Revision 9be3f5a0653739a591e2b30cc9e77900612dad9a authored by Wayne Zhang on 08 November 2011, 00:00:00 UTC, committed by Gabor Csardi on 08 November 2011, 00:00:00 UTC
1 parent 31a5c50
utilities.c
/************************************************************/
/* Utility functions common to cplm programs */
/* Author: Wayne Zhang */
/* actuary_zhang@hotmail.com */
/************************************************************/
/**
* @file utilities.c
* @brief Utility functions common to cplm programs
* @author Wayne Zhang
*/
#include "cplm.h"
/************************************************************/
/* Memory allocation utility functions */
/************************************************************/
/**
* allocate memory for a double vector of size n
*
* @param n length of the desire vector
*
* @return a double pointer
*/
double * dvect(int n){
return (double *)R_alloc(n, sizeof(double));
}
/**
* allocate memory for an int vector of size n
*
* @param n length of the desire vector
*
* @return a int pointer
*/
int * ivect(int n){
return (int *)R_alloc(n, sizeof(int));
}
/**
* allocate memory for a double matrix of nr times nc
*
* @param nr number of rows
* @param nc number of columns
*
* @return a 2d array
*/
double ** dmatrix(int nr, int nc)
{
int i;
double **m;
m = (double **) R_alloc(nr, sizeof(double *));
for (i = 0; i < nr; i++)
m[i] = (double*) R_alloc(nc, sizeof(double));
return m;
}
/**
* allocate memory for an integer matrix of nr times nc
*
* @param nr number of rows
* @param nc number of columns
*
* @return a 2d array
*/
int ** imatrix(int nr, int nc)
{
int i;
int **m;
m = (int **) R_alloc(nr, sizeof(int *));
for (i = 0; i < nr; i++)
m[i] = (int*) R_alloc(nc, sizeof(int));
return m;
}
/************************************************************/
/* Simple arithmetic utility */
/************************************************************/
/**
* cumulative sum of a vector of double
*
* @param x double vector to be summed
* @param n number of elements
*
* @return cumulative sum
*/
double dcumsum(double *x, int n){
int i;
double sm=0 ;
for (i=0;i<n;i++)
sm +=x[i] ;
return sm ;
}
/**
* cumulative sum of a vector of integer
*
* @param x vector to be summed
* @param n number of elements
*
* @return cumulative sum
*/
int icumsum(int *x, int n){
int i;
int sm=0 ;
for (i=0;i<n;i++)
sm +=x[i] ;
return sm ;
}
/**
* weighted cumulative sum of a double vector by weight w
*
* @param x double vector to be summed
* @param w weights
* @param n number of elements
*
* @return cumulative sum
*/
double dcumwsum(double *x, double *w, int n){
int i;
double sm=0 ;
for (i=0;i<n;i++)
sm +=x[i]*w[i] ;
return sm ;
}
/**
* weighted cumulative sum of an int vector by weight w
*
* @param x vector to be summed
* @param w weights
* @param n number of elements
*
* @return cumulative sum
*/
double icumwsum(int *x, double *w, int n){
int i;
double sm=0 ;
for (i=0;i<n;i++)
sm +=x[i]*w[i] ;
return sm ;
}
/**
* take the norm of a vector
*
* @param x a double vector
* @param n length of the vector
*
* @return norm of the vector
*/
double norm (double *x, int n){
int i ;
double nm=0 ;
for (i=0;i<n;i++)
nm += x[i] * x[i] ;
return sqrt(nm) ;
}
/**
* norm distance between two vectors
*
* @param x a double vector
* @param y a double vector
* @param n length of the vector
*
* @return norm distance of the two vectors
*/
double dist (double *x, double *y, int n){
int i ;
double nm=0 ;
for (i=0;i<n;i++)
nm += (x[i] - y[i])*(x[i] - y[i]) ;
return sqrt(nm) ;
}
/**
* get the max value of a double vector
*
* @param x a double vector
* @param n length of the vector
*
* @return max value
*/
double dmax (double *x, int n){
double ans = x[0] ;
if (n>1){
for (int i=1; i<n; i++)
if (x[i]>ans) ans = x[i] ;
}
return ans ;
}
/**
* get the max value of an int vector
*
* @param x an int vector
* @param n length of the vector
*
* @return max value
*/
int imax (int *x, int n){
int ans = x[0] ;
if (n>1){
for (int i=1; i<n; i++)
if (x[i]>ans) ans = x[i] ;
}
return ans ;
}
/************************************************************/
/* Matrix computations */
/************************************************************/
/**
* Multiply a matrix and a vector
*
* @param trans transpose of matrix?
* @param m row count of matrix
* @param n column count of matrix
* @param A input matrix
* @param x input vector
* @param out output vector
*
*/
void mult_mv(char *trans, int m, int n, double *A,
double *x, double *out){
double one = 1, zero = 0 ;
int incx = 1;
F77_CALL(dgemv)(trans, &m, &n, &one, A, &m, x, &incx,
&zero, out, &incx) ;
}
/**
* compute t(x) * x
*
* @param m row dimension of the matrix
* @param n column dimension of the matrix
* @param x the input matrix
* @param out output results
*
*/
void mult_xtx(int m, int n, double *x, double *out){
double alpha=1.0, beta=0,
*x2 = Calloc(m*n,double);
Memcpy(x2, x, m*n) ;
F77_CALL(dgemm)("T", "N", &n, &n, &m, &alpha, x2, &m,
x, &m, &beta, out, &n) ;
Free(x2) ;
}
/**
* compute the lower cholesky factor
*
* @param d dimension of the matrix
* @param v input matrix
* @param iv output cholesky factor
*
*/
void chol(int d, double *v, double *iv){
int info;
// cholesky factor of v
Memcpy(iv, v, d*d) ;
F77_CALL(dpotrf)("L",&d,iv,&d,&info) ;
if (info!=0)
error(_("Error %d in Cholesky decomposition."), info) ;
}
/**
* invert a positive symmetric matrix
*
* @param d dimension of the matrix
* @param v input matrix
* @param iv output inverse of the matrix
*
*/
void solve_po(int d, double *v, double *iv){
int info, i, j;
// cholesky factor of v
chol(d, v, iv) ;
// compute inverse
F77_CALL(dpotri)("L",&d,iv,&d,&info) ;
if (info!=0)
error(_("Error %d in inverting matrix."), info) ;
// fill upper triangle
for (i=0;i<d-1;i++){
for (j=i+1;j<d;j++)
iv[j*d+i] = iv[i*d+j] ;
}
}
/************************************************************/
/* Optimization utility function */
/************************************************************/
/**
* optimation using the lbfgsb algorithm. This is a modification
* of R's function lbfgsb, where memory allocation is changed
* to Calloc and Free
*/
void lbfgsb2(int n, int m, double *x, double *l, double *u, int *nbd,
double *Fmin, optimfn fminfn, optimgr fmingr, int *fail,
void *ex, double factr, double pgtol,
int *fncount, int *grcount, int maxit, char *msg,
int trace, int nREPORT)
{
char task[60];
double f, *g, dsave[29], *wa;
int tr = -1, iter = 0, *iwa, isave[44], lsave[4];
if(n == 0) { /* not handled in setulb */
*fncount = 1;
*grcount = 0;
*Fmin = fminfn(n, u, ex);
strcpy(msg, "NOTHING TO DO");
*fail = 0;
return;
}
if (nREPORT <= 0)
error(_("REPORT must be > 0 (method = \"L-BFGS-B\")"));
switch(trace) {
case 2: tr = 0; break;
case 3: tr = nREPORT; break;
case 4: tr = 99; break;
case 5: tr = 100; break;
case 6: tr = 101; break;
default: tr = -1; break;
}
*fail = 0;
g = Calloc(n, double);
/* this needs to be zeroed for snd in mainlb to be zeroed */
wa = Calloc(2*m*n+4*n+11*m*m+8*m, double);
iwa = Calloc(3*n, int);
strcpy(task, "START");
while(1) {
/* Main workhorse setulb() from ../appl/lbfgsb.c : */
setulb(n, m, x, l, u, nbd, &f, g, factr, &pgtol, wa, iwa, task,
tr, lsave, isave, dsave);
/* Rprintf("in lbfgsb - %s\n", task);*/
if (strncmp(task, "FG", 2) == 0) {
f = fminfn(n, x, ex);
if (!R_FINITE(f))
error(_("L-BFGS-B needs finite values of 'fn'"));
fmingr(n, x, g, ex);
} else if (strncmp(task, "NEW_X", 5) == 0) {
if(trace == 1 && (iter % nREPORT == 0)) {
Rprintf("iter %4d value %f\n", iter, f);
}
if (++iter > maxit) {
*fail = 1;
break;
}
} else if (strncmp(task, "WARN", 4) == 0) {
*fail = 51;
break;
} else if (strncmp(task, "CONV", 4) == 0) {
break;
} else if (strncmp(task, "ERROR", 5) == 0) {
*fail = 52;
break;
} else { /* some other condition that is not supposed to happen */
*fail = 52;
break;
}
}
*Fmin = f;
*fncount = *grcount = isave[33];
if (trace) {
Rprintf("final value %f \n", *Fmin);
if (iter < maxit && *fail == 0) Rprintf("converged\n");
else Rprintf("stopped after %i iterations\n", iter);
}
strcpy(msg, task);
Free(g) ;
Free(wa) ;
Free(iwa) ;
}
/*
* wrapper for the univariate lbfgsb function:
*
* @param x the value of the parameter
* @param lower the lower bound
* @param upper the upper bound
* @param val the value of the function
* @param fn function to be minimized
* @param gr derivative of function
* @param ex struct to be passed to fn and gr
* @param conv converged or not?
*/
void lbfgsbU(double *x, double lower, double upper, double *val,
optimfn fn, optimgr gr, void *ex, int *conv){
// nbd needed for lbfgsb
int nbd ;
if (!R_FINITE(lower)) {
if (!R_FINITE(upper)) nbd = 0; else nbd = 3;
} else {
if (!R_FINITE(upper)) nbd = 1; else nbd = 2;
}
// default the parameters needed for lbfgsb
double factr=1E7, pgtol=0 ;
int fncount, grcount;
int lmm=5, maxit=1000, trace=0, nREPORT=10 ;
char msg[60] ;
lbfgsb2(1,lmm, x, &lower, &upper, &nbd, val, fn, gr,
conv, (void *)ex, factr, pgtol, &fncount, &grcount,
maxit,msg ,trace, nREPORT) ;
}
/**
* extract element from a list
*
* @param list a R list
* @param str the name of the element to be extracted
*
* @return a SEXP pointer to the required list element
*
**/
SEXP getListElement (SEXP list, char *str)
{
SEXP elmt = R_NilValue, names = getAttrib(list, R_NamesSymbol);
int i;
for (i = 0; i < length(list); i++)
if(strcmp(CHAR(STRING_ELT(names, i)), str) == 0) {
elmt = VECTOR_ELT(list, i);
break;
}
return elmt;
}
/************************************************************/
/* cplm intermediate stats update */
/************************************************************/
/**
* compute variance function
*
* @param mu mean
* @param p index parameter
*
* @return variance function
*/
double varFun (double mu, double p){
return pow(mu,p) ;
}
/**
* get linear predictors
*
* @param mu mean vector
* @param link_power link power
*
* @return linear predictors
*/
double linkFun(double mu, double link_power){
if (link_power==0)
return log(mu);
else if (link_power==1)
return mu ;
else if (link_power==2)
return mu * mu ;
else if (link_power==-1)
return 1.0/mu ;
else
return pow(mu,link_power) ;
}
/**
* get mean from linear predictors
*
* @param eta linear predictors
* @param link_power link power
*
* @return mean
*/
double linkInv(double eta, double link_power){
if (link_power==0)
return exp(eta);
else if (link_power==1)
return eta ;
else if (link_power==2)
return sqrt(eta) ;
else if (link_power==-1)
return 1.0/eta ;
else
return pow(eta,1/link_power) ;
}
/**
* derivative d mu / d eta
*
* @param eta linear predictors
* @param link_power link power
*
* @return d(mu)/d(eta)
*/
double mu_eta(double eta, double link_power){
if (link_power==0)
return exp(eta);
else if (link_power==1)
return 1.0 ;
else if (link_power==2)
return 1.0/(sqrt(eta)*2) ;
else if (link_power==-1)
return -1.0/(eta*eta) ;
else
return pow(eta,1/link_power-1)/link_power ;
}
/**
* Compute the linear predictors given the design matrix and beta
*
* @param eta pointer to the vector of linear predictors
* @param nO number of obs
* @param nB number of coefficients
* @param X pointer to the design matrix (in long vector representation)
* @param beta pointer to the vector of coefficients
* @param offset pointer to the vector of offsets
*/
void cpglm_eta(double *eta, int nO, int nB, double *X,
double *beta, double *offset){
// eta = X %*% beta
mult_mv("N", nO, nB, X, beta, eta) ;
// eta = eta + offset
for (int i=0;i<nO;i++)
eta[i] += offset[i] ;
}
/**
* update the expected value and d(mu)/d(eta)
*
* @param mu pointer to the vector of expected values
* @param muEta pointer to the vector of d(mu)/d(eta)
* @param eta pointer to the vector of linear predictors
* @param nO number of observations
* @param link_power the power of the link function
*
*/
void cplm_mu_eta(double *mu, double* muEta, int nO,
double* eta, double link_power){
int i ;
for (i=0;i<nO;i++){
mu[i] = linkInv(eta[i], link_power);
if (muEta != NULL)
muEta[i] = mu_eta(eta[i], link_power) ;
}
}
/**
* update the eta, mu and d(mu)/d(eta) in cpglm
*
* @param eta pointer to the vector of linear predictors
* @param mu pointer to the vector of expected values
* @param muEta pointer to the vector of d(mu)/d(eta)
* @param dap pointer to a da_parm struct
*
*/
void cpglm_fitted(double *eta, double *mu, double *muEta,
da_parm *dap){
cpglm_eta(eta, dap->nO, dap->nB, dap->X, dap->beta,
dap->offset) ;
cplm_mu_eta(mu, muEta, dap->nO, eta, dap->link_power);
}
/**
* update the power variance function
*
* @param var pointer to the vector of variances
* @param mu pointer to the vector of mean values
* @param n number of observations
* @param p the index parameter
*
*/
void cplm_varFun(double* var, double* mu, int n, double p){
int i ;
for (i=0;i<n;i++)
var[i] = varFun(mu[i], p) ;
}
/************************************************************/
/* Compute sample statistics */
/************************************************************/
/**
* Compute sample mean
*
* @param n number of samples
* @param x samples in long vector
*
* @return mean
*/
double mean(int n, double *x){
return dcumsum(x, n)/n ;
}
/**
* Compute sample variance for a univariate variable
*
* @param n number of samples
* @param x samples in long vector
* @param ans pointer to store computed variance
*
*/
void cov1(int n, double *x, double *ans){
int i;
double m = mean(n, x) ;
ans[0] = 0;
for (i=0;i<n;i++)
ans[0] += (x[i]-m)*(x[i]-m);
ans[0] /= n - 1.0 ;
}
/**
* Compute sample covariance matrix
*
* @param n number of samples
* @param p number of variables (columns), p >2
* @param x samples in long vector
* @param ans vector to store computed covariance matrix
*
*/
void cov2(int n, int p, double *x, double *ans){
int i;
double *one = Calloc(n*n, double),
*x2 = Calloc(n*p, double),
*x3 = Calloc(n*p, double);
double alpha = -1.0/n, beta = 1.0, beta2=0;
// subtract mean
for (i=0;i<n*n;i++)
one[i] = 1.0 ;
Memcpy(x2, x, n*p) ;
Memcpy(x3, x, n*p);
F77_CALL(dgemm)("N","N",&n,&p,&n, &alpha, one,
&n, x2, &n, &beta, x3, &n);
Memcpy(x2, x3, n*p) ;
AZERO(ans,p*p) ;
// compute covariance
F77_CALL(dgemm)("T","N",&p,&p,&n, &beta, x2,
&n, x3, &n, &beta2, ans, &p);
for (i=0;i<p*p;i++)
ans[i] /= n-1 ;
Free(one) ;
Free(x2) ;
Free(x3);
}
/**
* Compute sample covariance matrix
*
* @param n number of samples
* @param p number of variables (columns)
* @param x samples in long vector
* @param ans vector to store computed covariance matrix
*
*/
void cov(int n, int p, double *x, double *ans){
if (p==1)
cov1(n, x, ans) ;
else
cov2(n, p, x, ans) ;
}
/************************************************************/
/* Distribution and simulation related utilities */
/************************************************************/
/**
* simulation of multivariate normal
*
* @param d dimension
* @param m mean vector
* @param v positive-definite covarince matrix
* @param s vector to store the simulated values
*
*/
void rmvnorm(int d, double *m, double *v, double *s){
int i, incx=1;
double *lv = Calloc(d*d, double) ;
GetRNGstate() ;
// simulate d univariate normal r.v.s
for (i=0;i<d;i++)
s[i] = rnorm(0,1) ;
PutRNGstate() ;
// cholesky factor of v
chol(d, v, lv) ;
// scale and shift univariate normal r.v.s
F77_CALL(dtrmv)("L","N","N",&d,lv,&d,s,&incx) ;
for (i=0;i<d;i++)
s[i] += m[i] ;
Free(lv) ;
}
/**
* return the exponent of a multivariate normal
*
* @param d dimension of the matrix
* @param x sample vector
* @param m mean vector
* @param iv inverse of the covariance matrix
*
* @return exponent of MVN
*/
double dmvnorm(int d, double *x, double *m, double *iv){
int i ;
double ep=0, *dx = Alloca(d,double), *tmp = Alloca(d,double) ;
R_CheckStack() ;
for (i=0;i<d;i++)
dx[i] = (m==NULL) ? x[i] : x[i] - m[i];
mult_mv("N",d,d,iv,dx,tmp) ;
for (i=0;i<d;i++)
ep += dx[i]*tmp[i] ;
ep *= -0.5 ;
return ep ;
}
/**
* random walk metropolis sampling for a vector of parameters
* of length d using multivariate normal proposal
*
* @param d dimension of the parameter
* @param m current values of the parameter (also mean vector
* in the multivariate Normal)
* @param v covariance matrix in the proposal distribution
* @param sn simulated new vector
* @param myfunc user specified function to compute log posterior
* @param data struct used in myfunc
*
* @return a 0-1 integer: 0 means not accept and 1 accept
*
*/
int metrop_mvnorm_rw(int d, double *m, double *v, double *sn,
double (*myfunc)(double *x, void *data),
void *data){
double A ;
rmvnorm(d, m, v, sn) ;
// determine if accept the sample
A = exp(myfunc(sn,data)-myfunc(m,data) ) ;
if (A<1 && runif(0,1)>=A){
Memcpy(sn, m, d) ;
return 0 ;
}
else return 1 ;
}
/**
* Simulate truncated Normal r.v.s.
*
* @param m mean of the untrucated normal
* @param sd standard deviation of the untrucated normal
* @param lb left bound of the trucated normal
* @param rb right bound of the trucated normal
*
* @return one simulated truncated normal
*/
double cplm_rtnorm(double m, double sd, double lb, double rb){
double u = runif(R_FINITE(lb)? pnorm(lb,m,sd,1,0): 0,
R_FINITE(rb)? pnorm(rb,m,sd,1,0): 1);
return qnorm(u,m,sd,1,0) ;
}
/**
* compute log density of truncated normal
*
* @param x the point at which to compute the log density
* @param m mean of the untrucated normal
* @param sd standard deviation of the untrucated normal
* @param lb left bound of the trucated normal
* @param rb right bound of the trucated normal
*
* @return log density at the point x
*/
double cplm_dtnorm(double x, double m, double sd, double lb, double rb){
double c = R_FINITE(rb)? pnorm(rb,m,sd,1,0): 1
- R_FINITE(lb)? pnorm(lb,m,sd,1,0): 0 ;
return dnorm(x,m,sd,1)- log(c) ;
}
/**
* RW Metropolis update using trucated Normal
*
* @param m mean of the untrucated normal
* @param sd standard deviation of the untrucated normal
* @param lb left bound of the trucated normal
* @param rb right bound of the trucated normal
* @param sn pointer to store simulated value
* @param myfunc user specified function to compute log posterior
* @param data struct used in myfunc
*
* @return a 0-1 integer: 0 means not accept and 1 accept
*/
int metrop_tnorm_rw( double m, double sd, double lb, double rb, double *sn,
double (*myfunc)(double x, void *data),
void *data){
double A ;
*sn = cplm_rtnorm(m, sd, lb, rb) ;
// determine if accept the sample
A = exp(myfunc(*sn,data)-myfunc(m,data)+
cplm_dtnorm(m,*sn,sd,lb,rb)-
cplm_dtnorm(*sn,m,sd,lb,rb)) ;
if (A<1 && runif(0,1)>=A){
*sn = m ;
return 0 ;
}
else return 1 ;
}
/**
* compute log density for tweedie with positive response
*
* @param y response
* @param mu mean
* @param phi scale parameter
* @param p index parameter
*
* @return log density
*/
static double dtweedie2(double y, double mu,
double phi, double p){
double a, a1, logz, drop = 37, jmax, j, cc, wmax, estlogw;
double wm = -1.0E16, sum_ww = 0, *ww, ld;
int k, lo_j, hi_j ;
a= (2-p)/(1-p) ;
a1 = 1 - a ;
logz = -a *log(y) + a*log(p-1)- a1*log(phi)-log(2-p);
jmax = pow(y,2-p)/(phi*(2-p)) ;
jmax = fmax2(1.0, jmax) ;
j = jmax ;
cc = logz + a1 + a *log(-a) ;
wmax = a1 * jmax ;
estlogw = wmax ;
while (estlogw > (wmax - drop)) {
j += 2.0 ;
estlogw = j * (cc - a1 * log(j)) ;
}
hi_j = ceil(j) ;
j = jmax ;
estlogw = wmax ;
while ((estlogw > (wmax - drop)) && (j >= 2)) {
j = fmax2(1, j - 2) ;
estlogw = j * (cc - a1 * log(j)) ;
}
lo_j = imax2(1, floor(j)) ;
ww = Calloc(hi_j-lo_j+1, double) ;
for (k=lo_j;k<hi_j+1;k++){
ww[k-lo_j] = k*logz - lgamma(1+k) - lgamma(-a*k) ;
wm = fmax2(wm, ww[k-lo_j]) ;
}
for (k=lo_j;k<hi_j+1;k++)
sum_ww += exp(ww[k-lo_j]-wm) ;
ld = -y/(phi * (p - 1) * pow(mu,p - 1)) -
(pow(mu, 2 - p)/(phi * (2 - p)))- log(y) +
log(sum_ww) + wm ;
Free(ww) ;
return ld ;
}
/**
* compute log density for tweeide
*
* @param y response
* @param mu mean
* @param phi scale parameter
* @param p index parameter
*
* @return log density
*/
double dtweedie(double y, double mu,
double phi, double p){
return y ? dtweedie2(y,mu, phi,p):
(-pow(mu,2 - p)/(phi * (2 - p))) ;
}
/**
* compute -2 logliklihood of tweedie
*
* @param n number of obs
* @param y vector of response
* @param mu vector of means
* @param phi scale parameter
* @param p index parameter
*
* @return -2 loglik
*/
double dl2tweedie(int n, double *y, double *mu,
double phi, double p){
int i ;
double ans = 0;
for (i=0;i<n;i++)
ans += dtweedie(y[i],mu[i],phi,p) ;
ans *= -2 ;
return ans ;
}
/**
* Simulate the Cholesky factor of a standardized Wishart variate with
* dimension p and nu degrees of freedom.
*
* @param nu degrees of freedom
* @param p dimension of the Wishart distribution
* @param upper if 0 the result is lower triangular, otherwise upper
triangular
* @param ans array of size p * p to hold the result
*
* @return ans
*/
static double *std_rWishart_factor(double nu, int p, int upper, double ans[])
{
int pp1 = p + 1;
if (nu < (double) p || p <= 0)
error("inconsistent degrees of freedom and dimension");
AZERO(ans, p * p);
for (int j = 0; j < p; j++) { /* jth column */
ans[j * pp1] = sqrt(rchisq(nu - (double) j));
for (int i = 0; i < j; i++) {
int uind = i + j * p, /* upper triangle index */
lind = j + i * p; /* lower triangle index */
ans[(upper ? uind : lind)] = norm_rand();
ans[(upper ? lind : uind)] = 0;
}
}
return ans;
}
/**
* Simulate a sample of random matrix from a Wishart distribution
*
* @param d row (=column) dimension of the matrix
* @param nu Degrees of freedom
* @param scal Positive-definite scale matrix
* @param out simulated matrix (d*d)
*
*/
void rwishart(int d, double nu, double *scal, double *out)
{
int info, psqr;
double *scCp, *tmp, one = 1, zero = 0;
psqr = d*d;
tmp = Calloc(psqr, double);
scCp = Calloc(psqr, double);
Memcpy(scCp, scal, psqr);
AZERO(tmp, psqr);
F77_CALL(dpotrf)("U", &d, scCp, &d, &info);
if (info)
error(_("scal matrix is not positive-definite"));
GetRNGstate();
std_rWishart_factor(nu, d, 1, tmp);
F77_CALL(dtrmm)("R", "U", "N", "N", &d, &d,
&one, scCp, &d, tmp, &d);
F77_CALL(dsyrk)("U", "T", &d, &d, &one, tmp, &d,
&zero, out, &d);
for (int i = 1; i < d; i++){
for (int k = 0; k < i; k++)
out[i + k * d] = out[k + i * d];
}
PutRNGstate();
Free(tmp) ;
Free(scCp) ;
}
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