Package: NBtsVarSel 1.0

NBtsVarSel: Variable Selection in a Specific Regression Time Series of Counts

Performs variable selection in sparse negative binomial GLARMA (Generalised Linear Autoregressive Moving Average) models. For further details we refer the reader to the paper Gomtsyan (2023), <arxiv:2307.00929>.

Authors:Marina Gomtsyan [aut, cre]

NBtsVarSel_1.0.tar.gz
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NBtsVarSel.pdf |NBtsVarSel.html✨
NBtsVarSel/json (API)

# Install 'NBtsVarSel' in R:

install.packages('NBtsVarSel', repos = c('https://mgomtsyan.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 2 scripts 155 downloads 4 exports 44 dependencies

Last updated 2 years agofrom:633b933f66. Checks:7 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 07 2025
R-4.5-win	OK	Feb 07 2025
R-4.5-linux	OK	Feb 07 2025
R-4.4-win	OK	Feb 07 2025
R-4.4-mac	OK	Feb 07 2025
R-4.3-win	OK	Feb 07 2025
R-4.3-mac	OK	Feb 07 2025

Exports:grad_hess_beta grad_hess_gamma NR_gamma variable_selection

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

NBtsVarSel package

Marina Gomtsyan

Rendered fromNBtsVarSel.Rmdusingknitr::knitron Feb 07 2025.

Last update: 2023-07-17
Started: 2023-07-17

Citation

Readme and manuals

Help Manual

Help page	Topics
Variable Selection in a Specific Regression Time Series of Counts	NBtsVarSel-package NBtsVarSel
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