Package: mrfse 0.4.1

mrfse: Markov Random Field Structure Estimator

Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).

Authors:Rodrigo Carvalho [aut, cre], Florencia Leonardi [rev, ths]

mrfse_0.4.1.tar.gz
mrfse_0.4.1.zip(r-4.5)mrfse_0.4.1.zip(r-4.4)mrfse_0.4.1.zip(r-4.3)
mrfse_0.4.1.tgz(r-4.4-x86_64)mrfse_0.4.1.tgz(r-4.4-arm64)mrfse_0.4.1.tgz(r-4.3-x86_64)mrfse_0.4.1.tgz(r-4.3-arm64)
mrfse_0.4.1.tar.gz(r-4.5-noble)mrfse_0.4.1.tar.gz(r-4.4-noble)
mrfse_0.4.1.tgz(r-4.4-emscripten)mrfse_0.4.1.tgz(r-4.3-emscripten)
mrfse.pdf |mrfse.html
mrfse/json (API)

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

11 exports 0.00 score 7 dependencies 12 scripts 256 downloads

Last updated 2 years agofrom:ef461185fc. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 21 2024
R-4.5-win-x86_64OKAug 21 2024
R-4.5-linux-x86_64OKAug 21 2024
R-4.4-win-x86_64OKAug 21 2024
R-4.4-mac-x86_64OKAug 21 2024
R-4.4-mac-aarch64OKAug 21 2024
R-4.3-win-x86_64OKAug 21 2024
R-4.3-mac-x86_64OKAug 21 2024
R-4.3-mac-aarch64OKAug 21 2024

Exports:mrfse.cimrfse.ci.conmrfse.ci.nconmrfse.create.samplermrfse.exactmrfse.exact.conmrfse.exact.nconmrfse.samrfse.sa.conmrfse.sa.nconmrfse.sample

Dependencies:gtoolsRcppRcppArmadilloRcppGSLRcppParallelRcppZigguratRfast