Explain and illustrate that omitting data dimensions produces marginal log-likelihoods in Chapter 7.
Add low-level functionality for reduced rank covariances in new Chapter 8.
invcholD and Dchol avoided very small diagonals;
now they also avoid very large diagonals (such that inverting the result
doesn't give too small diagonals).
Fix partial argument matches, reported by KH.
Challenging numeric test of score function at ML estimate failed on Mac M1.
Add logdet() function for computing log-determinants from
lower triangular matrices.
Speed up solve(<ltMatrices>, ...).
Avoid unnecessary copying of large data objects.
-1/2 -> -1 for det(C) in lmvnorm_src vignette; spotted by Kurt Hornik
Use M\_PI.
Update the Using mvtnorm package vignette and references therein.
Speed up Mult(<ltMatrices>, transpose = TRUE).
Speed up ldmvnorm().
Add constructor syMatrices() for multiple symmetric
matrices and as.syMatrices() for coercion from ltMatrices.
Fix segmentation fault or unnecessary error or warning + approximation for
algorithm = Miwa() or TVPACK(), in case dimension
reduction to one-dimensional is possible, e.g., for
pmvnorm(lower = rep(-Inf,3), upper = c(-1, Inf, Inf),
sigma = diag(3), algorithm = Miwa())
# or
pmvnorm(lower = c(-Inf,-Inf),
upper = c(- 1, Inf), sigma=diag(2), algorithm = TVPACK())
Remove empty print statement from miwa.c
Allow to change rnorm in rmvnorm, feature request
by Ralf Stubner.
Fix variable declarations in tvpack.f as reported by Intel
compilers icx/ipcx/ifx from oneAPI 2023.2.0 and oneMKL 2023.2.0, thanks to BDR
Fix overflow problem reported by ASAN.
Be even more careful inverting / computing Cholesky factors for hessians (M1 and macos-arm64).
Avoid attempts to allocate zero length arrays for one-dimensional problems.
Catch M1mac problems with inverting hessians.
New lpmvnorm() function for computing multivariate normal log-likelihoods for interval-censored observations.
New slpmvnorm() function for computing the corresponding score function for interval-censored observations.
New ldmvnorm() function for computing multivariate normal log-likelihoods for exact observations.
New sldmvnorm() function for computing the corresponding score function for exact observations.
New ldpmvnorm() function for computing multivariate normal log-likelihoods for a mix of exact and interval-censored observations.
New sldpmvnorm() function for computing the corresponding score function for a mix of exact and interval-censored observations.
New class ltMatrices representing multiple lower triangular matrices, with some useful methods.
New package vignette lmvnorm_src describing these new features.
pmvnorm(), qmvnorm(), pmvt(), and
qmvt() gain a seed argument.
Regression tests were improved.
Internal standardization is described better in the docs, suggested by Chris Wymant.
New NEWS file, the old file containing information up to version 1.1-3 is available as NEWS.old.