Package: rpql 0.8.1
Francis Hui
rpql: Regularized PQL for Joint Selection in GLMMs
Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then augmented to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.
Authors:
rpql_0.8.1.tar.gz
rpql_0.8.1.tar.gz(r-4.5-noble)rpql_0.8.1.tar.gz(r-4.4-noble)
rpql_0.8.1.tgz(r-4.4-emscripten)rpql_0.8.1.tgz(r-4.3-emscripten)
rpql.pdf |rpql.html✨
rpql/json (API)
NEWS
| # Install 'rpql' in R: |
| install.packages('rpql', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 1 years agofrom:9f80f65385. Checks:OK: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 12 2024 |
| R-4.5-linux-x86_64 | NOTE | Dec 12 2024 |
Exports:build.start.fitcalc.marglogLgendat.glmmlseqnb2rpqlrpql.defaultrpqlseqsummary.rpql
Dependencies:bootgamlss.distlatticelme4MASSMatrixminqamvtnormnlmenloptrRcppRcppArmadilloRcppEigen
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Joint effects selection in GLMMs using regularized PQL | rpql-package |
| Constructs a start fit for use in the 'rpql' function | build.start.fit |
| Calculate the marginal log-likelihood for a GLMM fitted using 'rpql' | calc.marglogL |
| Simulates datasets based on a Generalized Linear Mixed Model (GLMM). | gendat.glmm |
| Generates a sequence of tuning parameters on the log scale | lseq |
| A negative binomial family | nb2 |
| Joint effects selection in GLMMs using regularized PQL. | print.rpql rpql rpql.default |
| Wrapper function for joint effects selection in GLMMs using regularized PQL. | rpqlseq |
| Summary of GLMM fitted using regularized PQL. | print.summary.rpql summary.rpql |
