SLOPE v0.3.2

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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

R build
status Coverage
status CRAN
status Lifecycle:
maturing

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

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