Package: glt 1.0

glt: Bayesian Factor Modelling via Generalized Lower Triangular Priors

Sparse Bayesian factor models for multivariate data, built around a flexible Generalized Lower Triangular (GLT) process prior on the loading matrix by Canale and Frühwirth-Schnatter (2026) <doi:10.48550/arXiv.2610.09762>. The number of active factors, their sparsity pattern, and the loadings themselves are learned jointly via a Markov chain Monte Carlo sampler implemented in C++ with 'Rcpp' and 'RcppArmadillo'.

Authors:Antonio Canale [aut, cre]

glt_1.0.tar.gz
glt_1.0.zip(r-4.7-x86_64)glt_1.0.zip(r-4.7-arm64)glt_1.0.zip(r-4.6-x86_64)glt_1.0.zip(r-4.6-arm64)glt_1.0.zip(r-4.5-x86_64)
glt_1.0.tgz(r-4.6-x86_64)glt_1.0.tgz(r-4.6-arm64)glt_1.0.tgz(r-4.5-x86_64)glt_1.0.tgz(r-4.5-arm64)
glt_1.0.tar.gz(r-4.7-arm64)glt_1.0.tar.gz(r-4.7-x86_64)glt_1.0.tar.gz(r-4.6-arm64)glt_1.0.tar.gz(r-4.6-x86_64)
glt_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
glt/json (API)

# Install 'glt' in R:
install.packages('glt', repos = c('https://tonycanale.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tonycanale/glt/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.90 score 19 downloads 6 exports 3 dependencies

Last updated from:16796f4214. Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK174
linux-devel-x86_64OK185
source / vignettesOK243
linux-release-arm64OK163
linux-release-x86_64OK163
macos-release-arm64OK242
macos-release-x86_64OK401
macos-oldrel-arm64OK216
macos-oldrel-x86_64OK580
windows-devel-arm64OK403
windows-devel-x86_64OK216
windows-release-arm64OK419
windows-release-x86_64OK234
windows-oldrel-x86_64OK163
wasm-releaseOK136

Exports:facmatrixget_pivotsget_variancegltfagltfa_starplot_real_matrix

Dependencies:RcppRcppArmadillosparvaride