Package: pengls 1.13.0
Stijn Hawinkel
pengls: Fit Penalised Generalised Least Squares models
Combine generalised least squares methodology from the nlme package for dealing with autocorrelation with penalised least squares methods from the glmnet package to deal with high dimensionality. This pengls packages glues them together through an iterative loop. The resulting method is applicable to high dimensional datasets that exhibit autocorrelation, such as spatial or temporal data.
Authors:
pengls_1.13.0.tar.gz
pengls_1.13.0.zip(r-4.5)pengls_1.13.0.zip(r-4.4)pengls_1.13.0.zip(r-4.3)
pengls_1.13.0.tgz(r-4.4-any)pengls_1.13.0.tgz(r-4.3-any)
pengls_1.13.0.tar.gz(r-4.5-noble)pengls_1.13.0.tar.gz(r-4.4-noble)
pengls_1.13.0.tgz(r-4.4-emscripten)pengls_1.13.0.tgz(r-4.3-emscripten)
pengls.pdf |pengls.html✨
pengls/json (API)
NEWS
| # Install 'pengls' in R: |
| install.packages('pengls', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/sthawinke/pengls/issues
On BioConductor:pengls-1.13.0(bioc 3.21)pengls-1.12.0(bioc 3.20)
transcriptomicsregressiontimecoursespatial
Last updated 2 months agofrom:1a0f003a26. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 29 2024 |
| R-4.5-win | OK | Nov 29 2024 |
| R-4.5-linux | OK | Nov 29 2024 |
| R-4.4-win | OK | Nov 29 2024 |
| R-4.4-mac | OK | Nov 29 2024 |
| R-4.3-win | OK | Nov 29 2024 |
| R-4.3-mac | OK | Nov 29 2024 |
Exports:cv.penglsmakeFoldspengls
Dependencies:BHBiocParallelcodetoolscpp11foreachformatRfutile.loggerfutile.optionsglmnetiteratorslambda.rlatticeMatrixnlmeRcppRcppEigenshapesnowsurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Extract coefficients from a cv.pengls model | coef.cv.pengls |
| Extract coefficients from a pengls model | coef.pengls |
| Peform cross-validation pengls | cv.pengls |
| Get the (square root of the inverse of the) correlation matrix | getCorMat |
| Calculate the loss given predicted and observed values | getLoss |
| Divide observations into folds | makeFolds |
| Iterative estimation of penalised generalised least squares | pengls |
| Make predictions from a cv.pengls model | predict.cv.pengls |
| Make predictions from a pengls model | predict.pengls |
| Print a summary of a cv.pengls model | print.cv.pengls |
| Print a summary of a pengls model | print.pengls |