation, to expand scores to one per regressor
  struct smatrix colvector sTScores, sGammaScores

	void new(), BuildXU(), BuildTotalEffects(),
				setReverse(), setSigXform(), setQuadTol(), setQuadIter(), setGHKType(), setMaxCuts(), setindVars(), setLtVars(), setUtVars(), setyLVars(),
				setGHKAnti(), setGHKDraws(), setGHKScramble(), setQuadrature(), setd(), setL(), settodo(),
				setREAnti(), setREType(), setREScramble(), setEqs(), setGammaI(), setNumEff(), setNumMprobitGroups(), setNumRoprobitGroups(),
				setMprobitGroupInds(), setRoprobitGroupInds(), setNonbaseCases(), setvNumCuts(), settrunceqs(), setintregeqs(), setNumREDraws(), setGammaInd(),
				setAdaptNow(), setWillAdapt(), lf1(), gf1(), SaveSomeResults()
  static void scoreAccum(), setcol(), PasteAndAdvance(), CheckPrime()
  real colvector lnLCensored(), lnLTrunc()
  static real colvector quadrowsum_lnnormalden(), binormal2(), binormalGenz(), lnLContinuous(), normal2(), vecbinormal(), vecbinormal2(), vecmultinormal()
  static real rowvector vSigInds()
  static real matrix _panelsum(), Mdivs(), insert(), _PermuteTies(), dPhi_dpE_dSig(), dSigdsigrhos(), neg_half_E_Dinvsym_E(), PermuteTies(), QE2QSig(), SpGrGetSeq()
  static pointer(real matrix) scalar Xdotv(), getcol()
  static pointer (real matrix) rowvector SpGr(), SpGrKronProd()
  static pointer colvector GQNn1d(), GQNw1d(), KPNn1d(), KPNw1d()
	static void _st_view()
	real scalar getGHKDraws(), cmp_init()
}

void cmp_model::new()
	Adapted = AdaptivePhaseThisEst = WillAdapt = AdaptNextTime = HasGamma = ghkScramble = REScramble = 0

void cmp_model::setcol(pointer(real matrix) scalar pX, real rowvector c, real matrix v)
  if (cols(*pX)==cols(c))
    pX = &v
  else
    (*pX)[,c] = v

// return pointer to chosen columns of a matrix, but don't duplicate data if return value is whole matrix
pointer (real matrix) scalar cmp_model::getcol(real matrix A, real vector p)
	return(length(p)==cols(A)? &A : &A[,p])

real matrix cmp_model::Mdivs(real matrix X, real scalar c)
  return(c==1? X : (c==-1? -X : X/c))

void cmp_model::scoreAccum(real matrix S, real scalar r, real colvector v, real matrix X)
  S = r==1? v :* X : S + v :* X

pointer(real matrix) scalar cmp_model::Xdotv(real matrix X, real colvector v)
  return(rows(v)? &(X :* v) : &X)

// insert row vector into a matrix at specified row
real matrix cmp_model::insert(real matrix X, real scalar i, real rowvector newrow)
	return (i==1? newrow\X : (i==rows(X)+1? X\newrow : X[|.,.\i-1,.|] \ newrow \ X[|i,.\.,.|]))

void cmp_model::setd       (real scalar t) d  = t
void cmp_model::setL       (real scalar t) L  = t
void cmp_model::settodo    (real scalar t) _todo  = t
void cmp_model::setMaxCuts (real scalar t) MaxCuts  = t
void cmp_model::setReverse (real scalar t) reverse  = t
void cmp_model::setSigXform(real scalar t) SigXform = t
void cmp_model::setQuadTol (real scalar t) QuadTol  = t
void cmp_model::setQuadIter(real scalar t) QuadIter = t
void cmp_model::setGHKType(string scalar t) ghkType = t
void cmp_model::setGHKAnti(real scalar t) ghkAnti = t
void cmp_model::setGHKDraws(real scalar t) ghkDraws = t
real scalar cmp_model::getGHKDraws() return(ghkDraws)
void cmp_model::setGHKScramble(string scalar t) ghkScramble = select(0..3, ("", "sqrt", "negsqrt", "fl"):==t)
void cmp_model::setREType    (string scalar t) REType = t
void cmp_model::setREAnti    (real scalar t) REAnti = t
void cmp_model::setNumREDraws(real colvector t) NumREDraws = 1 \ t*REAnti
void cmp_model::setREScramble(string scalar t) REScramble = select(0..3, ("", "sqrt", "negsqrt", "fl"):==t)
void cmp_model::setQuadrature(real scalar t) Quadrature = t
void cmp_model::setEqs(real matrix t) Eqs = t
void cmp_model::setNumEff(real matrix t) NumEff = t
void cmp_model::setNumRoprobitGroups(real matrix t) NumRoprobitGroups = t
void cmp_model::setMprobitGroupInds(real matrix t) MprobitGroupInds = t
void cmp_model::setRoprobitGroupInds(real matrix t) NumRoprobitGroups = rows(RoprobitGroupInds = t)
void cmp_model::setNonbaseCases(real rowvector t) NonbaseCases = t
void cmp_model::setvNumCuts(real colvector t) NumCuts=sum(vNumCuts = t)
void cmp_model::settrunceqs(real rowvector t) trunceqs = t
void cmp_model::setintregeqs(real rowvector t) intregeqs = t
void cmp_model::setindVars(string scalar t) indVars = tokens(t)
void cmp_model::setyLVars(string scalar t) yLVars = tokens(t)
void cmp_model::setLtVars(string scalar t) LtVars = tokens(t)
void cmp_model::setUtVars(string scalar t) UtVars = tokens(t)
void cmp_model::setAdaptNow(real scalar t) Adapted = AdaptivePhaseThisEst = t

void cmp_model::setWillAdapt(real scalar t) {
	WillAdapt  = t
	Adapted = AdaptivePhaseThisEst = AdaptNextTime = 0
	Lastb = J(1,0,0)
}

void cmp_model::setGammaI(real matrix t) {
	real scalar i
	GammaId = t
	for (i=d-2; i>0; i--)
		GammaId = GammaId * t
}

void cmp_model::setGammaInd(real matrix t) {
	real scalar i
	GammaInd = t
	if (HasGamma = rows(t)) {
		GammaIndByEq = J(d, 1, NULL)
		for (i=d; i; i--)
			GammaIndByEq[i] = &(select(GammaInd, GammaInd[,2]:==i)[,1])
	}
}

real matrix cmp_model::_panelsum(real matrix X, real matrix W, real matrix info)
	return (rows(W)? panelsum(X, W, info) : panelsum(X, info))

// fast(?) computation of a :+ quadrowsum(lnnormalden(X))
real colvector cmp_model::quadrowsum_lnnormalden(real matrix X, real scalar a)
	return ( (a - 0.91893853320467267 /*ln(2pi)/2*/ * cols(X)) :- .5*quadrowsum(X:*X) )

// paste columns B into matrix A at starting index i, then advance index; for efficiency, overwrite A = B when possible
void cmp_model::PasteAndAdvance(real matrix A, real scalar i, real matrix B) {
	if (cols(B)) {
		real scalar t
		t = i + cols(B)
		if (cols(A) == cols(B))
      A = B
    else
      A[|.,i \ .,t-1|] = B
		i = t
	}
}

// prepare matrix to transform scores w.r.t. elements of Sigma to ones w.r.t. lnsig's and rho's
real matrix cmp_model::dSigdsigrhos(real scalar SigXform, real rowvector sig, real matrix Sig, real rowvector rho, real matrix Rho) {
	real matrix D, t, t2; real scalar i, j, k, _d, _d2
	_d = cols(sig); _d2 = _d + cols(rho)
	D = I(_d2)
	for (k=1; k<=_d; k++) {  // derivatives of Sigma w.r.t. lnsig's
		t2 = SigXform? Sig[k,] : (_d>1? Rho[k,]:*sig : sig)
		(t = J(_d,_d,0))[k,] = t2
		t[,k] = t[,k] + t2'
		D[,k] = vech(t)
	}
	if (_d > 1) {  // derivatives of Sigma w.r.t. rho's
		for (j=1; j<=_d; j++)
			for (i=j+1; i<=_d; i++) {
				(t = J(_d,_d,0))[i,j] = sig[i] * sig[j]
				D[,k++] = vech(t)
			}
		if (SigXform) {
			t = cosh(rho)
			D[|.,_d+1 \ .,.|] = D[|.,_d+1 \ .,.|] :/ (t:*t)  // Datanh=cosh^2
		}
	}
	return(D)
}

// Check whether all entries in vector are prime
void cmp_model::CheckPrime(real vector v) {
	real scalar i, j
	for (i=length(v); i; i--)
		for (j=floor(sqrt(v[i])); j>1; j--)
			if (mod(v[i], j) == 0) {
				printf("Note: %f is not prime. Prime draw counts are more reliable.\n\n", v[i])
				return
			}
}

// Given ranking potentially with ties, return matrix of all un-tied rankings consistent with it, one per row
real matrix cmp_model::PermuteTies(real vector v) {
	real colvector Indexes; real matrix  TiedRanges
	pragma unset   Indexes; pragma unset TiedRanges
	minindex(v, ., Indexes, TiedRanges)
	TiedRanges[,2] = rowsum(TiedRanges) :- 1
	return (_PermuteTies(Indexes, TiedRanges', rows(TiedRanges))')
}
real matrix cmp_model::_PermuteTies(real colvector Indexes, real matrix TiedRanges, real scalar ThisRank) {
	real colvector info, p, t; real matrix RetVal
	RetVal = J(rows(Indexes), 0, .)
	info = cvpermutesetup(Indexes[| p = TiedRanges[,ThisRank] |], 0)
	while (rows(t = cvpermute(info))) {
		Indexes[|p|] = t
		RetVal = RetVal, ( ThisRank==1? Indexes : _PermuteTies(Indexes, TiedRanges, ThisRank-1) )
	}
	return (RetVal)
}

// given indexes for variables, and dimension of variance matrix, return corresponding indexes in vectorized variance matrix
// e.g., (1,3) -> ((1,1), (3,1), (3,3)) -> (1, 3, 6)
real rowvector cmp_model::vSigInds(real rowvector inds, real scalar d)
	return (vech(invvech(1::d*(d+1)*0.5)[inds,inds])')

// Given transformation matrix for errors, return transformation matrix for vech(covar)
real matrix cmp_model::QE2QSig(real matrix QE)
	return (Lmatrix(cols(QE))*(QE#QE)'Dmatrix(rows(QE)))

// compute normal(F) - normal(E) while maximizing precision
// In Mata, 1 - normal(10) should = normal(-10) but the former = 0 because normal(10) is close to 1
// Ergo the best way to compute the former is to do the latter
// F = . means +infinity. E = . means -infinity
real colvector cmp_model::normal2(real colvector E, real colvector F) {
	real colvector sign
	sign = F+E:<0
	sign = sign + sign :- 1
	return (abs(normal(sign:*F) - normal(sign:*E)))
}

// integral of bivariate normal from -infinity to E1, F2 to E2, done to maximize precision as in normal2()
real colvector cmp_model::binormal2(real colvector E1, real matrix E2, real matrix F2, real scalar rho) {
	real colvector sign
	sign = E2 + F2 :< 0
	sign = sign + sign :- 1
	return (abs(binormalGenz(E1, sign:*E2, rho, sign) - binormalGenz(E1, sign:*F2, rho, sign)))
}

// Based on Genz 2004 Fortran code, https://web.archive.org/web/20180922125509/http://www.math.wsu.edu/faculty/genz/software/fort77/tvpack.f
// Alan Genz, "Numerical computation of rectangular bivariate and trivariate normal and t probabilities," Statistics and Computing, August 2004, Volume 14, Issue 3, pp 251-60.
//
//    A function for computing bivariate normal probabilities.
//    This function is based on the method described by
//        Drezner, Z and G.O. Wesolowsky, (1989), On the computation of the bivariate normal integral, Journal of Statist. Comput. Simul. 35, pp. 101-107,
//    with major modifications for double precision, and for |r| close to 1.
//
// Calculates the probability that X < x1 and Y < x2.
//
// Parameters
//   x1  integration limit
//   x2  integration limit
//   r   correlation coefficient
//   m   optional column vector of +/-1 multipliers for r
real colvector cmp_model::binormalGenz(real colvector x1, real colvector x2, real scalar r, | real colvector m) {
	real scalar a, as, absr, asinr; real colvector _X, W, B, C, _D, retval, BS, HS, HK, negx2, normalx1, normalx2, normalnegx1, normalnegx2; real rowvector xs, rs, sn, sn2; pointer (real colvector) px2

	if (r>=.) return (J(rows(x1),1,.))
	if (r==0) return (normal(x1):*normal(x2))

	if ((absr=abs(r)) < 0.925) {
		// Gauss Legendre Points and Weights
		if (absr < 0.3) {
			_X = -0.9324695142031522D+00, -0.6612093864662647D+00, -0.2386191860831970D+00
			W =  0.1713244923791705D+00,  0.3607615730481384D+00,  0.4679139345726904D+00
		} else if (absr < 0.75) {
			_X = -0.9815606342467191D+00, -0.9041172563704750D+00, -0.7699026741943050D+00, -0.5873179542866171D+00, -0.3678314989981802D+00, -0.1252334085114692D+00
			W =  0.4717533638651177D-01,  0.1069393259953183D+00,  0.1600783285433464D+00,  0.2031674267230659D+00,  0.2334925365383547D+00,  0.2491470458134029D+00
		} else {
			_X = -0.9931285991850949D+00, -0.9639719272779138D+00, -0.9122344282513259D+00, -0.8391169718222188D+00, -0.7463319064601508D+00,
			    -0.6360536807265150D+00, -0.5108670019508271D+00, -0.3737060887154196D+00, -0.2277858511416451D+00, -0.7652652113349733D-01

			W =  0.1761400713915212D-01,  0.4060142980038694D-01,  0.6267204833410906D-01,  0.8327674157670475D-01,  0.1019301198172404D+00,
			     0.1181945319615184D+00,  0.1316886384491766D+00,  0.1420961093183821D+00,  0.1491729864726037D+00,  0.1527533871307259D+00
		}
		_X = 1:-_X, 1:+_X
		W = W, W

		HK = x1:*x2; if (rows(m)) HK = m :* HK
		HS = x1:*x1 + x2:*x2
		asinr = asin(r)
		sn = sin((asinr * 0.5) * _X); sn2 = sn + sn
		asinr = asinr * 0.079577471545947673 // 1/(2 * tau)
		return ( normal(x1) :* normal(x2) + quadrowsum(W :* exp((HK * sn2 :- HS) :/ (2 :- sn2 :* sn))) :* (rows(m)? m * asinr : asinr) )
	}

	negx2 = -x2
	if (r<0) px2 = &x2
		else px2 = &negx2
	if (rows(m)) {
		px2 = &(m :* *px2)
		normalx1    = normal( x1)
		normalx2    = normal( x2)
		normalnegx1 = normal(-x1)
		normalnegx2 = normal(negx2)
	}
	HK = x1 :* *px2 * 0.5
	if (absr < 1) {
		_X = -0.9931285991850949D+00, -0.9639719272779138D+00, -0.9122344282513259D+00, -0.8391169718222188D+00, -0.7463319064601508D+00,
		    -0.6360536807265150D+00, -0.5108670019508271D+00, -0.3737060887154196D+00, -0.2277858511416451D+00, -0.7652652113349733D-01

		W =  0.1761400713915212D-01,  0.4060142980038694D-01,  0.6267204833410906D-01,  0.8327674157670475D-01,  0.1019301198172404D+00,
		     0.1181945319615184D+00,  0.1316886384491766D+00,  0.1420961093183821D+00,  0.1491729864726037D+00,  0.1527533871307259D+00
		_X = 1:-_X, 1:+_X
		W = W, W

		a = sqrt(as = (1-r)*(1+r))
		B = abs(x1 + *px2); BS = B :* B
		C = 2 :+ HK
		_D = 6 :+ HK
		asinr = HK - BS/(as+as)
		retval = a * exp(asinr) :* (1:-C:*(BS:-as):*(0.083333333333333333:-_D:*BS*0.0020833333333333333) + C:*_D:*(as*as*0.00625)) -
		              exp(HK) :* normal(B/-a) :* B :* (/*sqrt(tau)*/2.5066282746310002 :- C:*BS:*(/*sqrt(tau)/12*/0.20888568955258335:-_D:*BS*/*sqrt(tau)/480*/0.0052221422388145835))

		a = a * 0.5
		xs = a*_X; xs = xs :* xs
		rs = sqrt(1 :- xs)
		asinr = HK :- BS * 1:/(xs+xs)
		retval = (retval + quadrowsum((a*W) :* (exp(asinr) :* ( exp(HK*((1:-rs):/(1:+rs))):/rs - (1 :+ C*(xs*.25):*(1:+_D*(xs*.125))) ))))/-6.2831853071795862
		if (rows(m)) {
			if (r<0)
				return ((m:<0):*(retval + rowmin((normalx1,normalx2))) - (m:>0):*(retval + (x1:>=negx2):*((x1:>x2):*(normalnegx1-normalx2)+(x1:<=x2):*(normalnegx2-normalx1)))) // slow but max precision
			return     ((m:>0):*(retval + rowmin((normalx1,normalx2))) - (m:<0):*((x1:>=negx2):*(retval + (x1:>x2):*(normalnegx1-normalx2)+(x1:<=x2):*(normalnegx2-normalx1))))
		}
		if (r<0)
			return ((x1:>=negx2):*((x1:>x2):*(normal(x2)-normal(-x1))+(x1:<=x2):*(normal(x1)-normal(negx2))) - retval) // slow but max precision
		return (retval + normal(rowmin((x1,x2))))
	}
	if (rows(m)) {
		if (r<0)
			return ((m:<0):*(rowmin((normalx1,normalx2))) - (m:>0):*((x1:>=negx2):*((x1:>x2):*(normalnegx1-normalx2)+(x1:<=x2):*(normalnegx2-normalx1)))) // slow but max precision
		return     ((m:>0):*(rowmin((normalx1,normalx2))) - (m:<0):*((x1:>=negx2):*((x1:>x2):*(normalnegx1-normalx2)+(x1:<=x2):*(normalnegx2-normalx1))))
	}
	if (r<0)
		return ((x1:>=negx2):*((x1:>x2):*(normal(x2)-normal(-x1))+(x1:<=x2):*(normal(x1)-normal(negx2)))) // slow but max precision
	return (normal(rowmin((x1,x2))))
}

/*SpGr(dim, k): function for generating nodes & weights for nested sparse grids integration with Gaussian weights
dim  : dimension of the integration problem
k    : Accuracy level. The rule will be exact for polynomial up to total order 2k-1
Returns 1x2 vector of pointers to matrices: nodes and weights
correspond to Heiss and Winschel GQN & KPN types
Adapted with permission from Florian Heiss & Viktor Winschel, https://web.archive.org/web/20181007012445/http://sparse-grids.de/stata/build_nwspgr.do.
Sources: Florian Heiss and Viktor Winschel, "Likelihood approximation by numerical integration on sparse grids", Journal of Econometrics 144(1): 62-80.
         A. Genz and B. D. Keister (1996): "Fully symmetric interpolatory rules for multiple integrals over infinite regions with Gaussian weight." Journal of Computational and Applied Mathematics 71, 299-309.*/
pointer (real matrix) rowvector cmp_model::SpGr(real scalar dim, real scalar k) {
	pointer colvector n1d, w1d
	real matrix nodes, is, t
	pointer (real matrix) rowvector newnw
	real colvector weights, sortvec, keep, R1d, Rq
	real rowvector midx
	real scalar q, bq, j, r

	if (dim <= 2) { // "sparse" grids only sparser for dim > 2
		nodes = *GQNn1d()[k]; weights = *GQNw1d()[k] // use non-nested nodes
		nodes = nodes \ -nodes[|1+mod(k,2)\.|]; weights = weights \ weights[|1+mod(k,2)\.|]
		return (dim==1? (&              nodes          , & weights         ) :
		                (&(J(k,1,nodes),nodes#J(k,1,1)), &(weights#weights))) // Kronecker square of non-nested nodes
	}

	w1d = KPNw1d(); n1d = KPNn1d()
	nodes = J(0, dim,.); weights = J(0,1,.); R1d = J(25, 1, 0)
	for (r=25; r; r--) R1d[r] = rows(*n1d[r])

	for(q=max((0,k-dim)); q<k; q++) {
		r = rows(weights)
		bq = (2*mod(k-q, 2)-1) * comb(dim-1,dim+q-k)
		is = SpGrGetSeq(dim, dim+q) // matrix of all rowvectors in N^D_{D+q}
		Rq = R1d[is[,1]]
		for(j=dim; j>1; j--)
			Rq = Rq :* R1d[is[,j]]
		nodes   = nodes   \ J(colsum(Rq), dim, .)
		weights = weights \ J(colsum(Rq), 1  , .)

		// inner loop collecting product rules
		for (j=1; j<=rows(is); j++) {
			midx = is[j,]
			newnw = SpGrKronProd(n1d[midx], w1d[midx])
			nodes  [|r+1,. \ r+Rq[j],.|] = *newnw[1]
			weights[|r+1   \ r+Rq[j]  |] = *newnw[2] :* bq
			r = r + Rq[j]
		}

		// combine identical nodes, summing weights
		if (rows(nodes) > 1) {
			sortvec = order(nodes, 1..dim)
			nodes = nodes[sortvec,]
			weights = weights[sortvec]
			keep = rowmax(nodes[|.,.\rows(nodes)-1,.|] :!= nodes[|2,.\.,.|]) \ 1
			weights = select(quadrunningsum(weights), keep)
			weights = weights - (0 \ weights[|.\rows(weights)-1|])
			nodes = select(nodes, keep)
		}
	}

	// 2. expand rules to other orthants
	for(j=dim; j; j--)
		if (any(keep = nodes[,j])) {
			t = select(nodes, keep)
			t[,j] = -t[,j]
			nodes   = nodes   \ t
			weights = weights \ select(weights, keep)
		}

	return(&nodes, &weights)
}

// SpGrGetSeq(): generate all d-length sequences of positive integers summing to norm
//     Output: one sequence per row
real matrix cmp_model::SpGrGetSeq(real scalar d, real scalar norm) {
	real scalar i; real matrix retval
	if (d==1) return (norm)
	retval = norm-d+1, J(1,d-1,1)
	for (i=norm-d; i; i--)
		retval = retval \ J(comb(norm-i-1,d-2), 1, i), SpGrGetSeq(d-1, norm-i)
	return (retval)
}

// SpGrKronProd(): generate tensor product quadrature rule
// Input:
//     n1d : vector of pointers to 1D nodes
//     n1d : vector of pointers to 1D weights
// Output:
//     out  = pair of pointers to nodes and weights
pointer (real matrix) rowvector cmp_model::SpGrKronProd(pointer colvector n1d, pointer colvector w1d){
  real matrix nodes; real colvector weights; real scalar j
  nodes = *n1d[1]; weights = *w1d[1]
  for(j=2; j<=rows(n1d); j++){
    nodes = J(rows(*n1d[j]),1,nodes), *n1d[j] # J(rows(nodes),1,1)
    weights = *w1d[j] # weights
  }
  return(&nodes, &weights)
}

// build database of KPN nodes
pointer colvector cmp_model::KPNn1d() {
	pointer colvector n1d
	n1d = J(25, 1, NULL)
	n1d[1]= &0
	n1d[2]=
	n1d[3]= &(0 \ 1.bb67ae8584caaX+000)
	n1d[4]= &(0 \ 1.7b70d986e371bX-001 \ 1.bb67ae8584caaX+000 \ 1.0bd651c3c6940X+002)
	n1d[5]=
	n1d[6]=
	n1d[7]=
	n1d[8]= &(0 \ 1.7b70d986e371bX-001 \ 1.bb67ae8584caaX+000 \ 1.6e3e68bdf05c1X+001 \ 1.0bd651c3c6940X+002)
	n1d[9]= &(0 \ 1.7b70d986e371bX-001 \ 1.3afd0b145f6cbX+000 \ 1.bb67ae8584caaX+000 \ 1.4c4c73966ac4cX+001 \ 1.6e3e68bdf05c1X+001 \ 1.0bd651c3c6940X+002 \ 1.4bf8121fd06beX+002 \ 1.9741dafb2e279X+002)
	n1d[10]=
	n1d[11]=
	n1d[12]=
	n1d[13]=
	n1d[14]=
	n1d[15]=&(0 \ 1.7b70d986e371bX-001 \ 1.3afd0b145f6cbX+000 \ 1.bb67ae8584caaX+000 \ 1.4c4c73966ac4cX+001 \ 1.6e3e68bdf05c1X+001 \ 1.9a4860b6119dbX+001 \ 1.0bd651c3c6940X+002 \ 1.4bf8121fd06beX+002 \ 1.9741dafb2e279X+002)
	n1d[16]=&(0 \ 1.fdefac787ea12X-003 \ 1.7b70d986e371bX-001 \ 1.3afd0b145f6cbX+000 \ 1.bb67ae8584caaX+000 \ 1.1de7757332a7cX+001 \ 1.4c4c73966ac4cX+001 \ 1.6e3e68bdf05c1X+001 \ 1.9a4860b6119dbX+001 \ 1.d1521e02e7753X+001 \ 1.0bd651c3c6940X+002 \ 1.4bf8121fd06beX+002 \ 1.9741dafb2e279X+002 \ 1.c7d0989fa502aX+002 \ 1.fec4f713f2469X+002 \ 1.208ac550728f1X+003)
	n1d[17]=&(0 \ 1.fdefac787ea12X-003 \ 1.7b70d986e371bX-001 \ 1.3afd0b145f6cbX+000 \ 1.bb67ae8584caaX+000 \ 1.1de7757332a7cX+001 \ 1.4c4c73966ac4cX+001 \ 1.6e3e68bdf05c1X+001 \ 1.9a4860b6119dbX+001 \ 1.d1521e02e7753X+001 \ 1.0bd651c3c6940X+002 \ 1.4bf8121fd06beX+002 \ 1.6caef1ce9cd82X+002 \ 1.9741dafb2e279X+002 \ 1.c7d0989fa502aX+002 \ 1.fec4f713f2469X+002 \ 1.208ac550728f1X+003)
	n1d[18]=
	n1d[19]=
	n1d[20]=
	n1d[21]=
	n1d[22]=
	n1d[23]=
	n1d[24]=
	n1d[25]=&(0 \ 1.fdefac787ea12X-003 \ 1.7b70d986e371bX-001 \ 1.3afd0b145f6cbX+000 \ 1.bb67ae8584caaX+000 \ 1.1de7757332a7cX+001 \ 1.4c4c73966ac4cX+001 \ 1.6e3e68bdf05c1X+001 \ 1.9a4860b6119dbX+001 \ 1.d1521e02e7753X+001 \ 1.0bd651c3c6940X+002 \ 1.2f21b83cf6e0dX+002 \ 1.4bf8121fd06beX+002 \ 1.6caef1ce9cd82X+002 \ 1.9741dafb2e279X+002 \ 1.c7d0989fa502aX+002 \ 1.fec4f713f2469X+002 \ 1.208ac550728f1X+003)
	return (n1d)
}
// build database of KPN weights
pointer colvector cmp_model::KPNw1d() {
	pointer colvector w1d
	w1d = J(25, 1, NULL)
	w1d[1]= &1
	w1d[2]=
	w1d[3]= &(1.5555555555556X-001 \ 1.5555555555555X-003)
	w1d[4]= &(1.d5c136f97eb9fX-002 \ 1.0d103a2317c43X-003 \ 1.1bc1d1bdbe6a5X-003 \ 1.6cbd25ab17686X-00b)
	w1d[5]=
	w1d[6]=
	w1d[7]=
	w1d[8]= &(1.0410410410414X-002 \ 1.148e5d741b005X-002 \ 1.84826d9d7c2efX-004 \ 1.06060a315d4a6X-007 \ 1.8b650f2e8fcd4X-00e)
	w1d[9]= &(1.11540fa7752daX-002 \ 1.04abb319cb636X-002 \ 1.d1109e29589b7X-007 \ 1.6b3cc5404fbb8X-004 \ 1.01a52daa57d1aX-009 \ 1.ccf2379939783X-008 \ 1.bb13c34bd0925X-00e \ -1.b87f927a33201X-015 \ 1.6b1f4ed2fa996X-01a)
	w1d[10]=
	w1d[11]=
	w1d[12]=
	w1d[13]=
	w1d[14]=
	w1d[15]=&(1.36c01b0b214aaX-002 \ 1.aaa64b1098a9dX-003 \ 1.f4f4791f6a8d9X-005 \ 1.068995aaeb61eX-004 \ 1.284ef86a8d09eX-006 \ -1.9f50fd3c25122X-008 \ 1.7a2086415d886X-009 \ 1.f859f3e93d0b5X-00f \ 1.473669d2633bfX-015 \ 1.da6c037cb5564X-01f)
	w1d[16]=&(1.091d18766b7ceX-002 \ 1.ccd9cf0da1f36X-006 \ 1.98f65ae9b1ec1X-003 \ 1.0bf31bad212fdX-004 \ 1.f99924ddae2a8X-005 \ 1.cd987ab3a0a7eX-00a \ 1.0fd9f560112f9X-006 \ -1.6c723438806c2X-008 \ 1.65ce5537c60f6X-009 \ 1.f8ccbd8413685X-011 \ 1.f2e981ae3e0ebX-00f \ 1.49d4c7adcacdfX-015 \ 1.b3f263f7f563eX-01f \ 1.67fe27d2db829X-026 \ -1.4e2f97e6388d2X-02b \ 1.6bb3d51d2f57fX-032)
	w1d[17]=&(1.1ce5d1fcb8556X-003 \ 1.a97acb4daae07X-004 \ 1.689a86fc7dc03X-003 \ 1.3d3580d146bf1X-004 \ 1.bfeb256ec39b1X-005 \ 1.e1e30ddc332b8X-008 \ 1.79ca558bf4430X-007 \ -1.6b3aad1909c14X-009 \ 1.15e6fa482e21cX-009 \ 1.5d1e05e9c73c4X-00e \ 1.d32bda6e983caX-00f \ 1.72e76f320db55X-015 \ -1.cf60435a516d9X-01b \ 1.ab387b3b8eb18X-01e \ -1.54417e7671e89X-024 \ 1.2bf25fc6b54f0X-02a \ -1.1e40bbd0c6bd1X-032)
	w1d[18]=
	w1d[19]=
	w1d[20]=
	w1d[21]=
	w1d[22]=
	w1d[23]=
	w1d[24]=
	w1d[25]= &(1.0df3f89599c82X-00b \ 1.88b98713549feX-003 \ 1.2f3fc28a6ceecX-003 \ 1.7a536f88daddaX-004 \ 1.72e1ccce26990X-005 \ 1.00cb504b73588X-006 \ 1.9d97350f96961X-009 \ 1.2ef3e06f24d86X-009 \ 1.ad5e22af36681X-00b \ 1.209cbfeab0317X-00c \ 1.2bb830e618c44X-00f \ 1.6efb1b7210daeX-013 \ 1.08f607cf1c672X-016 \ 1.6f8ca9924e87bX-01a \ 1.f9e7977b533b5X-020 \ 1.b3e53215c6f94X-027 \ 1.88b784334d04dX-030 \ 1.3720030162321X-03c)
	return (w1d)
}

// build database of classic Gaussian quadrature node points, which are optimal (fewer) in 1- and 2-dimensional case
pointer colvector cmp_model::GQNw1d() {
	pointer colvector w1d
	w1d = J(25, 1, NULL)
	w1d[1] =&1
	w1d[2] =&0.5
	w1d[3] =&(1.5555555555555X-001 \ 1.5555555555558X-003)
	w1d[4] =&(1.d105eb806161eX-002 \ 1.77d0a3fcf4f09X-005)
	w1d[5] =&(1.1111111111112X-001 \ 1.c6cfbdb1f1fa3X-003 \ 1.70e202bebe3a7X-007)
	w1d[6] =&(1.a2a3ee29aae1dX-002 \ 1.6af858329214cX-004 \ 1.4efde4d84c7a5X-009)
	w1d[7] =&(1.d41d41d41d425X-002 \ 1.ebc5b378f5f4bX-003 \ 1.f7ecba63d3cabX-006 \ 1.1f7366724faa9X-00b)
	w1d[8] =&(1.7df6ecdef47e1X-002 \ 1.e036f41317d11X-004 \ 1.3bba15a77e75dX-007 \ 1.d856f0999f3a6X-00e)
	w1d[9] =&(1.a01a01a01a023X-002 \ 1.f3e9643fc0922X-003 \ 1.98ea4ad2e4eb8X-005 \ 1.6d940d8468e15X-009 \ 1.76e6ab51a9110X-010)
	w1d[10]=&(1.60e9eb9566811X-002 \ 1.15787acb87a16X-003 \ 1.391fc74e7189cX-006 \ 1.8d728ef7a4755X-00b \ 1.214872c35b4dfX-012)
	w1d[11]=&(1.7a463005e918fX-002 \ 1.f01baeaddb005X-003 \ 1.0ee78075fa6f1X-004 \ 1.b86bad4e71d18X-008 \ 1.9a5a915200b18X-00d \ 1.b409da81c1130X-015)
	w1d[12]=&(1.496261e3f1ff8X-002 \ 1.2cfd0f478e08eX-003 \ 1.dd0c3d967e08aX-006 \ 1.20cd2ffcb8399X-009 \ 1.95c5c15728197X-00f \ 1.421b5e3a9a562X-017)
	w1d[13]=&(1.5d2d18a2fe8d9X-002 \ 1.e7292f5e3ed36X-003 \ 1.4446aac477328X-004 \ 1.81b29e36e45f4X-007 \ 1.6529fec49affeX-00b \ 1.82c4b5d22b3aeX-011 \ 1.d3be6e1811e79X-01a)
	w1d[14]=&(1.35e5da033242eX-002 \ 1.3b900bcbdb3d0X-003 \ 1.3c9f272c6261cX-005 \ 1.2240eeb891669X-008 \ 1.a4247a9ba0ea2X-00d \ 1.6526f764ca204X-013 \ 1.4e899939f3b85X-01c)
	w1d[15]=&(1.45e5d2ba42e9fX-002 \ 1.dc1530e4db087X-003 \ 1.6e415aaec396bX-004 \ 1.1c855660dc3c2X-006 \ 1.9adf94e023fc0X-00a \ 1.d94c2be3fe52aX-00f \ 1.40cd8aa6ea5efX-015 \ 1.d8389345109e0X-01f)
	w1d[16]=&(1.257237eb1f12bX-002 \ 1.4446e8a55664eX-003 \ 1.835b501eb7e7eX-005 \ 1.dc3efb56d343fX-008 \ 1.13c480766c610X-00b \ 1.00b123449bbfeX-010 \ 1.19350d272303bX-017 \ 1.495f790d999f3X-021)
	w1d[17]=&(1.32ba2fbe5d1aeX-002 \ 1.d04b65a55e84cX-003 \ 1.8ef9fba90df96X-004 \ 1.7a4075398fbccX-006 \ 1.76ba4fad552feX-009 \ 1.615a26057b77bX-00d \ 1.0d494ed8ab7b5X-012 \ 1.e269dadb1cac0X-01a \ 1.c6a33273c5c56X-024)
	w1d[18]=&(1.17547cfef2f46X-002 \ 1.491560695d193X-003 \ 1.c1b695253803aX-005 \ 1.589af1e7fc724X-007 \ 1.174f796af7002X-00a \ 1.b2856c34a4c40X-00f \ 1.12388bd5565c6X-014 \ 1.95d273b675690X-01c \ 1.36ca30ad426f8X-026)
	w1d[19]=&(1.2295709965ab9X-002 \ 1.c47d16a1bd936X-003 \ 1.a85c4efbf3d6bX-004 \ 1.d5acd11d5e8b3X-006 \ 1.2762dcb9ca37eX-008 \ 1.8ce362e7050adX-00c \ 1.018cab7a125b2X-010 \ 1.0fe5225d2e717X-016 \ 1.4f73f6cb14e42X-01e \ 1.a53ef230db9faX-029)
	w1d[20]=&(1.0b0d563a28710X-002 \ 1.4b3dfdef813beX-003 \ 1.f7dc361055131X-005 \ 1.caae5f0667284X-007 \ 1.dfc024629beb9X-00a \ 1.0e2b151900242X-00d \ 1.276bdd4d66a02X-012 \ 1.072c77c840896X-018 \ 1.10e7d83542f2aX-020 \ 1.1b3b45ae1f15eX-02b)
	w1d[21]=&(1.14bf15e76d04cX-002 \ 1.b900e215176feX-003 \ 1.bbf98c9e7772bX-004 \ 1.16240901614afX-005 \ 1.a608303b60f73X-008 \ 1.7338910139939X-00b \ 1.61ef604c7b84aX-00f \ 1.48edbe3f7951cX-014 \ 1.f2718419f6999X-01b \ 1.b5a35749021e1X-023 \ 1.7a1ee275317e1X-02e)
	w1d[22]=&(1.003fdb7d7f495X-002 \ 1.4b9586d9d3975X-003 \ 1.133c707ffb8c5X-004 \ 1.1fda085c6c27eX-006 \ 1.702661aee92fbX-009 \ 1.1306235b39102X-00c \ 1.bfd11211e61bbX-011 \ 1.647771ec3c191X-016 \ 1.ceb0afd401710X-01d \ 1.5a4342ed6e5acX-025 \ 1.f57170af1a7b8X-031)
	w1d[23]=&(1.08b6c709e2b6cX-002 \ 1.adff55d2841ebX-003 \ 1.cb0d75907c529X-004 \ 1.3e66645c9c42dX-005 \ 1.19238f0cff5bbX-007 \ 1.32163b3f699f4X-00a \ 1.87c846af16c00X-00e \ 1.127946272007dX-012 \ 1.78e2d51532124X-018 \ 1.a5b629c720c4fX-01f \ 1.0ea10bc809809X-027 \ 1.4a730b4f958dcX-033)
	w1d[24]=&(1.ed4d4fa6da3d6X-003 \ 1.4aab48fb22861X-003 \ 1.27323497f4f17X-004 \ 1.5a224f9483d2bX-006 \ 1.049c6d4cc0b24X-008 \ 1.e74afb2f16fd5X-00c \ 1.0d3b7ff451495X-00f \ 1.46dcf0c7d6da6X-014 \ 1.858c04de91929X-01a \ 1.79f03beeecf96X-021 \ 1.a244d6f97e2fcX-02a \ 1.b10bd7013194eX-036)
	w1d[25]=&(1.fc403679615eeX-003 \ 1.a388ef3dc31c8X-003 \ 1.d68d614d1d96cX-004 \ 1.635e64257cda4X-005 \ 1.63c111f64510bX-007 \ 1.cccf5801e9c6eX-00a \ 1.74cde1a66238cX-00d \ 1.661974c8c9745X-011 \ 1.7b0c183df3fb2X-016 \ 1.8a50622866c6cX-01c \ 1.4d791bd04ec6dX-023 \ 1.3fd9ac565f9a5X-02c \ 1.1a3e0c7f3a049X-038)
	return (w1d)
}

pointer colvector cmp_model::GQNn1d() {
	pointer colvector n1d
	n1d = J(25, 1, NULL)
	n1d[1] =&(0.0000000000000X-3ff)
	n1d[2] =&(1.0000000000001X+000)
	n1d[3] =&(0.0000000000000X-3ff \ 1.bb67ae8584caaX+000)
	n1d[4] =&(1.7be2ad58cb0ffX-001 \ 1.2ace15c98aa9fX+001)
	n1d[5] =&(0.0000000000000X-3ff \ 1.5b0a513c97441X+000 \ 1.6db131839e414X+001)
	n1d[6] =&(1.3bc0f75835b11X-001 \ 1.e3a107c35822eX+000 \ 1.a98144804badfX+001)
	n1d[7] =&(0.0000000000000X-3ff \ 1.27871ca8bbf03X+000 \ 1.2ef1f8ed4d738X+001 \ 1.e00e689ea0325X+001)
	n1d[8] =&(1.140244df60425X-001 \ 1.a2f2e9768a3f2X+000 \ 1.66b7db50ddbecX+001 \ 1.094042d748ee4X+002)
	n1d[9] =&(0.0000000000000X-3ff \ 1.05f4154b6bccfX+000 \ 1.09d6279197adaX+001 \ 1.9a4b7f60758f2X+001 \ 1.20d0d4069d86cX+002)
	n1d[10]=&(1.f092fc71c448cX-002 \ 1.774b0fb0b3e2fX+000 \ 1.3dfe63a13936dX+001 \ 1.ca793120f33dbX+001 \ 1.37017060f4281X+002)
	n1d[11]=&(0.0000000000000X-3ff \ 1.db94b79c0a3abX-001 \ 1.e043d4c1b73c5X+000 \ 1.6ebc5b10fd018X+001 \ 1.f7d44eb09d822X+001 \ 1.4c0836499312fX+002)
	n1d[12]=&(1.c711949e60909X-002 \ 1.5722d43422c04X+000 \ 1.21362191c2f29X+001 \ 1.9ca2860f2e400X+001 \ 1.1165983dc4e75X+002 \ 1.600ec605ccc6dX+002)
	n1d[13]=&(0.0000000000000X-3ff \ 1.b69eb1cfa3c79X-001 \ 1.b9b504d83fb18X+000 \ 1.4f72c4e06c593X+001 \ 1.c81ef20936599X+001 \ 1.25d978e145995X+002 \ 1.7335f0b515220X+002)
	n1d[14]=&(1.a67e1cee3a09cX-002 \ 1.3e20dd06e9148X+000 \ 1.0b4ee170c819dX+001 \ 1.7b44c85ba11f6X+001 \ 1.f186be95f3409X+001 \ 1.396767eb4c3daX+002 \ 1.85981e3653268X+002)
	n1d[15]=&(0.0000000000000X-3ff \ 1.992771fb7948dX-001 \ 1.9b5159e0a1de5X+000 \ 1.375a1706cbe4dX+001 \ 1.a500a723520f6X+001 \ 1.0c8eaac9c7e65X+002 \ 1.4c2a7e4f7553aX+002 \ 1.974aec15fe3bcX+002)
	n1d[16]=&(1.8c0af8ced8268X-002 \ 1.29f0b43504447X+000 \ 1.f3b4fbe349534X+000 \ 1.614fb5b04289bX+001 \ 1.cce96d4a6c431X+001 \ 1.1f8c9465ada5bX+002 \ 1.5e38f22ad8822X+002 \ 1.a8604ef376e7bX+002)
	n1d[17]=&(0.0000000000000X-3ff \ 1.80f1836b86908X-001 \ 1.8287b663c367eX+000 \ 1.23f871ecb6a1dX+001 \ 1.89722f93492fdX+001 \ 1.f3355a79cee0fX+001 \ 1.31d3762917467X+002 \ 1.6fa53be2c1437X+002 \ 1.b8e761ce5f5f2X+002)
	n1d[18]=&(1.7602fbbba22caX-002 \ 1.193072dc46a64X+000 \ 1.d6fbd122b7929X+000 \ 1.4c44473fdd49dX+001 \ 1.aff75de6e4410X+001 \ 1.0c088602756abX+002 \ 1.4375ed47e5578X+002 \ 1.807ee8b4fde66X+002 \ 1.c8ecfdf981a52X+002)
	n1d[19]=&(0.0000000000000X-3ff \ 1.6c96693043f6bX-001 \ 1.6dcadca1c617aX+000 \ 1.13e783b5259f8X+001 \ 1.72f3581f62135X+001 \ 1.d50b99f71c617X+001 \ 1.1dd0db6c15d13X+002 \ 1.5483ab02758aeX+002 \ 1.90d3356de8734X+002 \ 1.d87c2cbb1629dX+002)
	n1d[20]=&(1.634a926e31cc3X-002 \ 1.0afe77649f855X+000 \ 1.bec88746540b3X+000 \ 1.3ab57d3cecdfdX+001 \ 1.9831a333c7333X+001 \ 1.f8d3ec11c84feX+001 \ 1.2f03616d7eb60X+002 \ 1.650a0e7d0f317X+002 \ 1.a0ad8256821f3X+002 \ 1.e79e7dc649aafX+002)
	n1d[21]=&(0.0000000000000X-3ff \ 1.5b28ce1473d5dX-001 \ 1.5c199cece9271X+000 \ 1.0648fd5ba8a05X+001 \ 1.60136e491d115X+001 \ 1.bc23efb4dcdfbX+001 \ 1.0db7cfd1dc8e7X+002 \ 1.3fad7d40e26dcX+002 \ 1.7514984550ddcX+002 \ 1.b017ab96e92dbX+002 \ 1.f65c4a1312ec3X+002)
	n1d[22]=&(1.5320aba86f5ecX-002 \ 1.fd85edd3b9dcdX-001 \ 1.aa0415e079011X+000 \ 1.2bbecaa391824X+001 \ 1.8425d25d6503fX+001 \ 1.dee95a373fc04X+001 \ 1.1e7cb6f266eabX+002 \ 1.4fdab7954e4f1X+002 \ 1.84ad428f4eb28X+002 \ 1.bf1a4da2ea0cbX+002 \ 1.025e7421097e3X+003)
	n1d[23]=&(0.0000000000000X-3ff \ 1.4c046939a8fceX-001 \ 1.4cc4b44834a04X+000 \ 1.f5136b2368a4eX+000 \ 1.4fe9d63b33b83X+001 \ 1.a70b8d2ab336fX+001 \ 1.004e3d26ab08fX+002 \ 1.2ec42f6c9ccb9X+002 \ 1.5f9513ea54adcX+002 \ 1.93dcc5a2cb26bX+002 \ 1.cdbcfaed54d66X+002 \ 1.09636b181b8c7X+003)
	n1d[24]=&(1.44fcaaa701e6fX-002 \ 1.e826eb1490d0aX-001 \ 1.97ee555ce0a70X+000 \ 1.1ec7a68b60b2cX+001 \ 1.72e8c5ada9cd9X+001 \ 1.c8df0b468b344X+001 \ 1.10aa1ffefe67aX+002 \ 1.3e983ad98d6f2X+002 \ 1.6ee554687c2dbX+002 \ 1.a2aacda3fd64aX+002 \ 1.dc06669272595X+002 \ 1.103fed2a77985X+003)
	n1d[25]=&(0.0000000000000X-3ff \ 1.3eb3603832154X-001 \ 1.3f4fd66f2eea1X+000 \ 1.e086e5b79be9dX+000 \ 1.41da42df91e4bX+001 \ 1.94d5d55dc73a6X+001 \ 1.e9b71c20e3ca5X+001 \ 1.20924ecfc66b2X+002 \ 1.4e019b6f3f9fcX+002 \ 1.7dd32f47e7204X+002 \ 1.b11e255e18de2X+002 \ 1.e9fc869ed6452X+002 \ 1.16f68f680d2d2X+003)
	return (n1d)
}

// vectorize binormal(). Accepts general covariance matrix, not just rho parameter. Optionally computes scores.
real colvector cmp_model::vecbinormal(real matrix X, real matrix Sig, real colvector one2N, real scalar todo, real matrix dPhi_dX, real matrix dPhi_dSig) {
	real colvector Phi, Xhat, X_2
	real matrix dPhi_dSigDiag, phi, X_
	real scalar rho
	real rowvector SigDiag, sqrtSigDiag

	Xhat = X :/ (sqrtSigDiag = sqrt(SigDiag = diagonal(Sig)'))
	rho = Sig[1,2]/(sqrtSigDiag[1]*sqrtSigDiag[2])
	Phi = binormalGenz(editmissing(Xhat[one2N,1], 1e6), editmissing(Xhat[one2N,2], 1e6), rho)

	if (todo) {
		phi = editmissing(normalden(Xhat), 0)
		X_ = Xhat * ((1,-rho \ -rho,1) / sqrt(1 - rho * rho)) // each X_ with the other partialled out, then renormalized to s.d. 1
		dPhi_dSig = phi[one2N,1] :* editmissing(normalden(X_2=X_[one2N,2]),0) / sqrt(det(Sig))
		dPhi_dX = phi :* (editmissing(normal(X_2), 1), editmissing(normal(X_[one2N,1]), 1)) :/ sqrtSigDiag
		dPhi_dSigDiag = (editmissing(X, 0):*dPhi_dX :+ (Sig[1,2]*dPhi_dSig)) :/ (-2 * SigDiag)
		dPhi_dSig = dPhi_dSigDiag[one2N,1], dPhi_dSig, dPhi_dSigDiag[one2N,2]
	}
	return (Phi)
}

// compute binormal(E1,E2,rho)-binormal(E1,F2,rho) so as to maximize precision. If midpoint between E2, F2 is >0, negate E2, F2, rho in order to take difference of smaller numbers
// infsign flag indicate whether to interpret . in E1 as + or - infinity. 1=+, 0=-
real colvector cmp_model::vecbinormal2(real colvector E1, real colvector E2, real colvector F2, real matrix Sig, real scalar infsign, real scalar flip, real colvector one2N,
							                         real scalar todo, real matrix dPhi_dE1, real matrix dPhi_dE2, real matrix dPhi_dF2, real matrix dPhi_dSig) {
	real colvector Phi, E1hat, E2hat, F2hat, phiE1, phiE2, phiF2, E1E2hat1, E1E2hat2, E1F2hat1, E1F2hat2
	real matrix dPhi_dSigDiagE, dPhi_dSigDiagF, dPhi_dXE, dPhi_dXF, dPhi_dSigE, dPhi_dSigF
	real scalar rho, i1, i2
	real rowvector SigDiag, sqrtSigDiag, t

	if (flip) {
		i1 = 2; i2 = 1
	} else {
		i1 = 1; i2 = 2
	}
	sqrtSigDiag = sqrt(SigDiag = diagonal(Sig)'[(i1,i2)])
	E1hat = E1 / sqrtSigDiag[1]
	E2hat = E2 / sqrtSigDiag[2]
	F2hat = F2 / sqrtSigDiag[2]
	rho = Sig[1,2]/(sqrtSigDiag[1]*sqrtSigDiag[2])

	if (infsign)
		Phi = binormal2(editmissing(E1hat, 1e6),
						editmissing(E2hat, 1e6), editmissing(F2hat, -1e6), rho)
	else
		Phi = binormal2(editmissing(E1hat,-1e6),
						editmissing(E2hat, 1e6), editmissing(F2hat, -1e6), rho)

	if (todo) {
		phiE1 = editmissing(normalden(E1hat), 0)
		phiE2 = editmissing(normalden(E2hat), 0)
		phiF2 = editmissing(normalden(F2hat), 0)
    t = sqrt(1-rho*rho); E1hat = E1hat / t; E2hat = E2hat / t; F2hat = F2hat / t
		E1E2hat1 = E1hat - rho * E2hat // each with the other partialled out, then renormalized to s.d. 1
		E1E2hat2 = E2hat - rho * E1hat
		E1F2hat1 = E1hat - rho * F2hat
		E1F2hat2 = F2hat - rho * E1hat
		dPhi_dSigE = phiE1 :* editmissing(normalden(E1E2hat2),0) / (t=sqrt(det(Sig)))
		dPhi_dSigF = phiE1 :* editmissing(normalden(E1F2hat2),0) /  t
		dPhi_dXE = (phiE1,phiE2) :* (editmissing(normal(E1E2hat2), 1), editmissing(normal(E1E2hat1), infsign)) :/ sqrtSigDiag
		dPhi_dXF = (phiE1,phiF2) :* (editmissing(normal(E1F2hat2), 0), editmissing(normal(E1F2hat1), infsign)) :/ sqrtSigDiag
		dPhi_dSigDiagE = (editmissing((E1,E2), 0) :* dPhi_dXE :+ (Sig[1,2] * dPhi_dSigE)) :/ (t = -SigDiag-SigDiag)
		dPhi_dSigDiagF = (editmissing((E1,F2), 0) :* dPhi_dXF :+ (Sig[1,2] * dPhi_dSigF)) :/  t
		dPhi_dSig = (dPhi_dSigDiagE[one2N,i1] - dPhi_dSigDiagF[one2N,i1]), (dPhi_dSigE - dPhi_dSigF), (dPhi_dSigDiagE[one2N,i2] - dPhi_dSigDiagF[one2N,i2])
		dPhi_dE1 = dPhi_dXE[one2N,1] - dPhi_dXF[one2N,1]
		dPhi_dE2 = dPhi_dXE[one2N,2]
		dPhi_dF2 =                   - dPhi_dXF[one2N,2]
	}
	return (Phi)
}

// neg_half_E_Dinvsym_E() -- compute -0.5 * inner product of given errors weighting by derivative of inverse of a symmetric matrix
// Passed +/- E times the inverse of X. Returns a matrix with one column for each of the N(N+1)/2 independent entries in X.
real matrix cmp_model::neg_half_E_Dinvsym_E(real matrix E_invX, real colvector one2N, real matrix EDE) {
	real colvector E_invX_j; real scalar N, j, l
	if (N = cols(E_invX)) {
		l = cols(EDE)
		E_invX_j = E_invX[one2N,N]
		EDE[,l--] = E_invX_j :* E_invX_j * .5
		for (j=N-1; j; j--) {
			E_invX_j = E_invX[one2N,j]
			EDE[|.,l-N+j+1 \ .,l|] = E_invX[|.,j+1 \ .,N|] :* E_invX_j // effectively double off-diagonal entries since symmetric
			l = l - N + j
			EDE[one2N,l--] = E_invX_j :* E_invX_j * .5
		}
	}
	return (EDE)
}

// Compute product of derivative of Phi w.r.t. partialled-out errors (provided) and derivative of partialled-out errors w.r.t.
// original covariance matrix. Used as part of an application of the chain rule to transform the initial scores for Phi
// w.r.t. the partialled-out errors and covariance matrix into scores w.r.t. the un-partialled ones.
// Returns a score matrix with one row for each observation and one column for each element of the lower triangle of
// Var[in | out], ordered by the lists in parameters "in" and "out". E.g. if in=(1,3) and out=(2), then the column
// order corresponds to (1,1),(1,3),(1,2),(3,3),(3,2),(2,2)
real matrix cmp_model::dPhi_dpE_dSig(real matrix E_out, real colvector one2N, real matrix beta, real matrix invSig_out, real matrix Sig_out_in,
					                           real matrix dPhi_dpE, real scalar lin, real scalar lout, real matrix scores, real matrix J_d_uncens_d_cens_0) {
	real matrix neg_dbeta_dSig; real rowvector beta_j; real colvector invSig_out_j; real scalar i, j, l

	l = lin + lout
	for(l=j=1; j<=lin; j++) {
		// scores w.r.t. sig_ij where both i,j are in are 0, so skip those columns in score matrix
		l = l + lin - j + 1
		// scores w.r.t. sig_ij where i out and j in
		for(i=1; i<=lout; i++) {
			(neg_dbeta_dSig = J_d_uncens_d_cens_0)[,j] = -invSig_out[,i]
			scores[one2N,l++] = quadrowsum(dPhi_dpE :* (E_out * neg_dbeta_dSig))
		}
	}
	// scores w.r.t. sig_ij where both i,j out
	for(j=1; j<=lout; j++) {
		beta_j = beta[j,]; invSig_out_j = invSig_out[,j]
		neg_dbeta_dSig = invSig_out_j * quadcross(invSig_out_j, Sig_out_in)
		scores[one2N,l++] = quadrowsum(dPhi_dpE :* (E_out * neg_dbeta_dSig))
		for(i=j+1; i<=lout; i++) {
			neg_dbeta_dSig = invSig_out[,i] * beta_j + invSig_out_j * beta[i,]
			scores[one2N,l++] = quadrowsum(dPhi_dpE :* (E_out * neg_dbeta_dSig))
		}
	}
	return (scores)
}

// (log) likelihood and scores for cumulative multivariate normal for a vector of observations of upper bounds and optional lower bounds
// i.e., computes multivariate normal cdf over L_1<=x_1<=U_1, L_2<=x_2<=U_2, ..., where some L_i's can be negative infinity
// Argument -bounded- indicates which dimensions have lower bounds as well as upper bounds.
// If argument log>0, returns Phi, not log Phi
// returns scores if requested in dPhi_dE, dPhi_dF, dPhi_dSig. dPhi_dF must already be allocated
real colvector cmp_model::vecmultinormal(real matrix E, real matrix F, real matrix Sig, real scalar d, real rowvector bounded, real colvector one2N, real scalar todo,
						                             real matrix dPhi_dE, real matrix dPhi_dF, real matrix dPhi_dSig, transmorphic ghk2DrawSet, real scalar ghkAnti, real scalar GHKStart, real scalar N_perm) {
	real matrix dPhi_dE1, dPhi_dE2, dPhi_dF1, dPhi_dF2, _dPhi_dF2, _dPhi_dE1, _dPhi_dF1, _dPhi_dSig, dM
	pragma unset dPhi_dE1; pragma unset dPhi_dE2; pragma unset dPhi_dF1; pragma unset dPhi_dF2; pragma unset _dPhi_dF2; pragma unset _dPhi_dE1; pragma unset _dPhi_dF1; pragma unset _dPhi_dSig; pragma unset dM
	real colvector Phi

	if (d == 1) {
		real scalar sqrtSig
		sqrtSig = sqrt(Sig[1,1])
		if (cols(bounded)) {
			Phi = normal2(Mdivs(*getcol(F,1), sqrtSig), Mdivs(*getcol(E,1), sqrtSig))
			if (todo) {  // Compute partial deriv w.r.t. sig^2 in 1/sqrt(sig^2) term in normal dist
				if (N_perm == 1) {
					dPhi_dE =  editmissing(normalden(E, 0, sqrtSig), 0) :/ Phi  // only in Stata 13 can the middle 0's be dropped; but this is still mostly running as Stata 11
					dPhi_dF = -editmissing(normalden(F, 0, sqrtSig), 0) :/ Phi
				}
				dPhi_dSig = (rowsum(dPhi_dE :* E) + rowsum(dPhi_dF :* F)) / (-2 * Sig)
			}
		} else {
			Phi = normal(Mdivs(E, sqrtSig))
			if (todo) {
				if (N_perm == 1) dPhi_dE = editmissing(normalden(E, 0, sqrtSig), 0) :/ Phi
				dPhi_dSig = dPhi_dE :* E / (-2 * Sig)
			}
		}
		if (N_perm==1)
			return (ln(Phi))
		return (Phi)
	}

	if (d == 2) {
		if (cols(bounded)) {
			if (bounded[1]==1) {
				pointer (real colvector) scalar pE1, pF1
				pE1 = &(E[one2N,1]); pF1 = &(F[one2N,1])
				Phi = vecbinormal2(E[one2N,2], *pE1, *pF1, Sig, 1, 1, one2N, todo, dPhi_dE2, dPhi_dE1, dPhi_dF1, dPhi_dSig)
				if (bounded==1) {
					if (todo) {
						dPhi_dE = dPhi_dE1, dPhi_dE2
						dPhi_dF = dPhi_dF1, J(rows(E), 1, 0)
					}
				} else {  // rectangular region integration
					Phi = Phi - vecbinormal2(F[one2N,2], *pE1, *pF1, Sig, 0, 1, one2N, todo, _dPhi_dF2, _dPhi_dE1, _dPhi_dF1, _dPhi_dSig)
					if (todo) {
						dPhi_dE   = dPhi_dE1 -_dPhi_dE1, dPhi_dE2
						dPhi_dF   = dPhi_dF1 -_dPhi_dF1,         -_dPhi_dF2
						dPhi_dSig = dPhi_dSig-_dPhi_dSig
					}
				}
			} else {
				Phi = vecbinormal2(E[one2N,1], E[one2N,2], F[one2N,2], Sig, 1, 0, one2N, todo, dPhi_dE1, dPhi_dE2, dPhi_dF2, dPhi_dSig)
				if (todo) {
					dPhi_dE =         dPhi_dE1, dPhi_dE2
					dPhi_dF = J(rows(E), 1, 0), dPhi_dF2
				}
			}
		} else
			Phi = vecbinormal(E, Sig, one2N, todo, dPhi_dE, dPhi_dSig)
	} else if (cols(bounded))
    if (ghk2DrawSet != .)
      if (todo)
        Phi = _ghk2_2d(ghk2DrawSet, F, E, Sig, ghkAnti, GHKStart, dPhi_dF, dPhi_dE, dPhi_dSig)
      else
        Phi = _ghk2_2 (ghk2DrawSet, F, E, Sig, ghkAnti, GHKStart)
    else {
      Phi = _mvnormalcv(F, E, J(1,cols(E),0), vech(Sig)')
      if (todo)
        _mvnormalcvderiv(F, E, J(1,cols(E),0), vech(Sig)', dPhi_dF, dPhi_dE, dM, dPhi_dSig)
    }
  else if (ghk2DrawSet != .)
    if (todo)
      Phi = _ghk2_d(ghk2DrawSet, E, Sig, ghkAnti, GHKStart, dPhi_dE, dPhi_dSig)
    else
      Phi = _ghk2  (ghk2DrawSet, E, Sig, ghkAnti, GHKStart)
    else {
      Phi = _mvnormalcv(J(1, cols(E), mindouble()), E, J(1,cols(E),0), vech(Sig)')
      if (todo)
        _mvnormalcvderiv(J(1, cols(E), invnormal(Phi*1e-20)), E, J(1,cols(E),0), vech(Sig)', dPhi_dF, dPhi_dE, dM, dPhi_dSig)
    }

	if (N_perm==1) {
		if (todo) {
			dPhi_dE = dPhi_dE :/ Phi
			dPhi_dSig = dPhi_dSig :/ Phi
			if (cols(bounded)) dPhi_dF = dPhi_dF :/ Phi
		}
		return(ln(Phi))
	}
	return (Phi)
}

real colvector _mvnormalcv(a,b,c,d) return (mvnormalcv(a,b,c,d))
void _mvnormalcvderiv(a,b,c,d,e,f,g,h) return (mvnormalcvderiv(a,b,c,d,e,f,g,h))

// compute the log likelihood associated with a given error data matrix, for "continuous" variables
// Sig is the assumed covariance for the full error set and inds marks the observed variables assumed to have a joint normal distribution,
// i.e., the ones not censored
// dphi_dE should already be allocated
real colvector cmp_model::lnLContinuous(pointer(struct subview scalar) scalar v, real scalar todo) {
	real matrix C, t, phi, invSig
	C = luinv(cholesky(v->Omega[v->uncens, v->uncens])) // if uncens were before cens, then this would just be the upper left of cholesky(Omega)
	phi = quadrowsum_lnnormalden(v->EUncens * C', quadsum(ln(diagonal(C)), 1))
	if (todo) {
		v->dphi_dE[., v->uncens] = t = v->EUncens * -(invSig = quadcross(C,C))
		v->dphi_dSig[., v->SigIndsUncens] = neg_half_E_Dinvsym_E(t, v->one2N, v->EDE) :- colshape(invSig, 1) ' v->halfDmatrix
	}
	return (phi)
}

// log likelihood and scores for likelihood over total range of truncation--denominator of L
real colvector cmp_model::lnLTrunc(pointer(struct subview scalar) scalar v, real scalar todo) {
	real matrix dPhi_dEt, dPhi_dFt, dPhi_dSigt; real colvector Phi
	pragma unset dPhi_dEt; pragma unset dPhi_dFt; pragma unset dPhi_dSigt

	Phi = vecmultinormal(*v->pEt, *v->pFt, v->Omega[v->trunc,v->trunc], v->d_trunc, v->one2d_trunc, v->one2N, todo,
							         dPhi_dEt, dPhi_dFt, dPhi_dSigt, ghk2DrawSet, ghkAnti, v->GHKStartTrunc, 1)

	if (todo) {
		v->dPhi_dEt[v->one2N,v->trunc] = dPhi_dEt + dPhi_dFt
		v->dPhi_dSigt[v->one2N, v->SigIndsTrunc] = dPhi_dSigt
	}
	return (Phi)
}

// log likelihood and scores for cumulative normal
// returns scores in v->dPhi_dE.M, v->dPhi_dSig.M if requested
real colvector cmp_model::lnLCensored(pointer(struct subview scalar) scalar v, real scalar todo) {
	real matrix t, pSig, roprobit_pSig, fracprobit_pSig, beta, invSig_uncens, Sig_uncens_cens, S_dPhi_dpE, S_dPhi_dpF, S_dPhi_dpSig, SS_dPhi_dpE, SS_dPhi_dpF, SS_dPhi_dpSig, dPhi_dpE, dPhi_dpF, dPhi_dpSig
	real scalar ThisNumCuts, d_cens, d_two_cens, N_perm, ThisPerm, ThisFracComb
	real colvector i, j, S_Phi, SS_Phi, Phi
	real rowvector uncens, cens, oprobit
	pointer (real matrix) pE, roprobit_pE, fracprobit_pE, pF, roprobit_pQE, pdPhi_dpF
	pragma unset dPhi_dpE; pragma unset dPhi_dpF; pragma unset dPhi_dpSig

	uncens=v->uncens; oprobit=v->oprobit; cens=v->cens; d_cens=v->d_cens; d_two_cens=v->d_two_cens; N_perm=v->N_perm; ThisNumCuts=v->NumCuts
	pdPhi_dpF = NumRoprobitGroups? &dPhi_dpF : &(v->dPhi_dpF)

	if (v->d_uncens) {  // Partial continuous variables out of the censored ones
		beta = (invSig_uncens = cholinv(v->Omega[uncens,uncens])) * (Sig_uncens_cens = v->Omega[uncens, cens])

		t = v->EUncens * beta
		roprobit_pE = fracprobit_pE = pE = &(*v->pECens - t)  // partial out errors from upper bounds
		pF = d_two_cens? &(*v->pF - t) : &J(0,0,0)  // partial out errors from lower bounds
		roprobit_pSig = fracprobit_pSig = pSig = v->Omega[cens, cens] - quadcross(Sig_uncens_cens, beta) // corresponding covariance
	} else {
		roprobit_pE = fracprobit_pE = pE = v->pECens
		pF = d_two_cens? v->pF : &J(0,0,0)
		roprobit_pSig = fracprobit_pSig = pSig = v->Omega[cens,cens]
	}

	for (ThisFracComb = v->NFracCombs; ThisFracComb; ThisFracComb--) {
		if (ThisFracComb < v->NFracCombs) {
			roprobit_pE   = fracprobit_pE   = &(*pE :* diagonal(v->frac_QE[ThisFracComb].M)')
			roprobit_pSig = fracprobit_pSig = cross(v->frac_QE[ThisFracComb].M, pSig) * v->frac_QE[ThisFracComb].M
		}

		for (ThisPerm = N_perm; ThisPerm; ThisPerm--) {
			if (NumRoprobitGroups) {
				roprobit_pQE = v->roprobit_QE[ThisPerm]
				roprobit_pE = &(*fracprobit_pE * *roprobit_pQE)
				roprobit_pSig = cross(*roprobit_pQE, fracprobit_pSig) * *roprobit_pQE
			}

			Phi = vecmultinormal(*roprobit_pE, *pF, roprobit_pSig, v->dCensNonrobase, v->two_cens, v->one2N, todo, dPhi_dpE, v->dPhi_dpF, dPhi_dpSig,
			                     ghk2DrawSet, ghkAnti, v->GHKStart, N_perm)

      if (todo & NumRoprobitGroups) {
				dPhi_dpE   = dPhi_dpE   * *roprobit_pQE'
				dPhi_dpSig = dPhi_dpSig * *v->roprobit_Q_Sig[ThisPerm]
				if (d_two_cens)
					(dPhi_dpF = J(v->N, d_cens, 0))[v->one2N,v->cens_nonrobase] = v->dPhi_dpF
			}

			if (N_perm > 1)
				if (ThisPerm == N_perm) {
					S_Phi = Phi
					if (todo) {
						S_dPhi_dpE   = dPhi_dpE
						S_dPhi_dpSig = dPhi_dpSig
						if (d_two_cens)
							S_dPhi_dpF   = dPhi_dpF
					}
				} else {
					S_Phi = S_Phi + Phi
					if (todo) {
						S_dPhi_dpE   = S_dPhi_dpE   + dPhi_dpE
						S_dPhi_dpSig = S_dPhi_dpSig + dPhi_dpSig
						if (d_two_cens)
							S_dPhi_dpF   = S_dPhi_dpF   + dPhi_dpF
					}
				}
		}

		if (N_perm > 1) {
			Phi = ln(S_Phi)
			if (todo) {
				dPhi_dpE   = S_dPhi_dpE   :/ S_Phi
				dPhi_dpSig = S_dPhi_dpSig :/ S_Phi
				if (d_two_cens)
					dPhi_dpF   = S_dPhi_dpF :/ S_Phi
			}
		}

		if (v->d_frac)
			if (ThisFracComb == v->NFracCombs) {
				SS_Phi = Phi :* v->yProd[ThisFracComb].M
				if (todo) {
					SS_dPhi_dpE   = dPhi_dpE   :* v->yProd[ThisFracComb].M
					SS_dPhi_dpSig = dPhi_dpSig :* v->yProd[ThisFracComb].M
					if (d_two_cens)
						SS_dPhi_dpF = *pdPhi_dpF :* v->yProd[ThisFracComb].M
				}
			} else if (ThisFracComb == 1) {
				Phi = SS_Phi + Phi :* v->yProd[ThisFracComb].M
				if (todo) {
					dPhi_dpE      = SS_dPhi_dpE   + (dPhi_dpE   :* v->yProd[ThisFracComb].M) * v->frac_QE  [ThisFracComb].M
					dPhi_dpSig    = SS_dPhi_dpSig + (dPhi_dpSig :* v->yProd[ThisFracComb].M) * v->frac_QSig[ThisFracComb].M
					if (d_two_cens)
						pdPhi_dpF = &(SS_dPhi_dpF   + (*pdPhi_dpF   :* v->yProd[ThisFracComb].M) * v->frac_QE  [ThisFracComb].M)
				}
			} else {
				SS_Phi = SS_Phi + Phi :* v->yProd[ThisFracComb].M
				if (todo) {
					SS_dPhi_dpE   = SS_dPhi_dpE   + (dPhi_dpE   :* v->yProd[ThisFracComb].M) * v->frac_QE  [ThisFracComb].M
					SS_dPhi_dpSig = SS_dPhi_dpSig + (dPhi_dpSig :* v->yProd[ThisFracComb].M) * v->frac_QSig[ThisFracComb].M
					if (d_two_cens)
						SS_dPhi_dpF = SS_dPhi_dpF + (*pdPhi_dpF   :* v->yProd[ThisFracComb].M) * v->frac_QE  [ThisFracComb].M
				}
			}
	}

	if (todo) {
		real matrix dpE_dE, dpSig_dSig; real scalar lcut, lcat
		pointer (real colvector) pYi_lcat, pYi_lcatm1

		// Translate scores w.r.t. partialled errors and variance to ones w.r.t. unpartialled ones
		if (v->d_uncens) {
			t = I(cols(beta)), -beta'
			(dpE_dE = v->J_d_cens_d_0)[, v->cens_uncens] = t
			v->dPhi_dE = dPhi_dpE * dpE_dE
			(dpSig_dSig = v->J_d2_cens_d2_0)[, v->SigIndsCensUncens] = (t#t)[v->CensLTInds,] * v->dSig_dLTSig
			v->dPhi_dpE_dSig[v->one2N, v->SigIndsCensUncens] =
					dPhi_dpE_dSig(v->EUncens, v->one2N, beta, invSig_uncens, Sig_uncens_cens, dPhi_dpE, d_cens, v->d_uncens, v->_dPhi_dpE_dSig, v->J_d_uncens_d_cens_0)
			v->dPhi_dSig = dPhi_dpSig * dpSig_dSig + v->dPhi_dpE_dSig
		} else {
			v->dPhi_dE  [v->one2N, v->cens_uncens      ] = dPhi_dpE
			v->dPhi_dSig[v->one2N, v->SigIndsCensUncens] = dPhi_dpSig
		}

		if (d_two_cens) {
			if (v->d_uncens) {
				v->dPhi_dF = *pdPhi_dpF * dpE_dE
				v->dPhi_dpF_dSig[v->one2N, v->SigIndsCensUncens] =
						dPhi_dpE_dSig(v->EUncens, v->one2N, beta, invSig_uncens, Sig_uncens_cens, v->dPhi_dpF, d_cens, v->d_uncens,
											v->_dPhi_dpF_dSig, v->J_d_uncens_d_cens_0)
				v->dPhi_dSig = v->dPhi_dSig + v->dPhi_dpF_dSig
			} else
				v->dPhi_dF[v->one2N, v->cens_uncens] = *pdPhi_dpF

			if (ThisNumCuts) {
				lcat = (lcut = ThisNumCuts) + (i = v->d_oprobit) + 1
				for (; i; i--) {  // for each oprobit eq
					pYi_lcat = &(v->Yi[v->one2N, --lcat])
					for (j = (v->vNumCuts)[i]; j; j--) {
						pYi_lcatm1 = &(v->Yi[v->one2N, --lcat])
						v->dPhi_dcuts[v->one2N, (v->CutInds)[lcut--]] = v->dPhi_dE[v->one2N, oprobit[i]] :* *pYi_lcatm1 + v->dPhi_dF[v->one2N, oprobit[i]] :* *pYi_lcat
						pYi_lcat = pYi_lcatm1
					}
				}
			}
			v->dPhi_dE = v->dPhi_dE + v->dPhi_dF
		}
	}
	return(Phi)
}

// translate draws or nodes at a given level, possibly adaptively shifted, into total effects of random effects and coefficients
void cmp_model::BuildTotalEffects(real scalar l) {
	real scalar r, eq; pointer(real matrix) scalar pUT; pointer (struct RE scalar) scalar RE
	RE = &((*REs)[l])
	for (r=NumREDraws[l+1]; r; r--) {
		if (RE->HasRC) {
			pUT = &RE->J_N_NEq_0
			if (cols(RE->REInds))
  			setcol(pUT, RE->REEqs, RE->U[r].M * RE->T[, RE->REInds]) // REs
		} else
			pUT                              = & (RE->U[r].M * RE->T)     // REs

		for (eq=RE->NEq; eq; eq--)               // RCs
			if (RE->RCk[eq])
				setcol(pUT, eq, *getcol(*pUT, eq) + quadrowsum((RE->U[r].M * RE->T[, RE->RCInds[eq].M]) :* RE->X[eq].M)) // RCs * X
		if (HasGamma)
			for (eq=cols(RE->GammaEqs); eq; eq--)
				RE->TotalEffect[r,RE->GammaEqs[eq]].M = *pUT * RE->invGamma[,eq]
		else
			for (eq=cols(RE->GammaEqs); eq; eq--)
				RE->TotalEffect[r,RE->GammaEqs[eq]].M = *getcol(*pUT, eq)
	}
}

void cmp_model::BuildXU(real scalar l) {
	real scalar c, r, j, k, e, eq1, eq2; pointer (struct RE scalar) scalar RE; pointer(struct subview scalar) scalar v; real colvector Ue
	RE = &((*REs)[l])
	if (RE->HasRC)
		for (r=RE->R; r; r--) {  // pre-compute X-U products in order most convenient for computing scores w.r.t upper-level T's
			k = e = 0
			for (eq1=1; eq1<=RE->NEq; eq1++)
				for (c=1; c<=RE->RCk[eq1] + anyof(RE->REEqs, eq1); c++) {
          Ue = *getcol(RE->U[r].M, ++e)
					RE->pXU[r,++k] = &( c <= RE->RCk[eq1]? Ue :* *getcol(RE->X[eq1].M, c..RE->RCk[eq1]) : base->J_N_0_0 )
					if (anyof(RE->REEqs, eq1))
						RE->pXU[r,k] = &(*RE->pXU[r,k], Ue)
					for (eq2=eq1+1; eq2<=RE->NEq; eq2++) {
						RE->pXU[r,++k] = &( RE->RCk[eq2]? Ue :*RE->X[eq2].M : base->J_N_0_0 )
						if (anyof(RE->REEqs, eq2))
							RE->pXU[r,k] = &(*RE->pXU[r,k], Ue)  // avoid concatenation?
					}
				}
		}
	else
		for (r=RE->R; r; r--)  // simpler form works when just REs
			for (j=RE->d; j; j--)
				RE->pXU[r,j] = getcol(RE->U[r].M, j)

	for (v = subviews; v; v = v->next)
		for (r=RE->R; r; r--)
			for (j=cols(v->XU[l].M); j; j--)
				if (RE->pXU[r,j])
					v->XU[l].M[r,j].M = (*RE->pXU[r,j])[v->SubsampleInds,]
}

void cmp_model::_st_view(real matrix V, real scalar missing, string rowvector vars) {
	pragma unused missing
	if (vars != ".")
		st_view(V, ., vars, st_global("ML_samp"))
}


// main evaluator routine
void cmp_lf1(transmorphic M, real scalar todo, real rowvector b, real colvector lnf, real matrix S, real matrix H) {
  pragma unused H
  pointer(class cmp_model scalar) scalar mod
  mod = moptimize_init_userinfo(M, 1)
  mod->lf1(M, todo, b, lnf, S)
}


void cmp_model::lf1(transmorphic M, real scalar todo, real rowvector b, real colvector lnf, real matrix S) {
	real matrix t, L_g, invGamma, C, dOmega_dSig, L_gv, L_gvr, sThetaScores, sCutScores
	real scalar e, c, i, j, k, l, m, _l, r, tEq, EUncensEq, ECensEq, FCensEq, NewIter, eq, eq1, eq2, _eq, c1, c2, cut, lnsigWithin, lnsigAccross, atanhrhoAccross, atanhrhoWithin, Iter
	real colvector shift, lnLmin, lnLmax, lnL, out
	pointer(struct subview scalar) scalar v
	pointer(real matrix) scalar pdlnL_dtheta, pdlnL_dSig, pThisQuadXAdapt_j, pt
	pointer(struct scores scalar) scalar pScores
	pointer (struct RE scalar) scalar RE
	pointer(pointer (real matrix) colvector) scalar pThisQuadXAdapt
	pragma unset out; pragma unset sThetaScores; pragma unset sCutScores

	lnf = .

	for (i=1; i<=d; i++) {
		REs->theta[i].M = moptimize_util_xb(M, b, i)
		if (rows(REs->theta[i].M)==1) REs->theta[i].M = J(base->N, 1, REs->theta[i].M)
	}

	for (j=1; j<=rows(GammaInd); j++)
		Gamma[|GammaInd[j,]|] = -moptimize_util_xb(M, b, i++)

	for (eq1=1; eq1<=d; eq1++)
		if (vNumCuts[eq1])
			for (cut=2; cut<=vNumCuts[eq1]+1; cut++) {
				cuts[cut, eq1] = moptimize_util_xb(M, b, i++)
				if (trunceqs[eq1])
					if (any(indicators[,eq1] :& ((Lt[eq1].M :< . :& cuts[cut, eq1] :< Lt[eq1].M) :| cuts[cut, eq1]:>Ut[eq1].M)))
						return
			}

  for (l=1; l<=L; l++) {  // loop over hierarchy levels
		RE = &((*REs)[l])
		RE->sig = RE->rho = J(1, 0, 0)
		if (RE->covAcross==0)  // exchangeable across?
			lnsigWithin = lnsigAccross = moptimize_util_xb(M, b, i++)

		for (eq1=1; eq1<=RE->NEq; eq1++) {
			if (RE->covWithin[RE->Eqs[eq1]]==0 & RE->covAcross)  // exchangeable within but not across?
				lnsigWithin = lnsigAccross

			for (c1=1; c1<=RE->NEff[eq1]; c1++)
				if (RE->FixedSigs[RE->Eqs[eq1]] == .) {
					if (RE->covWithin[RE->Eqs[eq1]] & RE->covAcross)  // exchangeable neither within nor accross?
						lnsigWithin = moptimize_util_xb(M, b, i++)
				  if (SigXform==0 & lnsigWithin==0)
						return
					RE->sig = RE->sig, (SigXform? exp(lnsigWithin) : lnsigWithin)
				} else
					RE->sig = RE->sig, RE->FixedSigs[RE->Eqs[eq1]]
		}

		if (RE->covAcross==0 & RE->d > 1)  // exchangeable across?
			atanhrhoAccross = moptimize_util_xb(M, b, i++)
		for (eq1=1; eq1<=RE->NEq; eq1++) {
			if (RE->covWithin[RE->Eqs[eq1]] == 2)  // independent?
				atanhrhoWithin = 0
			else if (RE->covWithin[RE->Eqs[eq1]]==0 & RE->NEff[eq1] > 1)  // exchangeable within?
				atanhrhoWithin = moptimize_util_xb(M, b, i++)
			for (c1=1; c1<=RE->NEff[eq1]; c1++) {
				for (c2=c1+1; c2<=RE->NEff[eq1]; c2++) {
					if (RE->covWithin[RE->Eqs[eq1]] == 1) // unstructured?
						atanhrhoWithin = moptimize_util_xb(M, b, i++)
					RE->rho = RE->rho, atanhrhoWithin
				}
				for (eq2=eq1+1; eq2<=RE->NEq; eq2++)
					for (c2=1; c2<=RE->NEff[eq2]; c2++)
						if (RE->FixedRhos[RE->Eqs[eq2],RE->Eqs[eq1]] == .) {
							if (RE->covAcross == 1) // unstructured?
								atanhrhoAccross = moptimize_util_xb(M, b, i++)
							RE->rho = RE->rho, atanhrhoAccross
						} else
							RE->rho = RE->rho, RE->FixedRhos[RE->Eqs[eq2],RE->Eqs[eq1]]
			}
		}
	}

	if (HasGamma) {
		invGamma = luinv(Gamma)
		if (invGamma[1,1] == .) return
		for (eq1=d; eq1; eq1--)  // XXX is this faster than manually multipling invGamma by the individual theta columns and summing?
			Theta[base->one2N,eq1] = editmissing(REs->theta[eq1].M, 0)  // only time missing values would appear and be used is when multiplied by invGamma with 0's in corresponding entries
		for (eq1=d; eq1; eq1--)
			REs->theta[eq1].M = Theta * invGamma[,eq1]
	}

	if (WillAdapt)
		if (NewIter = (Iter = moptimize_result_iterations(M)) != LastIter) {
			LastIter = Iter
			if (Adapted==0)
				if (AdaptNextTime) {
					setAdaptNow(1)
					printf("\n{res}Performing Naylor-Smith adaptive quadrature.\n")
				} else {
					if (cols(Lastb))
						AdaptNextTime = mreldif(b, Lastb) < .1  // criterion to begin adaptive phase
					Lastb = b
				}
		}

  for (l=1; l<=L; l++) {
		RE = &((*REs)[l])
		if (RE->d == 1)
			RE->Sig = (RE->T = RE->sig) * RE->sig
		else {
			k = 0
			for (j=1; j<=RE->d; j++)
				for (i=j+1; i<=RE->d; i++)
					if (SigXform)
						if (RE->rho[++k]>100)
							RE->Rho[i,j] = 1
						else if (RE->rho[k]<-100)
							RE->Rho[i,j] = -1
						else
  						RE->Rho[i,j] = tanh(RE->rho[k])
					else
						RE->Rho[i,j] = RE->rho[++k]
			_makesymmetric(RE->Rho)
			RE->T = cholesky(RE->Rho)' :* RE->sig
			if (RE->T[1,1] == .) return
			RE->Sig = quadcross(RE->sig,RE->sig) :* RE->Rho
		}

		if (todo)
			RE->D = dSigdsigrhos(SigXform, RE->sig, RE->Sig, RE->rho, RE->Rho) * RE->dSigdParams

		if (HasGamma)
			RE->invGamma = invGamma[RE->Eqs,RE->GammaEqs]

		if (l < L) {
			BuildTotalEffects(l)
			for (eq1=cols(RE->GammaEqs); eq1; eq1--) {  // compute effect of first draws
				_eq = RE->GammaEqs[eq1]
				(*REs)[l+1].theta[_eq].M = rows(RE->TotalEffect[1,_eq].M)? RE->theta[_eq].M :+ RE->TotalEffect[1,_eq].M : RE->theta[_eq].M
			}
			for (eq1=d; eq1; eq1--)  // by default lower errors = upper ones, for eqs with no random effects/coefs at this level
				if (anyof(RE->GammaEqs,eq1)==0)
					(*REs)[l+1].theta[eq1].M = RE->theta[eq1].M
			if (todo)
				RE->D = ghk2_dTdV(RE->T') * RE->D
			if (AdaptivePhaseThisEst & NewIter)
				RE->ToAdapt = RE->JN12
			RE->AdaptivePhaseThisIter = 0
		}
	}

	if (HasGamma) {
		if (todo) {
			dOmega_dSig = (Lmatrix(cols(invGamma))*(invGamma'#invGamma')*Dmatrix(rows(invGamma))) // QE2QSig(invGamma)
			t = colshape(invGamma, 1)
			t = (colshape(base->Sig,1)'#Idd)[vLd,vIKI] * (Idd#t + t#Idd)[,vKd]
			for (m=d; m; m--)
				for (c=1; c<=G[m]; c++)
					dOmega_dGamma[m,c].M = t * invGamma[m,]'#invGamma[,(*GammaIndByEq[m])[c]]
		}
		pOmega = &(quadcross(invGamma, base->Sig) * invGamma)
	}

	for (v = subviews; v; v = v->next) {
		v->Omega = quadcross(v->QE, *pOmega) * v->QE
		if (todo)
			if (HasGamma) {
				for (m=d; m; m--) {
					if (G[m] & v->TheseInds[m])
						for (c=1; c<=G[m]; c++)
							v->dOmega_dGamma[m,c].M = v->QSig * dOmega_dGamma[m,c].M
				}
				v->QEinvGamma    = quadcross(v->QE, invGamma')
				v->invGammaQSigD = quadcross(v->QSig, dOmega_dSig) * base->D
			} else {
				v->QEinvGamma    = v->QE'
				v->invGammaQSigD = quadcross(v->QSig, base->D)
			}
	}

	base->plnL = &(lnf = base->J_N_1_0)
	if (todo) S = S0

	do {  // for each draw combination
		for (v = subviews; v; v = v->next) {
			tEq = EUncensEq = ECensEq = FCensEq = 0
			for (i=1; i<=d; i++)
				if (v->TheseInds[i]==`cmp_mprobit') {  // handle mprobit eqs below
					++ECensEq
					++FCensEq
				} else {
					if (v->NotBaseEq[i]) {
						v->theta[i].M = base->theta[i].M[v->SubsampleInds]

						if (v->TheseInds[i] & v->TheseInds[i]<.) {
							if (v->TheseInds[i]==`cmp_cont')
								v->EUncens[v->one2N,++EUncensEq] = v->y[i].M - v->theta[i].M
							else {
								++ECensEq
								if (v->TheseInds[i]==`cmp_left' | v->TheseInds[i]==`cmp_int')
									setcol(v->pECens, ECensEq, v->y[i].M - v->theta[i].M)
								else if (v->TheseInds[i]==`cmp_right')
									setcol(v->pECens, ECensEq, v->theta[i].M - v->y[i].M)
								else if (v->TheseInds[i]==`cmp_probit')
									setcol(v->pECens, ECensEq, -v->theta[i].M)
								else if (v->TheseInds[i]==`cmp_probity1' | v->TheseInds[i]==`cmp_frac')
									setcol(v->pECens, ECensEq, v->theta[i].M)
								else if (v->TheseInds[i]==`cmp_oprobit') {
									if (trunceqs[i]) {
										t = v->y[i].M :> v->vNumCuts[i] // bit of inefficiency in truncated oprobit case
										setcol(v->pECens, ECensEq, (t :* v->Ut[i].M + (1:-t) :* cuts[v->y[i].M:+1, i]) - v->theta[i].M)
									} else
										setcol(v->pECens, ECensEq, cuts[v->y[i].M:+1, i] - v->theta[i].M)
								} else // roprobit
									setcol(v->pECens, ECensEq, -v->theta[i].M)
								if (v->pF)
									if (NonbaseCases[ECensEq]) {
										++FCensEq
                    v->Fi = J(0,0,0)
										if (v->TheseInds[i]==`cmp_int')
											v->Fi = v->yL[i].M - v->theta[i].M
										else if (v->TheseInds[i]==`cmp_oprobit')
											if (trunceqs[i]) {
												t = v->y[i].M
												v->Fi = (t :* v->Lt[i].M + (1:-t) :* cuts[v->y[i].M, i]) - v->theta[i].M
											} else
												v->Fi = cuts[ v->y[i].M, i] - v->theta[i].M
										else if (trunceqs[i])
											if (v->TheseInds[i]==`cmp_left')
												v->Fi = v->Lt[i].M - v->theta[i].M
											else if (v->TheseInds[i]==`cmp_right')
												v->Fi = v->theta[i].M - v->Ut[i].M
											else if (v->TheseInds[i]==`cmp_probit')
												v->Fi = v->Lt[i].M - v->theta[i].M
											else if (v->TheseInds[i]==`cmp_probity1')
												v->Fi = v->theta[i].M - v->Ut[i].M
                    if (rows(v->Fi)) setcol(v->pF, FCensEq, v->Fi)
									}
							}

							if (trunceqs[i]) {
								++tEq
								if (v->TheseInds[i]==`cmp_left') {
									setcol(v->pEt, tEq, v->Ut[i].M - v->theta[i].M)
									setcol(v->pFt, tEq, v->Fi)
								} else if (v->TheseInds[i]==`cmp_right') {
									setcol(v->pEt, tEq, v->theta[i].M - v->Lt[i].M)
									setcol(v->pFt, tEq, v->Fi)
								} else if (v->TheseInds[i]==`cmp_probit') {
									setcol(v->pEt, tEq, v->Ut[i].M - v->theta[i].M)
									setcol(v->pFt, tEq, v->Fi)
								} else if (v->TheseInds[i]==`cmp_probity1') {
									setcol(v->pEt, tEq, v->theta[i].M - v->Lt[i].M)
									setcol(v->pFt, tEq, v->Fi)
								} else if (anyof((`cmp_cont',`cmp_oprobit',`cmp_int'), v->TheseInds[i])) {
									setcol(v->pEt, tEq, v->Ut[i].M - v->theta[i].M)
									setcol(v->pFt, tEq, v->Lt[i].M - v->theta[i].M)
								}
							}
						}
					}
				}

      for (j=rows(MprobitGroupInds); j; j--) // relative-difference mprobit errors
				if (v->mprobit[j].d > 0) {
					out = base->theta[v->mprobit[j].out].M[v->SubsampleInds]
					for (i=v->mprobit[j].d; i; i--)
						setcol(v->pECens, (v->mprobit[j].res)[i], out - base->theta[(v->mprobit[j].in)[i]].M[v->SubsampleInds])
				}

			if (v->pECens) _editmissing(*v->pECens,  1.701e+38) // maxfloat()--just a big number
			if (v->pF    ) _editmissing(*v->pF    , -1.701e+38)
			if (v->pEt   ) _editmissing(*v->pEt   ,  1.701e+38)
			if (v->pFt   ) _editmissing(*v->pFt   , -1.701e+38)

			if (v->d_cens) {
				lnL = lnLCensored(v, todo)
				if (v->d_uncens)
					lnL = lnL + lnLContinuous(v, todo)
			} else
				lnL = lnLContinuous(v, todo)

			if (v->d_trunc)
				lnL = lnL - lnLTrunc(v, todo)

      (*(base->plnL))[v->SubsampleInds] = lnL

			if (todo) {
				if (v->d_cens)
					if (v->d_uncens) {
						pdlnL_dtheta = &(v->dphi_dE + v->dPhi_dE)
						pdlnL_dSig =  &(v->dphi_dSig + v->dPhi_dSig)
					} else {
						pdlnL_dtheta = &(v->dPhi_dE)
						pdlnL_dSig   =  &(v->dPhi_dSig)
					}
				else {
					pdlnL_dtheta = &(v->dphi_dE)
					pdlnL_dSig   = &(v->dphi_dSig)
				}
				if (v->d_trunc) {
					pdlnL_dtheta = &(*pdlnL_dtheta - v->dPhi_dEt)
					pdlnL_dSig   = &(*pdlnL_dSig   - v->dPhi_dSigt)
				}

				pdlnL_dtheta = &(*pdlnL_dtheta * v->QEinvGamma)

				if (L == 1) {
					                   S[v->SubsampleInds, Scores.ThetaScores  ] = *pdlnL_dtheta
					if (NumCuts)  S[v->SubsampleInds, Scores.  CutScores  ] = v->dPhi_dcuts
					if (cols(base->D)) S[v->SubsampleInds, Scores.  SigScores.M] = *pdlnL_dSig * v->invGammaQSigD
					for (i=m=1; m<=d; m++)
						for (c=1; c<=G[m]; c++)
   						S[v->SubsampleInds, Scores.GammaScores[i++].M] = v->TheseInds[m]?
								(v->NotBaseEq[(*GammaIndByEq[m])[c]] ? *pdlnL_dSig*v->dOmega_dGamma[m,c].M + (*pdlnL_dtheta)[v->one2N,m]:*v->theta[(*GammaIndByEq[m])[c]].M :
                                                            *pdlnL_dSig*v->dOmega_dGamma[m,c].M                                                                        ) :
                v->J_N_1_0
				} else {
					_editmissing(*pdlnL_dtheta, 0)
					_editmissing(v->dPhi_dcuts, 0)
					_editmissing(*pdlnL_dSig, 0)

					pScores = &(v->Scores[L].M[ThisDraw[L]])
					                   pScores->ThetaScores  = *pdlnL_dtheta
					if (NumCuts)  pScores->CutScores    = v->dPhi_dcuts
					if (cols(base->D)) pScores->TScores[L].M = *pdlnL_dSig
					for (i=m=1; m<=d; m++)
						if (v->TheseInds[m])
							for (c=1; c<=G[m]; c++)
								pScores->GammaScores[i++].M  = *getcol(*pdlnL_dtheta,m) :* v->theta[(*GammaIndByEq[m])[c]].M
						else
							i = i + G[m]

					for (l=1; l<L; l++) {
						RE = &((*REs)[l])
						 // dlnL/dSigparams = dlnL/dE^ * dE^/dE * dE/dT * dT/dOmega * dOmega/dSig * dSig/dSigparams = dlnL/dE * QE * {X*U} * dT_dSig * dOmega_dSig * D. Last 3 terms draw-invariant, so saved for end
						for (e=k=eq1=1; eq1<=RE->NEq; eq1++)
							if (RE->HasRC)
								for (c=1; c<=cols(RE->RCInds[eq1].M)+anyof(RE->REEqs, eq1); c++)
									for (eq2=eq1; eq2<=RE->NEq; eq2++)
										PasteAndAdvance(pScores->TScores[l].M, k,
											(v->XU[l].M[ThisDraw[l+1], e++].M) :* *getcol(pScores->ThetaScores, RE->Eqs[eq2]))
							else
								PasteAndAdvance(pScores->TScores[l].M, k, (v->XU[l].M[ThisDraw[l+1], eq1].M) :* *getcol(pScores->ThetaScores, RE->Eqs[|eq1 \ .|]))
					}
				}
			}
		}

		for (l=L-1; l; l--) {  // If L=1, sets l=0 as needed to terminate do loop. Usually this loop runs once.
			RE = &((*REs)[l])

			RE->lnLByDraw[RE->one2N, ThisDraw[l+1]] = _panelsum(*((*REs)[l+1].plnL), (*REs)[l+1].Weights, RE->IDRangesGroup)
			if (ThisDraw[l+1] < RE->R)
				ThisDraw[l+1] = ThisDraw[l+1] + 1
			else {
				if (Adapted)
					RE->lnLByDraw = RE->lnLByDraw + RE->AdaptiveShift  // even if active adaptation done, add adaptive ln(det(C)*normalden(QuadXAdapt)/normalden(QuadX))

        // for each group, make weights proportional to L (not lnL) for the group/obs at next-lower level
				t = RE->lnLlimits :- rowminmax(RE->lnLByDraw)  // In summing groups' Ls, shift just enough to prevent underflow in exp(), but if necessary even less to avoid overflow
				lnLmin = t[,1]; lnLmax = t[,2]
				t = lnLmin:*(lnLmin:>0) - lnLmax; shift = t :* (t :< 0) + lnLmax // parallelizes better than rowminmax()
				L_g = editmissing(exp(RE->lnLByDraw:+shift), 0)  // un-log likelihood for each group & draw; lnL=. => L=0
				if (Quadrature)
					L_g = L_g :* RE->QuadW
				RE->plnL = &quadrowsum(L_g)  // in non-quadrature case, sum rather than average of likelihoods across draws
				if (todo | (AdaptivePhaseThisEst & WillAdapt))
					L_g = editmissing(L_g :/ *(RE->plnL), 0)  // normalize L_g's as weights for obs-level scores or for use in Naylor-Smith adaptation

				if (AdaptivePhaseThisEst & NewIter) {
					pThisQuadXAdapt = &asarray(RE->QuadXAdapt, ThisDraw[|.\l|])
					if (rows(*pThisQuadXAdapt)==0) {  // initialize if needed
						asarray(RE->QuadXAdapt, ThisDraw[|.\l|], RE->JN1pQuadX)
						pThisQuadXAdapt = &asarray(RE->QuadXAdapt, ThisDraw[|.\l|])
					}

          if (RE->d == 1) {  // optimized code for 1-D case
            for (j=RE->N; j; j--)
              if (RE->ToAdapt[j]) {
                RE->QuadMean[j].M = (t = L_g[j,]) * *(pThisQuadXAdapt_j = (*pThisQuadXAdapt)[j])  // weighted sum

                C = *pThisQuadXAdapt_j :- RE->QuadMean[j].M; C = sqrt(t * (C :* C))

                if (C == .) {  // diverged? try restarting, but decrement counter to prevent infinite loop
                  RE->ToAdapt[j] = RE->ToAdapt[j] - 1
                  pThisQuadXAdapt_j = (*pThisQuadXAdapt)[j] = &(RE->QuadX)
                  RE->AdaptiveShift[j,] = RE->J1R0
                } else {
                  RE->QuadSD[j].M = C
                  if (mreldif(*pThisQuadXAdapt_j, *(pt = &(RE->QuadX * C :+ RE->QuadMean[j].M))) < QuadTol) {  // has adaptation converged for this ML search iteration?
                    RE->ToAdapt[j] = 0
                    continue
                  }
                  (*pThisQuadXAdapt)[j] = pt
                  if (pThisQuadXAdapt_j != (&(RE->QuadX))) pThisQuadXAdapt_j = pt
                  RE->AdaptiveShift[j,] = (ln(C) - 0.91893853320467267 /*ln(2pi)/2*/) :- (.5 * (*pt :* *pt)' + RE->lnnormaldenQuadX)
                }

                for (r=RE->R; r; r--)
                  RE->U[r].M[|RE->Subscript[j].M|] = J(RE->IDRangeLengths[j], 1, (*pThisQuadXAdapt_j)[r,])  // faster to explode these here than after multiplying by T in BuildTotalEffects(), BuildXU()
              }
          } else {
            for (j=RE->N; j; j--)
              if (RE->ToAdapt[j]) {
                RE->QuadMean[j].M = (t = L_g[j,]) * *(pThisQuadXAdapt_j = (*pThisQuadXAdapt)[j])  // weighted sum

                C = cholesky(crossdev(*pThisQuadXAdapt_j, RE->QuadMean[j].M, t, *pThisQuadXAdapt_j, RE->QuadMean[j].M))

                if (C[1,1] == .) {  // diverged? try restarting, but decrement counter to prevent infinite loop
                  RE->ToAdapt[j] = RE->ToAdapt[j] - 1
                  pThisQuadXAdapt_j = (*pThisQuadXAdapt)[j] = &(RE->QuadX)
                  RE->AdaptiveShift[j,] = RE->J1R0
                } else {
                  RE->QuadSD[j].M = diagonal(C)
                  if (mreldif(*pThisQuadXAdapt_j, *(pt = &(RE->QuadX * C' :+ RE->QuadMean[j].M))) < QuadTol) {  // has adaptation converged for this ML search iteration?
                    RE->ToAdapt[j] = 0
                    continue
                  }
                  (*pThisQuadXAdapt)[j] = pt
                  if (pThisQuadXAdapt_j != (&(RE->QuadX))) pThisQuadXAdapt_j = pt
                  RE->AdaptiveShift[j,] = quadrowsum_lnnormalden(*pt, quadcolsum(ln(RE->QuadSD[j].M),1))' - RE->lnnormaldenQuadX
                }

                for (r=RE->R; r; r--)
                  RE->U[r].M[|RE->Subscript[j].M|] = J(RE->IDRangeLengths[j], 1, (*pThisQuadXAdapt_j)[r,])  // faster to explode these here than after multiplying by T in BuildTotalEffects(), BuildXU()
              }
          }

          if (RE->AdaptivePhaseThisIter = any(RE->ToAdapt) * mod(RE->AdaptivePhaseThisIter-1, QuadIter)) {  // not converged and haven't hit max number of adaptations?
						BuildTotalEffects(l)
						if (_todo)
							BuildXU(l)
					}
				}
				ThisDraw[l+1] = 1
			}

			if (ThisDraw[l+1] > 1 | RE->AdaptivePhaseThisIter) {  // no (more) carrying? propagate draw changes down the tree
				for (_l=l; _l<L; _l++)
					for (eq=cols(RE->GammaEqs); eq; eq--) {
						_eq = RE->GammaEqs[eq]
						(*REs)[_l+1].theta[_eq].M = cols((*REs)[_l].TotalEffect[ThisDraw[_l+1], _eq].M)? (*REs)[_l].theta[_eq].M + (*REs)[_l].TotalEffect[ThisDraw[_l+1], _eq].M : (*REs)[_l].theta[_eq].M
					}
				break
 			}

			// finished the group's (adaptive) draws
			if (todo) // obs-level score for next level up is avg of scores over this level's draws, weighted by group's L for each draw
				for (v = subviews; v; v = v->next) {
					L_gv = L_g[v->id[l].M, RE->one2R]
					for (r=1; r<=NumREDraws[l+1]; r++) {
						L_gvr = L_gv[v->one2N, r]

            scoreAccum(sThetaScores, r, L_gvr, v->Scores[l+1].M[r].ThetaScores)
						if (NumCuts)
              scoreAccum(sCutScores, r, L_gvr, v->Scores[l+1].M[r].CutScores)
						for (i=L; i; i--)
							if ((*REs)[i].NSigParams)
                scoreAccum(sTScores[i].M, r, L_gvr, v->Scores[l+1].M[r].TScores[i].M)
						for (i=cols(v->Scores.M.GammaScores); i; i--)
							if (rows(v->Scores[l+1].M[r].GammaScores[i].M))
                scoreAccum(sGammaScores[i].M, r, L_gvr, v->Scores[l+1].M[r].GammaScores[i].M)
					}
					if (l==1) { // final scores
						S[v->SubsampleInds, Scores.ThetaScores] = *Xdotv(sThetaScores, v->WeightProduct)
						if (NumCuts)
							S[v->SubsampleInds, Scores.CutScores] = *Xdotv(sCutScores, v->WeightProduct)
						if (base->NSigParams)
							S[v->SubsampleInds, Scores.SigScores[L].M] = *Xdotv(sTScores[L].M * v->invGammaQSigD, v->WeightProduct)
						for (i=L-1; i; i--)
							if ((*REs)[i].NSigParams)
								S[v->SubsampleInds, Scores.SigScores[i].M] = *Xdotv(sTScores[i].M * (*REs)[i].D, v->WeightProduct)
						for (i=m=1; m<=d; m++)
							for (c=1; c<=G[m]; c++) {
								if (v->TheseInds[m])
									S[v->SubsampleInds, Scores.GammaScores[i].M]  = *Xdotv(sGammaScores[i].M + sTScores[L].M * v->dOmega_dGamma[m,c].M, v->WeightProduct)
								else
									S[v->SubsampleInds, Scores.GammaScores[i].M] = v->J_N_1_0
								i++
							}
					} else {
							v->Scores[l].M[ThisDraw[l]].ThetaScores = sThetaScores
						if (NumCuts)
							v->Scores[l].M[ThisDraw[l]].CutScores = sCutScores
						for (i=L; i; i--)
							if ((*REs)[i].NSigParams)
								v->Scores[l].M[ThisDraw[l]].TScores[i].M = sTScores[i].M
						for (i=cols(v->Scores.M[1].GammaScores); i; i--)
							v->Scores[l].M[ThisDraw[l]].GammaScores[i].M = sGammaScores[i].M
					}
				}

			RE->plnL = &(ln(*(RE->plnL)) - shift)
			if (Quadrature==0)
				RE->plnL = &(*(RE->plnL) :- RE->lnNumREDraws)  // in simulation (vs quadrature), average unweighted evaluations rather than summing weighted ones
		}
	} while (l) // exit when adding one more draw causes carrying all the way accross the draw counters, back to 1, 1, 1...

	if (L > 1) {
		lnf = quadsum(rows(REs->Weights)? REs->Weights :* *(REs->plnL) : *(REs->plnL), 1)
		if (AdaptivePhaseThisEst & NewIter) {
			if (AdaptivePhaseThisEst = mreldif(LastlnLThisIter, LastlnLLastIter) >= 1e-6)
				LastlnLLastIter = LastlnLThisIter
			else
				printf("\n{res}Adaptive quadrature points fixed.\n")
		}
		if (lnf < .) LastlnLThisIter = lnf
		if (todo == 0)
			lnf = J(base->N, 1, lnf/base->N)
	}
}

void cmp_gf1(transmorphic M, real scalar todo, real rowvector b, real colvector lnf, real matrix S, real matrix H) {
	pointer(class cmp_model scalar) scalar mod
	pragma unused H
	mod = moptimize_init_userinfo(M, 1)
  mod->gf1(M, todo, b, lnf, S)
}

void cmp_model::gf1(transmorphic M, real scalar todo, real rowvector b, real colvector lnf, real matrix S) {
	real matrix subscripts, _S; real scalar i, n, K
  pragma unset _S

	lf1(M, todo, b, lnf, _S)

	if (hasmissing(lnf) == 0) {
		lnf = *(REs->plnL)
		if (todo) {
			K = cols(b); n = moptimize_init_eq_n(M)  // numbers of eqs (inluding auxiliary parameters); number of parameters
			S = J(rows(lnf), K, 0)
			if (length(X) == 0) {
				X = smatrix(base->d)
				for (i=base->d;i;i--)
					X[i].M = editmissing(moptimize_util_indepvars(M, i),0)
			}
			for (i=1;i<=base->d;i++) {
				(subscripts = moptimize_util_eq_indices(M,i))[2,1] = .
				S[|subscripts|] = _panelsum(_S[,i] :* X[i].M, WeightProduct, REs->IDRanges)
			}

			if (n > d) { // any aux params?
				subscripts[1,2] = subscripts[2,2] + 1
				subscripts[2,2] = .
				S[|subscripts|] = _panelsum(_S[|.,base->d+1\.,.|], WeightProduct, REs->IDRanges)
			}
		}
	}
}

real scalar cmp_model::cmp_init(transmorphic M) {
	real scalar i, l, ghk_nobs, d_ghk, eq1, eq2, c, m, j, r, k, d_oprobit, d_mprobit, d_roprobit, start, stop, PrimeIndex, Hammersley, NDraws, HasRE, cols, d2
	real matrix Yi, U
	real colvector remaining, S
	real rowvector mprobit, Primes, t, one2d
	string scalar varnames, LevelName
	pointer(struct subview scalar) scalar v, next
	pointer(struct RE scalar) scalar RE
	pointer(real matrix) rowvector QuadData
	pragma unset Yi

	REs = &RE(L, 1)
	base = &((*REs)[L])
	base->d = d
	one2d = 1..d; d2 = d*(d+1)*.5

	Gamma = I(d)  // really will hold I - Gamma
	cuts = J(MaxCuts+2, d, 1.701e+38) // maxfloat()
	cuts[1,] = J(1, d, -1.701e+38) // minfloat()
	y = Lt = Ut = yL = smatrix(d)

	if (HasGamma) {
		dOmega_dGamma = smatrix(d,d)
		Idd = I(d*d)
		vLd = rowsum(Lmatrix(d) :*(1..d*d)) // X[vLd,] = L*X, but faster
		vKd = colsum(Kmatrix(d,d) :* (1::d*d))
		vIKI = colsum((I(d) # Kmatrix(d,d) # I(d)) :* (1::d^4))  // storage for I(d) # Kmatrix(d,d) # I(d) grows with d^8!
	} else
    pOmega = &(base->Sig)

	ThisDraw = J(1,L,1)

	for (l=L; l; l--) {
		RE = &((*REs)[l])
		RE->NEq = cols(RE->Eqs = selectindex(Eqs[,l]'))
		RE->NEff = NumEff[l,RE->Eqs]
		RE->GammaEqs = HasGamma? selectindex((GammaId * Eqs[,l])') : RE->Eqs
		RE->one2d = 1..( RE->d = rowsum(RE->NEff) )
		RE->theta = smatrix(d)
    RE->Rho = I(RE->d)
		RE->d2 = RE->d * (RE->d + 1) * .5
		RE->covAcross = cross( st_global("cmp_cov"+strofreal(l)) :== ("exchangeable"\"unstructured"\"independent"), 0::2 )
		for (i=d; i; i--)
			RE->covWithin = cross( st_global("cmp_cov"+strofreal(l)+"_"+strofreal(i)) :== ("exchangeable"\"unstructured"\"independent"), 0::2 ) \ RE->covWithin
		RE->FixedSigs = st_matrix("cmp_fixed_sigs"+strofreal(l))
		RE->FixedRhos = st_matrix("cmp_fixed_rhos"+strofreal(l))
	}

	_st_view(indicators, ., indVars)
	base->N = rows(indicators)
	base->one2N = base->N<10000? 1::base->N : .
	base->J_N_1_0 = J(base->N, 1, 0)
  base->J_N_0_0 = J(base->N, 0, 0)
	Theta = J(base->N,d,0)

	for (i=d; i; i--) {
		y[i].M = moptimize_util_depvar(M, i)
		if (trunceqs[i]) {
			_st_view(Lt[i].M, ., LtVars[i])
			_st_view(Ut[i].M, ., UtVars[i])
		}

		if (intregeqs[i])
			_st_view(yL[i].M,  ., yLVars[i])
	}

	for (l=L-1; l; l--)
		_st_view((*REs)[l].id,  ., "_cmp_id" + strofreal(l))

	for (l=L; l; l--) {
		RE = &((*REs)[l])
		if (_todo | HasGamma) {
			// build dSigdParams, derivative of sig, vech(rho) vector w.r.t. vector of actual sig, rho parameters, reflecting "exchangeable" and "independent" options
			real scalar accross, within, c1, c2
			t = J(0, 1, 0); i = 0 // index of entries in full sig, vech(rho) vector
			if (RE->covAcross==0) // exchangeable across?
				accross = ++i
			for (eq1=1; eq1<=RE->NEq; eq1++) {
				if (RE->covWithin[RE->Eqs[eq1]]==0) // exchangeable within?
					if (RE->covAcross) // exchangeable across?
						within = ++i
					else
						within = accross
				for (c1=1; c1<=RE->NEff[eq1]; c1++)
					if  (RE->FixedSigs[RE->Eqs[eq1]] == .)
						if (RE->covWithin[RE->Eqs[eq1]] & RE->covAcross) // exchangeable neither within nor across?
							t = t \ ++i
						else
							t = t \ within
					else
						t = t \ . // entry of sig vector corresponds to no parameter in model, being fixed
			}
			if (RE->covAcross==0 & RE->d>1) // exchangeable across?
				accross  = ++i
			for (eq1=1; eq1<=RE->NEq; eq1++) {
				if (RE->covWithin[RE->Eqs[eq1]]==0 & RE->NEff[eq1]>1) // exchangeable within?
					within = ++i
				for (c1=1; c1<=RE->NEff[eq1]; c1++) {
					for (c2=c1+1; c2<=RE->NEff[eq1]; c2++) {
						if (RE->covWithin[RE->Eqs[eq1]]==1) // unstructured
							within = ++i
						t = t \ (RE->covWithin[RE->Eqs[eq1]]==2? . : within) // independent?
					}
					for (eq2=eq1+1; eq2<=RE->NEq; eq2++)
						for (c2=1; c2<=RE->NEff[eq2]; c2++) {
							if (RE->FixedRhos[RE->Eqs[eq2],RE->Eqs[eq1]]==.) {
								if (RE->covAcross == 1) // unstructured
									accross = ++i
								t = t \ accross
							} else
								t = t \ .
					}
				}
			}
			RE->dSigdParams = (RE->NSigParams = i)? designmatrix(editmissing(t,i+1))[|.,.\.,i|] : J(RE->d2, 0, 0)
		}
	}

	Primes = 2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109
	if (L>1 & REType != "random" & length(Primes) < sum(NumEff) - 1 - (ghkType=="hammersley" | REType=="hammersley")) {
		errprintf("Number of unobserved variables to simulate too high for Halton-based simulation. Try {cmd:retype(random)}.\n")
		return(1001)
	}
	PrimeIndex = 1

	if (_todo) {
		Scores = scores()
		G = J(d, 1, 0); Scores.GammaScores = smatrix(d*d) // more than needed
		cols = d + 1
		if (HasGamma)
			for (c=m=1; m<=d; m++)
				for (i=1; i<=(G[m]=rows(*GammaIndByEq[m])); i++)
											Scores.GammaScores[c++].M = cols++
		             Scores.ThetaScores   = one2d
		if (NumCuts) Scores.CutScores     = cols..cols+NumCuts-1
		cols = cols + NumCuts
		Scores.SigScores = smatrix(L)
		for (l=1; l<=L; l++)
			if (t = cols((*REs)[l].dSigdParams)) {
											Scores.SigScores[l].M  = cols..cols+t-1
				cols = cols + t
			}
		S0 = J(base->N, cols-1, 0)
	}

	for (l=L-1; l; l--) {
		RE = &((*REs)[l])

		RE->N = RE->id[base->N]
    RE->R = NumREDraws[l+1]
		RE->one2N = RE->N<10000? 1::RE->N : .
		RE->J_N_1_0 = J(RE->N, 1, 0)
		RE->REInds = selectindex(tokens(st_global("cmp_rc"+strofreal(l))) :== "_cons")
		RE->X = RE->RCInds = smatrix(RE->NEq)

		RE->HasRC = 0
    RE->RCk = J(RE->NEq, 1, 0)
		for (start=j=1; j<=RE->NEq; j++) {
			if (HasRE = st_global("cmp_re"+strofreal(l)+"_"+strofreal(RE->Eqs[j])) != "")
				RE->REEqs = RE->REEqs, j
			if (strlen(varnames = st_global("cmp_rc"+strofreal(l)+"_"+strofreal(RE->Eqs[j])))) {
				RE->HasRC = 1
				RE->X[j].M = editmissing(st_data(., varnames, st_global("ML_samp")), 0) // missing values in X can occur if there's a random coefficient on a var used in one eq and not another, with a distinct sample
				RE->RCk[j] = cols(RE->X[j].M)
        stop = start + RE->RCk[j]
				RE->RCInds[j].M = start..stop-1
				start = stop + HasRE
			}
		}
		if (RE->HasRC) RE->J_N_NEq_0 = J(base->N, RE->NEq, 0)

		RE->IDRanges = panelsetup(RE->id, 1)
		RE->IDRangesGroup = l==L-1? RE->IDRanges : panelsetup(RE->id[(*REs)[l+1].IDRanges[,1]], 1)
    RE->IDRangeLengths = RE->IDRanges[,2] - RE->IDRanges[,1] :+ 1

    if (Quadrature) {
      RE->Subscript = smatrix(RE->N)
      for (j=RE->N;j;j--)
        RE->Subscript[j].M = RE->IDRanges[j,]', (.\.)
    }

		Hammersley = REType=="hammersley" & l==1

		LevelName = L>2? " for level " +strofreal(l) : ""

		if (Quadrature)
			if (RE->d <= 2)
				printf("{res}Random effects/coefficients%s modeled with Gauss-Hermite quadrature with %f integration points.\n", LevelName, RE->R)
			else {
				printf("{res}Random effects/coefficients%s modeled with sparse-grid quadrature.\n", LevelName)
				printf("Precision equivalent to that of one-dimensional quadrature with %f integration points.\n", RE->R)
			}
		else {
			printf("{res}Random effects/coefficients%s simulated.\n", LevelName)
			printf("    Sequence type = %s\n", REType)
			printf("    Number of draws per observation = %f\n", RE->R/REAnti)
			printf("    Include antithetic draws = %s\n", REAnti==2? "yes" : "no")
			printf("    Scramble = %s\n", ("no", "square root", "negative square root", "Faure-Lemieux")[1+REScramble])
			if ((REType=="halton" | REType=="ghalton") | (Hammersley & RE->d>1))
				printf("    Prime base%s = %s\n", RE->d > 1+Hammersley? "s" : "", invtokens(strofreal(Primes[PrimeIndex..PrimeIndex-1+RE->d-Hammersley])))
			if (l==1) printf(`"Each observation gets different draws, so changing the order of observations in the data set would change the results.\n\n"')
		}

		if (Quadrature) {
			QuadData = SpGr(RE->d, RE->R)
			RE->R = NDraws = NumREDraws[l+1] = rows(*QuadData[1])
			if (WillAdapt==0) printf("Number of integration points = %f.\n\n", NDraws)
// inefficiently duplicates draws over groups then parcels them out below
			U = J(RE->N, 1, *QuadData[1])
			RE->QuadX = *QuadData[1]
			RE->QuadW = *QuadData[2]'
			if (WillAdapt) {
				RE->QuadMean = RE->QuadSD = smatrix(RE->N)
				RE->QuadXAdapt = asarray_create("real", l)
				for (j=RE->N; j; j--)
					RE->QuadSD[j].M = J(RE->d, 1, .)
				RE->AdaptiveShift = J(RE->N, NDraws, 0)
				RE->lnnormaldenQuadX = quadrowsum_lnnormalden(RE->QuadX,0)'
				LastlnLThisIter=0; LastlnLLastIter=1
        RE->JN12 = J(RE->N, 1, 2)
        RE->J1R0 = J(1, RE->R, 0)
        RE->JN1pQuadX = J(RE->N, 1, &(RE->QuadX))
			}
		} else {
			NDraws = RE->R / REAnti
			if (REType=="random")
				U = invnormal(uniform(RE->N * NDraws / REAnti, RE->d))
			else if (REType=="halton" | Hammersley) {
				U = J(RE->N * NDraws, RE->d, 0)
				if (Hammersley)
					U[,1] = invnormal(J(RE->N, 1, (0.5::NDraws)/NDraws))
				for (r=1+Hammersley; r<=cols(U); r++)
					U[,r] = invnormal(halton2(rows(U), Primes[PrimeIndex++], (NULL, &ghk2SqrtScrambler(), &ghk2NegSqrtScrambler(), &ghk2FLScrambler())[1+REScramble]))
			} else {
				U = J(RE->N * NDraws, RE->d, 0)
				for (r=1; r<=cols(U); r++)
					U[,r] = invnormal(ghalton(rows(U), Primes[PrimeIndex++], uniform(1,1)))
			}
		}
		RE->one2R = 1..RE->R
		RE->U = smatrix(RE->R)
		RE->TotalEffect = smatrix(RE->R, d)
 		RE->pXU         = J(RE->R, sum((RE->NEq..1) :* RE->NEff), NULL)

		S = ((1::RE->N) * NDraws)[RE->id]
		for (r=NDraws; r; r--) {
			RE->U[r].M = U[S, RE->one2d]
			if (REAnti == 2)
				RE->U[r+RE->R*0.5].M = -RE->U[r].M
			S = S :- 1
		}

		RE->lnLlimits = ln(smallestdouble()) + 1, ln(maxdouble()) - (RE->lnNumREDraws = ln(RE->R)) - 1

		RE->lnLByDraw = J(RE->N, RE->R, 0)
	}

	if (L > 1)
		for (l=L; l; l--)
			if (st_global("parse_wexp"+strofreal(l)) != "") {
				RE = &((*REs)[l])
				RE->Weights = st_data(., st_global("cmp_weight"+strofreal(l)), st_global("ML_samp"))  // can't be a view because panelsum() doesn't accept weights in views
				if (l < L) RE->Weights = RE->Weights[RE->IDRanges[,1]]  // get one instance of each group's weight
				if (anyof(("pweight", "aweight"), st_global("parse_wtype"+strofreal(l))))  // normalize pweights, aweights to sum to # of groups
					if (l == 1)
						REs->Weights = RE->Weights * rows(RE->Weights) / quadsum(RE->Weights)  // fast way to divide by mean
					else
						for (j=(*REs)[l-1].N; j; j--) {
							S = (*REs)[l-1].IDRangesGroup[j,]', (.\.)
              t = RE->Weights[|S|]
							RE->Weights[|S|] = t * (rows(t) / quadsum(t))  // fast way to divide by mean
						}
				t = l==L? RE->Weights : RE->Weights[RE->id]
				WeightProduct = rows(WeightProduct)? WeightProduct:* t : t
			}

	ghk_nobs = 0; v = NULL
	remaining = 1::base->N
	d_cens = d_ghk = 0
	while (t = max(remaining)) { // build linked list of subviews onto data, each a set of rows with same indicator combination
		next = v; (v = &(subview()))->next = next  // add new subview to linked list
		remaining = remaining :* !(v->subsample = rowmin(indicators :== (v->TheseInds = indicators[t,])))
		v->SubsampleInds = selectindex(v->subsample)
		v->theta = smatrix(d)
		v->QE = diag(2*(v->TheseInds:==`cmp_right' :| v->TheseInds:==`cmp_probity1' :| v->TheseInds:==`cmp_frac') :- 1)
		v->N = colsum(v->subsample)
		v->one2N = v->N<10000? 1::v->N : .
		v->d_uncens = cols(v->uncens = selectindex(v->TheseInds:==`cmp_cont'))
		v->halfDmatrix = 0.5 * Dmatrix(v->d_uncens)
		v->d_oprobit = d_oprobit = cols(v->oprobit = selectindex(v->TheseInds:==`cmp_oprobit'))
		v->d_trunc = cols(v->trunc = selectindex(trunceqs))
		v->d_cens = cols(v->cens = selectindex(v->TheseInds:>`cmp_cont' :& v->TheseInds:<. :& (v->TheseInds:<`mprobit_ind_base' :| v->TheseInds:>=`roprobit_ind_base')))
		v->censnonfrac           = selectindex(v->TheseInds:>`cmp_cont' :& v->TheseInds:<. :& (v->TheseInds:<`mprobit_ind_base' :| v->TheseInds:>=`roprobit_ind_base') :& v->TheseInds:!=`cmp_frac')
		v->d_frac = cols(v->frac = cols(v->cens)? selectindex(v->TheseInds[v->cens]:==`cmp_frac') : J(1,0,0))
		d_cens = max((d_cens, v->d_cens))
    d_ghk = max((d_ghk, v->d_trunc, v->d_cens))
		v->dCensNonrobase = cols(v->cens_nonrobase = selectindex(NonbaseCases :& (v->TheseInds:>`cmp_cont' :& v->TheseInds:<. :& (v->TheseInds:<`mprobit_ind_base' :| v->TheseInds:>=`roprobit_ind_base'))))

		if (v->d_cens)
			v->d_two_cens = cols(v->two_cens = selectindex((v->TheseInds:==`cmp_oprobit' :| v->TheseInds:==`cmp_int' :| (v->TheseInds:==`cmp_left' :| v->TheseInds:==`cmp_right' :| v->TheseInds:==`cmp_probit' :| v->TheseInds:==`cmp_probity1') :& trunceqs)[v->cens])) //indexes *within* list of censored eqs of doubly censored ones
		else
			v->d_two_cens = 0

		v->y = v->Lt = v->Ut = v->yL = smatrix(d)
		for (i=d; i; i--) {
			v->y[i].M = y[i].M[v->SubsampleInds]
			if (trunceqs[i]) {
				v->Lt[i].M = Lt[i].M[v->SubsampleInds]
				v->Ut[i].M = Ut[i].M[v->SubsampleInds]
			}
			if (intregeqs[i])
				v->yL[i].M = yL[i].M[v->SubsampleInds]
		}

		if (v->d_cens > 2) {
			v->GHKStart = ghk_nobs + 1
			ghk_nobs = ghk_nobs + v->N
		}
		if (v->d_uncens) v->EUncens =  J(v->N, v->d_uncens, 0)
		if (v->d_cens)   v->pECens  = &J(v->N, v->d_cens  , 0)
		if (NumCuts | sum(intregeqs) | sum(trunceqs)) {
			v->pF = &J(v->N, v->dCensNonrobase, .)
			if (sum(trunceqs)) {
				v->pEt = &J(v->N, v->d_trunc, .)
				v->pFt = &J(v->N, v->d_trunc, .)
      }
		}

		if (v->d_frac) {
			v->FracCombs = 2*mod(floor(J(1,v->d_frac,0::2^v->d_frac-1):/2:^(v->d_frac-1..0)),2):-1 // matrix whose rows count from 0 to 2^v->d_frac-1 in +/-1 binary, one digit/column
			v->yProd = v->frac_QE = v->frac_QSig = smatrix(v->NFracCombs = rows(v->FracCombs))
			for (i=v->NFracCombs; i; i--) {
				if (i < v->NFracCombs) {
				 (v->frac_QE[i].M = I(v->d_cens))[v->frac,v->frac] = diag(v->FracCombs[i,])
				 v->frac_QSig[i].M = QE2QSig(v->frac_QE[i].M)
				}

				v->yProd[i].M = J(v->N, 1, 1)
				for (j=v->d_frac; j; j--) // make all the combinations of products of frac prob y's and 1-y's
					v->yProd[i].M = v->yProd[i].M :* (v->FracCombs[i,j]==1? y[v->cens[v->frac[j]]].M[v->SubsampleInds] : 1:-y[v->cens[v->frac[j]]].M[v->SubsampleInds])
			}
		} else
			v->NFracCombs = 1

		v->dPhi_dpE = v->dPhi_dpSig = smatrix(2^v->d_frac)

		if (d_oprobit) {
			l = 1
			if (v->oprobit[1]>1) l = l + colsum(vNumCuts[1::v->oprobit[1]-1])
			v->CutInds = l .. l+vNumCuts[v->oprobit[1]]-1
			for (k=2; k<=d_oprobit; k++) {
				l = l + colsum(vNumCuts[v->oprobit[k-1]::v->oprobit[k]-1])
				v->CutInds = v->CutInds, l .. l+vNumCuts[v->oprobit[k]]-1
			}
			v->vNumCuts = vNumCuts[v->oprobit]

			v->NumCuts = cols(v->CutInds)
		} else
			v->NumCuts = 0

		v->mprobit = mprobit_group(rows(MprobitGroupInds))
		for (k=rows(MprobitGroupInds); k; k--) {
			start = MprobitGroupInds[k, 1]; stop = MprobitGroupInds[k, 2]
			v->mprobit[k].d = d_mprobit = (v->TheseInds[start]<.) * (cols( mprobit = selectindex(v->TheseInds :& one2d:>=start :& one2d:<=stop) ) - 1)
			if (d_mprobit > 0) {
				v->mprobit[k].out = v->TheseInds[start] - `mprobit_ind_base' // eq of chosen alternative
				v->mprobit[k].res = selectindex((v->TheseInds :& one2d:>start  :& one2d:<=stop)[v->cens]) // index in v->ECens for relative differencing results
				v->mprobit[k].in =  selectindex( v->TheseInds :& one2d:>=start :& one2d:<=stop :& one2d:!=v->mprobit[k].out) // eqs of rejected alternatives
				(v->QE)[mprobit,mprobit] = J(d_mprobit+1, 1, 0), insert(-I(d_mprobit), v->mprobit[k].out-start+1-sum(!v->TheseInds[|start\v->mprobit[k].out|]), J(1, d_mprobit, 1))
			}
		}

		v->N_perm = 1
		if (NumRoprobitGroups) {
			pointer (real rowvector) colvector roprobit
			real rowvector this_roprobit
			pointer (real matrix) colvector perms
			pointer (real matrix) scalar ThesePerms
			real scalar ThisPerm

			perms = roprobit = J(NumRoprobitGroups, 1, NULL)
			v->d2_cens = v->d_cens * (v->d_cens + 1)*.5

			for (k=NumRoprobitGroups; k; k--)
				if (cols(this_roprobit=*(roprobit[k] = &selectindex(v->TheseInds :& one2d:>=RoprobitGroupInds[k,1] :& one2d:<=RoprobitGroupInds[k,2]))))
					v->N_perm = v->N_perm * (rows(*(perms[k] = &PermuteTies(reverse? v->TheseInds[this_roprobit] : -v->TheseInds[this_roprobit]))))

			v->roprobit_QE = v->roprobit_Q_Sig = J(i=v->N_perm, 1, NULL)
			for (; i; i--) { // combinations of perms across multiple roprobit groups
				j = i - 1
				t = I(d)
				for (k = NumRoprobitGroups; k; k--)
					if (d_roprobit = cols(this_roprobit = *roprobit[k])) {
						ThisPerm = mod(j, rows(*(ThesePerms=perms[k]))) + 1
						t[this_roprobit, this_roprobit] =
							J(d_roprobit, 1, 0), (I(d_roprobit)[,(*ThesePerms)[|ThisPerm, 2 \ ThisPerm, .           |]] -
																		I(d_roprobit)[,(*ThesePerms)[|ThisPerm, 1 \ ThisPerm, d_roprobit-1|]] )
						j = (j - ThisPerm + 1) / rows(*ThesePerms)
					}
				(v->roprobit_Q_Sig)[i] = &QE2QSig(*((v->roprobit_QE)[i] = &t[v->cens, v->cens_nonrobase]))
			}
		}

    v->NotBaseEq = v->TheseInds :< `mprobit_ind_base' :| v->TheseInds :>= `roprobit_ind_base'

		if (v->d_trunc) {
			v->one2d_trunc = 1..v->d_trunc
			v->SigIndsTrunc = vSigInds(v->trunc, d)

			if (v->d_trunc > 2) {
				v->GHKStartTrunc = ghk_nobs + 1
				ghk_nobs = ghk_nobs + v->N
			}
		}

		if (_todo) {
			v->XU = ssmatrix(L-1)
			for (l=L-1; l; l--)
				v->XU[l].M = smatrix(rows((*REs)[l].pXU), cols((*REs)[l].pXU))
		}

		if (_todo) { // pre-compute stuff for scores
			v->Scores = scorescol(L)
      sTScores=smatrix(L); sGammaScores=smatrix(sum(G))
			for (l=L; l; l--) {
				v->Scores[l].M = scores(NumREDraws[l])
				for (r=NumREDraws[l]; r; r--) {
					v->Scores[l].M[r].GammaScores = sGammaScores
					v->Scores[l].M[r].TScores = sTScores  // last entry holds scores of base-level Sig parameters not T
				}
			}
			v->Scores.M.SigScores = smatrix(L)
			v->id =  smatrix(L-1)
			for (l=L-1; l; l--)
				v->id[l].M = (*REs)[l].id[v->SubsampleInds,]

			if (rows(WeightProduct)) v->WeightProduct = WeightProduct[v->SubsampleInds,]

			for (l=L-1; l; l--)
				for (r=NumREDraws[L]; r; r--)
					v->Scores[L].M[r].TScores[l].M = J(v->N, (*REs)[l].d2, 0)

			v->J_N_1_0 = J(v->N, 1, 0)

			v->dOmega_dGamma = smatrix(d,d)

			v->SigIndsUncens = vSigInds(v->uncens, d)
			v->cens_uncens = v->cens, v->uncens
			v->J_d_uncens_d_cens_0 = J(v->d_uncens, v->d_cens, 0)
			v->J_d_cens_d_0 = J(v->d_cens, d, 0)
			v->J_d2_cens_d2_0 = J(v->d_cens*(v->d_cens+1)*0.5, d2, 0)

			if (v->d_uncens) {
				v->dphi_dE = J(v->N, d, 0)
				v->dphi_dSig = J(v->N, d2, 0)
				v->EDE = J(v->N, v->d_uncens*(v->d_uncens+1)*.5, 0)
			} else
				v->dPhi_dE = J(v->N, d, 0)

			if (v->d_two_cens | v->d_trunc) {
				v->dPhi_dpF = J(v->N, v->dCensNonrobase, 0)
				if (v->d_uncens==0)
					v->dPhi_dF = J(v->N, d, 0)
				if (v->d_trunc) {
					v->dPhi_dEt = J(v->N, d,  0)
					v->dPhi_dSigt = J(v->N, d2, 0)
				}
			}

			if (v->d_cens & v->d_uncens==0)
				v->dPhi_dSig = J(v->N, d2, 0)
			if (v->d_cens & v->d_uncens) {
				v->dPhi_dpE_dSig = J(v->N, d2, 0)
				v->_dPhi_dpE_dSig = J(v->N, (v->d_cens+v->d_uncens)*(v->d_cens+v->d_uncens+1)*.5, 0)
			}
			if (v->d_two_cens & v->d_uncens) {
				v->dPhi_dpF_dSig = J(v->N, d2, 0)
				v->_dPhi_dpF_dSig = J(v->N, (v->d_cens+v->d_uncens)*(v->d_cens+v->d_uncens+1)*.5, 0)
			}
			if (NumCuts)
				v->dPhi_dcuts = J(v->N, NumCuts, 0)

			if (v->d_cens)
				v->CensLTInds = vech(colshape(1..v->d_cens*v->d_cens, v->d_cens)')

			if (d_oprobit) {
				varnames = ""
				for (k=1; k<=d_oprobit; k++) {
					stata("unab yis: _cmp_y" + strofreal(v->oprobit[k]) + "_*")
					varnames = varnames + " " + st_local("yis")
				}
				_st_view(Yi, ., tokens(varnames))
				st_select(v->Yi, Yi, v->subsample)
			}

			v->QSig = QE2QSig(v->QE)'
			v->SigIndsCensUncens = vSigInds(v->cens_uncens, d)
			v->dSig_dLTSig = Dmatrix(v->d_cens + v->d_uncens)
		}
	}
	subviews = v

	if (_todo)
		for (l=L-1;l;l--)
			BuildXU(l)

  if (ghk_nobs)
    if (ghkDraws < .) {
      // by default, make # draws at least sqrt(N) (Cappellari and Jenkins 2003)
      if (ghkDraws == 0) ghkDraws = ceil(2 * sqrt(ghk_nobs+1))

      printf("{res}Likelihoods for %f observations involve cumulative normal distributions above dimension 2.\n", ghk_nobs)
      printf(`"Using {stata "help ghk2" :ghk2()} to simulate them. Settings:\n"')
      printf("    Sequence type = %s\n", ghkType)
      printf("    Number of draws per observation = %f\n", ghkDraws)
      printf("    Include antithetic draws = %s\n", ghkAnti? "yes" : "no")
      printf("    Scramble = %s\n", ("no", "square root", "negative square root", "Faure-Lemieux")[1+ghkScramble])
      printf("    Prime bases = %s\n", invtokens(strofreal(Primes[PrimeIndex..PrimeIndex-2+d_ghk])))
      if (ghkType=="random" | ghkType=="ghalton")
        printf(`"    Initial {stata "help mf_uniform" :seed string} = %s\n"', uniformseed())
      printf(`"Each observation gets different draws, so changing the order of observations in the data set would change the results.\n\n"')

      ghk2DrawSet = ghk2setup(ghk_nobs, ghkDraws, d_ghk, ghkType, PrimeIndex, (NULL, &ghk2SqrtScrambler(), &ghk2NegSqrtScrambler(), &ghk2FLScrambler())[1+ghkScramble])
    } else
      ghk2DrawSet = .

	if ((ghk_nobs & ghkDraws<. & (ghkType=="random" | ghkType=="ghalton")) | (L>1 & (REType=="random" | REType=="ghalton")))
		printf("Starting seed for random number generator = %s\n", st_strscalar("c(seed)"))

  return(0)
}


void cmp_model::SaveSomeResults() {
	pointer (struct RE scalar) scalar RE; real scalar L, l, j, k_aux_nongamma; real matrix means, ses; string matrix colstripe, _colstripe

	st_matrix("e(MprobitGroupEqs)", MprobitGroupInds)
	st_matrix("e(ROprobitGroupEqs)", RoprobitGroupInds)

	if ((L =st_numscalar("e(L)")) == 1)
		st_matrix("e(Sigma)", REs->Sig)
	else {
		for (l=L; l; l--) {
			RE = &((*REs)[l])
			st_matrix("e(Sigma"+(l<L?strofreal(l):"")+")", RE->Sig)
			if (l<L & Quadrature & (AdaptivePhaseThisEst | Adapted)) { // means and ses don't exist if iter() option stopped search before adaptive phase
				ses = means = J(RE->N, RE->d, 0)
				for (j=RE->N; j; j--) {
					means[j,] = RE->QuadMean[j].M
					ses  [j,] = RE->QuadSD[j].M'
				}
				st_matrix("e(REmeans"+strofreal(l)+")", means * RE->T)
				st_matrix("e(RESEs"  +strofreal(l)+")", ses   * RE->T)
				colstripe = tokens(st_global("cmp_rceq"+strofreal(l)))', tokens(st_global("cmp_rc"+strofreal(l)))'
				st_matrixcolstripe("e(REmeans"+strofreal(l)+")", colstripe)
				st_matrixcolstripe("e(RESEs"  +strofreal(l)+")", colstripe)
			}
		}
		if (rows(WeightProduct))
			st_numscalar("e(N)", sum(WeightProduct))
	}

	if (HasGamma) {
		real scalar eq, d, k, NumCoefs, rows_dbr_db, cols_dbr_db, k_gamma
		real matrix Beta, BetaInd, GammaInd, REInd, dBeta_dB, dBeta_dGamma, dbr_db, dOmega_dSig, V, br, sig, rho, Rho, invGamma, Omega, NumEff
		real rowvector eb, p
		real colvector keep
		string rowvector eqnames
		pragma unset p

		colstripe = J(0, 1, ""); _colstripe = J(0, 2, "")
		V = st_matrix("e(V)")
		BetaInd = st_matrix("cmpBetaInd"); GammaInd = st_matrix("cmpGammaInd")
		invGamma = (*REs)[L].invGamma'
		Beta = invGamma * st_matrix(st_local("Beta"))
		d = rows(Beta); k = cols(Beta)
		br = colshape(Beta, 1)
		eb = st_matrix("e(b)")
		k_aux_nongamma = st_numscalar("e(k_aux)") - (k_gamma = st_numscalar("e(k_gamma)"))
		dBeta_dB = invGamma # I(k); dBeta_dGamma = invGamma # Beta'

		dbr_db = J(rows(dBeta_dB), 0, 0)
		for (eq=d; eq; eq--)
			dbr_db = dBeta_dGamma[, GammaInd[selectindex(GammaInd[,2]:==eq),1] :+ (eq-1)*d], dbr_db
		for (eq=d; eq; eq--)
			dbr_db = dBeta_dB    [, BetaInd [selectindex(BetaInd [,2]:==eq),1] :+ (eq-1)*k], dbr_db

		keep = selectindex(rowsum(dbr_db:!=0):>0)
		br = br[keep]'
		dbr_db = dbr_db[keep,]
    rows_dbr_db = rows(dbr_db); cols_dbr_db = cols(dbr_db)

		eqnames = tokens(st_global("cmp_eq"))
		for (eq=d; eq; eq--)
			colstripe = J(k, 1, eqnames[eq]) \ colstripe
		colstripe = (colstripe, J(d, 1, tokens(st_local("xvarsall"))'))[keep,]

		if (NumCuts) {
			br = br, eb[|cols(eb)-k_aux_nongamma+1 \ cols(eb)-k_aux_nongamma+NumCuts|]
			colstripe = colstripe \ st_matrixcolstripe("e(b)")[|cols(eb)-k_aux_nongamma+1, . \ cols(eb)-k_aux_nongamma+NumCuts,.|]
			dbr_db = blockdiag(dbr_db, I(NumCuts))
		}

		NumEff = J(0, d, 0)
		for (l=1; l<=L; l++) {
			RE = &((*REs)[l])
			REInd = st_matrix("cmpREInd"+strofreal(l))
			k = colmax(REInd[,2])
			dOmega_dSig = (invGamma # I(k))[, (REInd[,1]:-1)*k + REInd[,2]]'
			st_matrix("e(Omega"+(l<L?strofreal(l):"")+")", Omega = quadcross(dOmega_dSig, RE->Sig) * dOmega_dSig)
			Rho = corr(Omega); rho = rows(Rho)>1? vech(Rho[|2,1 \ .,cols(Rho)-1|])' : J(1,0,0)
			sig = sqrt(diagonal(Omega))'
			dOmega_dSig = edittozero(pinv(editmissing(dSigdsigrhos(SigXform, sig, Omega, rho, Rho),0)),10) * QE2QSig(dOmega_dSig) * dSigdsigrhos(SigXform, RE->sig, RE->Sig, RE->rho, Rho) * RE->dSigdParams
			keep = selectindex((((sig:!=.) :* (sig:>0)), (rho:!=.)) :* (rowsum(dOmega_dSig:!=0):>0)')'
			br = br, (SigXform? ln(sig), atanh(rho) : sig, rho)[keep]
			_colstripe = _colstripe \ ((tokens(st_local("sigparams"+strofreal(l)))' \ tokens(st_local("rhoparams"+strofreal(l)))')[keep] , J(rows(keep), 1, "_cons"))
			dbr_db = blockdiag(dbr_db, dOmega_dSig[keep,])

			if (RE->NSigParams) {
        keep = colshape(rowsum(dOmega_dSig[|.,.\k*d,.|]:!=0):>0, k) // get retained sig params by eq
        NumEff = NumEff \ rowsum(keep)'
        for (j=d; j; j--)
          st_global("e(EffNames_reducedform"+strofreal(l)+"_"+strofreal(j)+")", invtokens(tokens(st_local("cmp_rcu"+strofreal(l)))[selectindex(keep[j,])]))
      } else
        NumEff = NumEff \ J(1,d,0)

			st_matrix("e(fixed_sigs_reducedform"+strofreal(l)+")", J(1, d, .))
			st_matrix("e(fixed_rhos_reducedform"+strofreal(l)+")", J(d, d, .))
		}
		st_matrix("e(NumEff_reducedform)", NumEff)
		st_numscalar("e(k_sigrho_reducedform)", rows(_colstripe))
		colstripe = colstripe \ _colstripe

		NumCoefs = rows(BetaInd) - 1
		BetaInd  = runningsum(colsum( BetaInd[|2,2\.,.|]#J(1,d,1) :== (1..d))')
		GammaInd = runningsum(colsum(GammaInd[|2,2\.,.|]#J(1,d,1) :== (1..d))') :+ NumCoefs
		BetaInd   = (0        \  BetaInd[|.\d-1|]):+1,  BetaInd
		GammaInd  = (NumCoefs \ GammaInd[|.\d-1|]):+1, GammaInd
		for (eq=1; eq<=d; eq++) {
			if (GammaInd[eq,2] >= GammaInd[eq,1]) p = p, GammaInd[eq,1]..GammaInd[eq,2]
			if ( BetaInd[eq,2] >=  BetaInd[eq,1]) p = p,  BetaInd[eq,1].. BetaInd[eq,2]
		}
		if (cols(p)<cols(eb))
			p = p, cols(p)+1 .. cols(eb)

		st_matrix("e(br)", br)
		st_matrix("e(Vr)", dbr_db * V * dbr_db')
		st_matrix("e(_p)", p)
		st_matrixcolstripe("e(br)", colstripe)
		st_matrixcolstripe("e(Vr)", colstripe)
		st_matrixrowstripe("e(Vr)", colstripe)
    st_matrix("e(invGamma)", (*REs)[L].invGamma)
    st_matrix("e(dbr_db)", k_aux_nongamma? blockdiag(blockdiag(invGamma, J(0, k_gamma, 0)), dbr_db[|rows_dbr_db+1, cols_dbr_db+1 \ .,.|]) : invGamma)
	}
}

mata mlib create lcmp, dir("`c(sysdir_plus)'l") replace
mata mlib add lcmp *(), dir("`c(sysdir_plus)'l")
mata mlib index
end