SLOPE v0.3.2

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by Johan Larsson View Source

Sorted L1 Penalized Estimation

Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm (Bogdan et al. (2015) <doi:10/gfgwzt>). Supported models include ordinary least-squares regression, binomial regression, multinomial regression, and Poisson regression. Both dense and sparse predictor matrices are supported. In addition, the package features predictor screening rules that enable fast and efficient solutions to high-dimensional problems.

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SLOPE

Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm. There is support for ordinary least-squares regression, binomial regression, multinomial regression, and poisson regression, as well as both dense and sparse predictor matrices. In addition, the package features predictor screening rules that enable efficient solutions to high-dimensional problems.

Installation

You can install the current stable release from CRAN with

install.packages("SLOPE")

or the development version from GitHub with

# install.packages("remotes")
remotes::install_github("jolars/SLOPE")

Versioning

SLOPE uses semantic versioning.

Code of conduct

Please note that the ‘SLOPE’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Functions in SLOPE

Name	Description
coef.SLOPE	Obtain coefficients
predict.SLOPE	Generate predictions from SLOPE models
plotDiagnostics	Plot results from diagnostics collected during model fitting
SLOPE	Sorted L-One Penalized Estimation
SLOPE-package	SLOPE: Sorted L1 Penalized Estimation
interpolatePenalty	Interpolate penalty values
trainSLOPE	Train a SLOPE model
wine	Wine cultivars
heart	Heart disease
setupDiagnostics	Setup a data.frame of diagnostics
plot.SLOPE	Plot coefficients
student	Student performance
print.SLOPE	Print results from SLOPE fit
plot.TrainedSLOPE	Plot results from cross-validation
score	Compute one of several loss metrics on a new data set
caretSLOPE	Model objects for model tuning with caret
deviance.SLOPE	Model deviance
abalone	Abalone
interpolateCoefficients	Interpolate coefficients
bodyfat	Bodyfat
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Vignettes of SLOPE

Name
SLOPE.bib
introduction.Rmd
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Details

License	GPL-3
LazyData	true
LinkingTo	Rcpp, RcppArmadillo (>= 0.9.850.1.0)
RoxygenNote	7.1.1
Language	en-US
Encoding	UTF-8
URL	https://jolars.github.io/SLOPE/, https://github.com/jolars/SLOPE
BugReports	https://github.com/jolars/SLOPE/issues
VignetteBuilder	knitr
NeedsCompilation	yes
Packaged	2020-07-08 06:51:09 UTC; gerd-jln
Repository	CRAN
Date/Publication	2020-07-10 15:20:22 UTC

suggests	caret , covr , glmnet , knitr , rmarkdown , spelling , testthat (>= 2.1.0)
imports	foreach , lattice , Matrix , methods , Rcpp
depends	R (>= 3.3.0)
linkingto	RcppArmadillo (>= 0.9.850.1.0)
Contributors	Malgorzata Bogdan, Ewout van den Berg, Chiara Sabatti, Weijie Su, Rob Tibshirani, Trevor Hastie, Emmanuel Candes, Evan Patterson, Jerome Friedman, Balasubramanian Narasimhan, Noah Simon, Junyang Qian, Jonas Wallin, Akarsh Goyal