# User visible changes in tmvtnorm package ## changes in tmvtnorm 1.6 (2023-12-05) * Changed package encoding from 'latin1' to 'UTF-8'. * Converted the non-ASCII content to ASCII. * Fixed CITATION file ## changes in tmvtnorm 1.5 (2022-03-22) * fixed misleading stop message to "lower bound should be strictly less than the upper bound". Reported by Chao Wang [chao-wang@uiowa.edu] * Added README.md * Fixed two warnings/errors for R 4.2.0 in `tmvtnorm::rtmvnorm` input checks ``` 1: In !is.null(H) && sigma != diag(length(mean)) : 'length(x) = 9 > 1' in coercion to 'logical(1)' 2: In start.value < lower || start.value > upper : 'length(x) = 3 > 1' in coercion to 'logical(1)' ``` ## changes in tmvtnorm 1.4-10 (2015-08-24) * Fixed problem with build process in src/Makevars (parallel make) ## changes in tmvtnorm 1.4-9 (2014-03-03) * Moved package vignette to vignettes/ directory to be consistent with R 3.1.0 ## changes in tmvtnorm 1.4-8 (2013-03-29) * bugfix in dtmvnorm(...,margin=NULL). Introduced in 1.4-7. Reported by Julius.Vainora [julius.vainora@gmail.com] * bugfix in rtmvt(..., algorithm="gibbs"): Algorithm="gibbs" was not forwarded properly to rtmvnorm(). Reported by Aurelien Bechler [aurelien.bechler@agroparistech.fr] * allow non-integer degrees of freedom in rtmvt, e.g. rtmvt(..., df=3.2). Suggested by Aurelien Bechler [aurelien.bechler@agroparistech.fr] Rejection sampling does not work with non-integer df, only Gibbs sampling. ## changes in tmvtnorm 1.4-7 (2012-11-29) * new method rtmvnorm2() for drawing random samples with general linear constraints a <= Dx <= b with x (d x 1), D (r x d), a,b (r x 1) which can also handle the case r > d. Requested by Xiaojin Xu [xiaojinxu.fdu@gmail.com] Currently works with Gibbs sampling. * bugfix in dtmvnorm(...,log=TRUE). Reported by John Merrill [john.merrill@gmail.com] * optimization in mtmvnorm() to speed up the calculations * dtmvnorm.marginal2() can now be used with vectorized xq, xr. ## changes in tmvtnorm 1.4-6 (2012-03-23) * further optimization in mtmvnorm() and implementation of Johnson/Kotz-Formula when only a subset of variables is truncated ## changes in tmvtnorm 1.4-5 (2012-02-13) * rtmvnorm() can be used with both sparse triplet representation and (compressed sparse column) for H * dramatic performance gain in mtmvnorm() through optimization ## changes in tmvtnorm 1.4-4 (2012-01-10) * dramatic performance gain in rtmvnorm.sparseMatrix() through optimization * Bugfix in rtmvnorm() with linear constraints D: (reported by Claudia Köllmann [koellmann@statistik.tu-dortmund.de]) - forwarding "algorithm=" argument from rtmvnorm() to internal methods dealing with linear constraints was corrupt. - sampling with linear constraints D lead to wrong results due to missing t() ## changes in tmvtnorm 1.4-2 (2012-01-04) * Bugfix in rtmvnorm.sparseMatrix(): fixed a memory leak in Fortran code * Added a package vignette with a description of the Gibbs sampler ## changes in tmvtnorm 1.4-1 (2011-12-27) * Allow a sparse precision matrix H to be passed to rtmvnorm.sparseMatrix() which allows random number generation in very high dimensions (e.g. d >> 5000) * Rewritten the Fortran version of the Gibbs sampler for the use with sparse precision matrix H. ## changes in tmvtnorm 1.3-1 (2011-12-01) * Allow for the use of a precision matrix H rather than covariance matrix sigma in rtmvnorm() for both rejection and Gibbs sampling. (requested by Miguel Godinho de Matos from Carnegie Mellon University) * Rewritten both the R and Fortran version of the Gibbs sampler. * GMM estimation in gmm.tmvnorm(,method=c("ManjunathWilhelm","Lee")) can now be done using the Manjunath/Wilhelm and Lee moment conditions. ## changes in tmvtnorm 1.2-3 (2011-06-04) * rtmvnorm() works now with general linear constraints a<= Dx<=b, with x (d x 1), full-rank matrix D (d x d), a,b (d x 1). * Implemented with both rejection sampling and Gibbs sampling (Geweke (1991)) * Added GMM estimation in gmm.tmvnorm() * Bugfix in dtmvt() thanks to Jason Kramer: Using type="shifted" in pmvt() (reported by Jason Kramer [jskramer@uci.edu]) ## changes in tmvtnorm 1.1-5 (2010-11-20) * Added Maximum Likelihood estimation method (MLE) mle.tmvtnorm() * optimized mtmvnorm(): precalcuted F_a[i] in a separate loop which improved the computation of the mean, suggested by Miklos.Reiter@sungard.com * added a flag doComputeVariance (default TRUE), so users which are only interested in the mean, can compute only the variance (BTW: this flag does not make sense for the mean, since the mean has to be calculated anyway.) * Fixed a bug with LAPACK and BLAS/FLIBS libraries: Prof. Ripley/Writing R extensions: "For portability, the macros @code{BLAS_LIBS} and @code{FLIBS} should always be included @emph{after} @code{LAPACK_LIBS}." ## changes in tmvtnorm 1.0-2 (2010-01-28) * Added methods for the truncated multivariate t-Distribution : rtmvt(), dtmvt() und ptmvt() and ptmvt.marginal() ## changes in tmvtnorm 0.9-2 (2010-01-03) * Implementation of "thinning technique" for Gibbs sampling: Added parameter thinning=1 to rtmvnorm.gibbs() for thinning of Markov chains, i.e. reducing autocorrelations of random samples * Documenting additional arguments "thinning", "start.value" and "burn.in", for rmvtnorm.gibbs() * Added parameter "burn-in" and "thinning" in the Fortran code for discarding burn-in samples and thinng the Markov chain. * Added parameter log=FALSE to dtmvnorm.marginal() * Added parameter margin=NULL to dtmvnorm() as an interface/wrapper to marginal density functions dtmvnorm.marginal() and dtmvnorm.marginal2() * Code polishing and review