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/dports/science/R-cran-bayesm/bayesm/src/
H A Dghkvec_rcpp.cpp4 vec HaltonSeq(int pn, int r, int burnin, bool rand){ in HaltonSeq() argument
24 vec seq = zeros<vec>(r+burnin+1); in HaltonSeq()
35 if ((t+2)*index-1>r+burnin){ in HaltonSeq()
36 seq(span((t+1)*index, r+burnin)) = add(span(0, r+burnin-(t+1)*index)); in HaltonSeq()
40 if ((t+2)*index==r+burnin+1){ in HaltonSeq()
51 seq = seq(span(burnin+1,burnin+r)); in HaltonSeq()
78 …(vec const& L, vec const& trunpt, vec const& above, int r, bool HALTON, vec const& pn, int burnin){ in ghk_oneside() argument
98 udrawHalton(j, span::all) = trans(HaltonSeq(pn[j], r, burnin, TRUE)); in ghk_oneside()
189 int burnin = 0; in ghkvec() local
193 …res[i] = ghk_oneside(vectorise(L), trunpt(span(i*dim, (i+1)*dim-1)), above, r, HALTON, pn, burnin); in ghkvec()
/dports/math/R-cran-MCMCpack/MCMCpack/src/
H A DMCMCpack_init.c9 …ouble *Ydata, const int *Yrow, const int *Ycol, const int *m, const int *burnin, const int *mcmc, …
15 …*Ycol, const double *Xdata, const int *Xrow, const int *Xcol, const int *burnin, const int *mcmc, …
20 …ouble *Xdata, const int *Xrow, const int *Xcol, const int *m, const int *burnin, const int *mcmc, …
22 …ouble *Ydata, const int *Yrow, const int *Ycol, const int *m, const int *burnin, const int *mcmc, …
25 …t* jlabelsunique, const int* n, const int* ni, const int* nj, const int* burnin, const int* mcmc, …
35 …*Ycol, const double *Xdata, const int *Xrow, const int *Xcol, const int *burnin, const int *mcmc, …
38 …*Ycol, const double *Xdata, const int *Xrow, const int *Xcol, const int *burnin, const int *mcmc, …
39 …uble *resvecdata, const int *resvecrow, const int *resveccol, const int *burnin, const int *mcmc, …
40 …*Ycol, const double *Xdata, const int *Xrow, const int *Xcol, const int *burnin, const int *mcmc, …
41 …w, const int *Xcol, const double *below, const double *above, const int *burnin, const int *mcmc, …
[all …]
H A DcMCMCquantreg.cc63 unsigned int burnin, unsigned int mcmc, unsigned int thin, in MCMCquantreg_impl() argument
68 const unsigned int tot_iter = burnin + mcmc; //total iterations in MCMCquantreg_impl()
84 if (iter >= burnin && (iter % thin == 0)) { in MCMCquantreg_impl()
109 const int *Xcol, const int *burnin, const int *mcmc, in cMCMCquantreg() argument
127 *burnin, *mcmc, *thin, *verbose, in cMCMCquantreg()
/dports/science/R-cran-bayesm/bayesm/R/
H A Dsummary.bayesm.nmix.R1 summary.bayesm.nmix=function(object,names,burnin=trunc(.1*nrow(probdraw)),...){ argument
15 if(burnin > R) {cat("burnin set larger than number of draws submitted (chk keep) \n");
21 for(i in (burnin+1):R){
30 summary(mumat,names,burnin=burnin,QUANTILES=FALSE,TRAILER=FALSE)
32 summary(sigmat,burnin=burnin)
H A Dsummary.bayesm.var.R1 summary.bayesm.var=function(object,names,burnin=trunc(.1*nrow(Vard)),tvalues,QUANTILES=FALSE,...){ argument
12 if(burnin > nrow(Vard)) {cat("burnin set larger than number of draws submitted (chk keep) \n");
15 corrd=t(apply(Vard[(burnin+1):nrow(Vard),],1,nmat))
19 var=Vard[(burnin+1):nrow(Vard),indexdiag]
37 summary(uppertri,names=plabels,burnin=burnin,tvalues=tvalues,QUANTILES=QUANTILES)
H A Dplot.bayesm.mat.R1 plot.bayesm.mat=function(x,names,burnin=trunc(.1*nrow(X)),tvalues,TRACEPLOT=TRUE,DEN=TRUE,INT=TRUE, argument
17 if(burnin > nrow(X)) {cat("burnin set larger than number of draws submitted (chk keep) \n");
34 …hist(X[(burnin+1):nrow(X),index],xlab="",ylab=ylabtxt,main=names[index],freq=!DEN,col="magenta",..…
37 quants=quantile(X[(burnin+1):nrow(X),index],prob=c(.025,.975))
38 mean=mean(X[(burnin+1):nrow(X),index])
39 semean=numEff(X[(burnin+1):nrow(X),index])$stderr
/dports/math/R-cran-MCMCpack/MCMCpack/R/
H A DMCMCprobitChange.R177 burnin = 10000, mcmc = 10000, thin = 1, verbose = 0, argument
193 check.mcmc.parameters(burnin, mcmc, thin)
194 totiter <- mcmc + burnin
217 output <- MCMCprobit(formula=Y~X-1, burnin = burnin, mcmc = mcmc,
224 output <- MCMCprobit(formula=Y~X-1, burnin = burnin, mcmc = mcmc,
247 burnin = as.integer(burnin),
283 output <- mcmc(data=beta.holder, start=burnin+1, end=burnin + mcmc, thin=thin)
H A DMCMCregressChange.R224 mcmc = 1000, burnin = 1000, thin = 1, verbose = 0, argument
238 check.mcmc.parameters(burnin, mcmc, thin)
239 totiter <- mcmc + burnin
271 output <- MCMCregress(formula, burnin = burnin, mcmc = mcmc,
283 … burnin = burnin, mcmc = mcmc, thin = thin, verbose = verbose,
315 burnin = as.integer(burnin),
362 output1 <- mcmc(data=beta.holder, start=burnin+1, end=burnin + mcmc, thin=thin)
367 output2 <- mcmc(data=Sigma.holder, start=burnin+1, end=burnin + mcmc, thin=thin)
H A DMCMCresidualBreakAnalysis.R165 mcmc = 1000, burnin = 1000, thin = 1, verbose = 0, argument
175 check.mcmc.parameters(burnin, mcmc, thin)
176 totiter <- mcmc + burnin
197 output <- MCMCregress(y~1, mcmc=mcmc, burnin=burnin, verbose=verbose, thin=thin,
224 burnin = as.integer(burnin),
270 output1 <- mcmc(data=beta.holder, start=burnin+1, end=burnin + mcmc, thin=thin)
275 output2 <- mcmc(data=Sigma.holder, start=burnin+1, end=burnin + mcmc, thin=thin)
H A DMCMCbinaryChange.R146 burnin = 10000, mcmc = 10000, thin = 1, verbose = 0, argument
151 check.mcmc.parameters(burnin, mcmc, thin)
152 totiter <- mcmc + burnin
182 output <- MCMCprobit(y~1, burnin = burnin, mcmc = mcmc,
204 burnin = as.integer(burnin),
232 output <- mcmc(data=phi.holder, start=burnin+1, end=burnin + mcmc, thin=thin)
H A DMCMCpoissonChange.R206 burnin = 1000, mcmc = 1000, thin = 1, verbose = 0, argument
223 check.mcmc.parameters(burnin, mcmc, thin)
224 totiter <- mcmc + burnin
264 output <- MCMCpoisson(formula, burnin = burnin, mcmc = mcmc,
270 output <- MCMCpoisson(formula, burnin = burnin, mcmc = mcmc,
317 burnin = as.integer(burnin),
361 output <- mcmc(data=beta.holder, start=burnin+1, end=burnin + mcmc, thin=thin)
H A DMCMChierEI.R178 function(r0, r1, c0, c1, burnin=5000, mcmc=50000, thin=1, argument
221 check.mcmc.parameters(burnin, mcmc, thin)
274 burnin = as.integer(burnin),
295 output <- mcmc(data=sample, start=burnin+1, end=burnin+mcmc, thin=thin)
H A DMCMCdynamicEI.R221 function(r0, r1, c0, c1, burnin=5000, mcmc=50000, argument
262 check.mcmc.parameters(burnin, mcmc, thin)
314 burnin = as.integer(burnin),
331 output <- mcmc(data=sample, start=(burnin+1), end=burnin+mcmc, thin=thin)
H A DMCMChierBetaBinom.R23 "MCMChierBetaBinom" <- function(y, s, i.labels, j.labels, burnin=1000, argument
30 check.mcmc.parameters(burnin, mcmc, thin)
156 burnin = as.integer(burnin),
173 output <- mcmc(data=sample, start=burnin+1, end=burnin+mcmc, thin=thin)
182 attr(output, "acceptance.rates") <- posterior$accepts / (posterior$mcmc + posterior$burnin)
H A DMCMCnegbinChange.R218 burnin = 1000, mcmc = 1000, thin = 1, verbose = 0, argument
236 check.mcmc.parameters(burnin, mcmc, thin)
237 totiter <- mcmc + burnin
261 output <- MCMCnegbin(formula, data = data, burnin = burnin, mcmc = mcmc,
326 burnin = as.integer(burnin),
374 output <- mcmc(data=beta.holder, start=burnin+1, end=burnin + mcmc, thin=thin)
H A DMCMClogit.R201 function(formula, data=NULL, burnin = 1000, mcmc = 10000, argument
208 check.mcmc.parameters(burnin, mcmc, thin)
314 burnin=as.integer(burnin),
351 my.env, as.integer(burnin), as.integer(mcmc),
383 output <- mcmc(data=sample, start=burnin+1,
384 end=burnin+mcmc, thin=thin)
H A DHMMpanelRE.R254 mcmc=1000, burnin=1000, thin=1, verbose=0, argument
381 burnin = as.integer(burnin), mcmc = as.integer(mcmc),
424 output1 <- mcmc(data=beta.samp, start=burnin+1, end=burnin + mcmc, thin=thin)
425 output2 <- mcmc(data=sigma.samp, start=burnin+1, end=burnin + mcmc, thin=thin)
426 output3 <- mcmc(data=D.samp, start=burnin+1, end=burnin + mcmc, thin=thin)
H A DMCMCfactanal.R223 data=NULL, burnin = 1000, mcmc = 20000, argument
268 check.mcmc.parameters(burnin, mcmc, thin)
311 sample.nonconst=sample, X=X, burnin=as.integer(burnin),
341 output <- mcmc(as.matrix(output.df), start=burnin+1, end=mcmc+burnin,
H A DMCMCoprobit.R171 function(formula, data = parent.frame(), burnin = 1000, mcmc = 10000, argument
177 check.mcmc.parameters(burnin, mcmc, thin)
190 mf$burnin <- mf$mcmc <- mf$b0 <- mf$B0 <- mf$a0 <- mf$A0 <- NULL
304 burnin=as.integer(burnin),
323 output <- mcmc(data=sample, start=burnin+1, end=burnin+mcmc, thin=thin)
H A DMCMCmetrop1R.R236 burnin=500, mcmc=20000, thin=1, argument
255 check.mcmc.parameters(burnin, mcmc, thin)
349 my.env, as.integer(burnin), as.integer(mcmc),
360 sample <- mcmc(data=sample, start=burnin+1, end=burnin+mcmc, thin=thin)
H A DMCMCpaircompare2d.R322 burnin=1000, mcmc=20000, thin=1, argument
333 check.mcmc.parameters(burnin, mcmc, thin)
510 burnin = as.integer(burnin),
550 output <- mcmc(data=sample, start=burnin+1, end=burnin+mcmc, thin=thin)
584 start=burnin+1, end=burnin+mcmc, thin=thin)
/dports/biology/iqtree/IQ-TREE-2.0.6/pda/
H A Decopdmtreeset.cpp18 int burnin, int max_count, const char *tree_weight_file) { in EcoPDmtreeset() argument
19 initEcoSD(userTreeFile, is_rooted, burnin, max_count, tree_weight_file); in EcoPDmtreeset()
22 void EcoPDmtreeset::initEcoSD(const char *userTreeFile, bool &is_rooted, int burnin, int max_count, in initEcoSD() argument
25 readTrees(userTreeFile, is_rooted, burnin, max_count, weights, compressed); in initEcoSD()
29 readIntVector(tree_weight_file, burnin, max_count, tree_weights); in initEcoSD()
/dports/math/R-cran-spdep/spdep/R/
H A Dmcmcsamp.R4 burnin=0L, scale=1, listw, control=list()) { argument
79 proposal=list(var=V, scale=scale), m=(mcmc+burnin), env=env)
83 res <- as.mcmc(res0$par[(burnin+1):(mcmc+burnin),])
94 burnin=0L, scale=1, listw, listw2=NULL, control=list()) { argument
199 proposal=list(var=V, scale=scale), m=(mcmc+burnin), env=env)
208 res <- as.mcmc(res0$par[(burnin+1):(mcmc+burnin),])
225 proposal=list(var=V, scale=scale), m=(mcmc+burnin), env=env)
234 res <- as.mcmc(res0$par[(burnin+1):(mcmc+burnin),])
249 proposal=list(var=V, scale=scale), m=(mcmc+burnin), env=env)
253 res <- as.mcmc(res0$par[(burnin+1):(mcmc+burnin),])
/dports/math/py-chaospy/chaospy-4.3.3/chaospy/distributions/sampler/sequences/
H A Dhalton.py8 def create_halton_samples(order, dim=1, burnin=-1, primes=()): argument
57 if burnin < 0:
58 burnin = max(primes)
61 indices = [idx+burnin for idx in range(order)]
/dports/science/R-cran-eco/eco/R/
H A DecoCV.R4 n.draws = 5000, burnin = 0, thin = 5, verbose = TRUE){ argument
7 if (burnin >= n.draws)
64 n.a <- floor((n.draws-burnin)/thin)
81 as.integer(n.samp), as.integer(n.draws), as.integer(burnin), as.integer(thin),
102 res.out <- list(model="Normal prior", burnin=burnin, thin = thin, X=X, Y=Y, nameattr

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