Package: GlarmaVarSel 1.0

Marina Gomtsyan

GlarmaVarSel: Variable Selection in Sparse GLARMA Models

Performs variable selection in high-dimensional sparse GLARMA models. For further details we refer the reader to the paper Gomtsyan et al. (2020), <arxiv:2007.08623v1>.

Authors:M. Gomtsyan, C. Levy-Leduc, S. Ouadah, L. Sansonnet

GlarmaVarSel_1.0.tar.gz
GlarmaVarSel_1.0.tar.gz(r-4.5-noble)GlarmaVarSel_1.0.tar.gz(r-4.4-noble)
GlarmaVarSel_1.0.tgz(r-4.4-emscripten)GlarmaVarSel_1.0.tgz(r-4.3-emscripten)
GlarmaVarSel.pdf |GlarmaVarSel.html✨
GlarmaVarSel/json (API)

# Install 'GlarmaVarSel' in R:

install.packages('GlarmaVarSel', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

Y - Observation matrix Y

On CRAN:

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

2.00 score 166 downloads 4 exports 36 dependencies

Last updated 4 years agofrom:da79cec7d0. Checks:3 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 19 2025
R-4.5-linux	OK	Mar 19 2025
R-4.4-linux	OK	Mar 19 2025

Exports:grad_hess_beta grad_hess_gamma NR_gamma variable_selection

Dependencies:cli codetools colorspace fansi farver foreach ggplot2 glmnet glue gtable isoband iterators labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme pillar pkgconfig R6 RColorBrewer Rcpp RcppEigen rlang scales shape survival tibble utf8 vctrs viridisLite withr

GlarmaVarSel package

Marina Gomtsyan, Céline Lévy-Leduc, Sarah Ouadah, Laure Sansonnet

Rendered fromGlarmaVarSel.Rmdusingknitr::knitron Mar 19 2025.

Last update: 2021-09-16
Started: 2021-09-16

Citation

Readme and manuals

Help Manual

Help page	Topics
Variable Selection in Sparse GLARMA Models	GlarmaVarSel-package GlarmaVarSel
Gradient and Hessian of the log-likelihood with respect to beta	grad_hess_beta
Gradient and Hessian of the log-likelihood with respect to gamma	grad_hess_gamma
Newton-Raphson method for estimation of gamma	NR_gamma
Variable selection	variable_selection
Observation matrix Y	Y