SLOPE v0.3.2
Monthly downloads
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.
Readme
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 | |
No Results! |
Vignettes of SLOPE
Name | ||
SLOPE.bib | ||
introduction.Rmd | ||
No Results! |
Last month downloads
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 |
Include our badge in your README
[](http://www.rdocumentation.org/packages/SLOPE)