Package: rplam 1.0.2

Alejandra Mercedes Martínez

rplam: Robust Estimators for Partially Linear Additive Models

Robust estimation for partially linear additive models based on B-splines. The package implements a robust proposal that combines robust linear MM-regression estimators, which use an initial S-estimator followed by an M-estimation step with fixed scale, and B-spline approximations. The M-estimation step uses Tukey's bisquare function. The least-squares counterpart is also implemented. See Boente and Martínez (2023) <doi:10.1016/j.csda.2022.107611> for more details. The package also provides robust and least-squares estimators for additive models, that is, when the covariates enter only the nonparametric component of the model.

Authors:Alejandra Mercedes Martínez [aut, cre]

rplam_1.0.2.tar.gz
rplam_1.0.2.zip(r-4.7-x86_64)rplam_1.0.2.zip(r-4.7-arm64)rplam_1.0.2.zip(r-4.6-x86_64)rplam_1.0.2.zip(r-4.6-arm64)
rplam_1.0.2.tar.gz(r-4.7-arm64)rplam_1.0.2.tar.gz(r-4.7-x86_64)rplam_1.0.2.tar.gz(r-4.6-arm64)rplam_1.0.2.tar.gz(r-4.6-x86_64)
rplam_1.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
rplam/json (API)

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

On CRAN:

Conda:

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

1.00 score 2 scripts 17 exports 51 dependencies

Last updated from:11c16aa801. Checks:10 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK104
linux-devel-x86_64OK119
source / vignettesOK189
linux-release-arm64OK99
linux-release-x86_64OK125
windows-devel-arm64OK205
windows-devel-x86_64OK138
windows-release-arm64OK226
windows-release-x86_64OK72
wasm-releaseOK121

Exports:am.clam.robfitted.values.amfitted.values.plammy.norm.2plam.clplam.robpsi.tukeypsi.tukey.derivativepsi.wR2R2.robselect.nknots.clselect.nknots.cl.amselect.nknots.robselect.nknots.rob.amtukey.loss

Dependencies:ashaskpasscliclustercolorspacecpp11curlDEoptimRdeSolvefarverfdafdsFNNggplot2gluegmpgtablehdrcdehttrisobandjsonlitekernlabKernSmoothkslabelinglatticelifecyclelocfitMASSMatrixmclustmgcvmimemulticoolmvtnormnlmeopensslpcaPPpracmaR6rainbowRColorBrewerRcpprlangrobustbaseS7scalessysvctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Classical estimator for additive modelsam.cl
Robust estimator for additive modelsam.rob
Deviance for objects of class 'am'deviance.am
Deviance for objects of class 'plam'deviance.plam
Fitted values for objects of class 'am'fitted.values.am
Fitted values for objects of class 'plam'fitted.values.plam
Partially linear additive model formulaformula.plam
Euclidean norm of a vectormy.norm.2
Classical estimator for partially linear additive modelsplam.cl
Robust estimator for partially linear additive modelsplam.rob
Diagnostic plots for objects of class 'am'plot.am
Diagnostic plots for objects of class 'plam'plot.plam
Fitted values for objects of class 'am'predict.am
Fitted values for objects of class 'plam'predict.plam
Print a Partially Linear Additive Modelprint.plam
Derivative of Tukey's bi-square loss function.psi.tukey
Derivative of the Tukey's loss functionpsi.tukey.derivative
Tukey's weight functionpsi.w
Multiple R-squaredR2
Robust multiple R-squaredR2.rob
Residuals for objects of class 'am'residuals.am
Residuals for objects of class 'plam'residuals.plam
Selection of the number of internal knots for the least-squares estimator under a PLAMselect.nknots.cl
Selection of the number of internal knots for the least-squares estimator for additive modelsselect.nknots.cl.am
Selection of the number of knots for the robust estimator under a PLAMselect.nknots.rob
Selection of the number of knots for the robust estimator for additive modelsselect.nknots.rob.am
Summary for additive models fitssummary.am summary.am.cl summary.am.rob
Summary for partially linear additive models fitssummary.plam summary.plam.cl summary.plam.rob
Tukey's loss functiontukey.loss