smurf: Sparse Multi-Type Regularized Feature Modeling

Implementation of the SMuRF algorithm of Devriendt et al. (2018) <arXiv:1810.03136> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.

Version: 1.0.2
Depends: R (≥ 3.1)
Imports: catdata, glmnet, graphics, MASS, Matrix, methods, mgcv, parallel, RColorBrewer, Rcpp (≥ 0.12.12), speedglm, stats
LinkingTo: Rcpp, RcppArmadillo (≥ 0.8.300.1.0)
Suggests: bookdown, knitr, roxygen2 (≥ 6.0.0), testthat
Published: 2019-11-25
Author: Tom Reynkens ORCID iD [aut, cre], Sander Devriendt [aut], Katrien Antonio [aut]
Maintainer: Tom Reynkens <tomreynkens at hotmail.com>
BugReports: https://gitlab.com/TReynkens/smurf/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://gitlab.com/TReynkens/smurf
NeedsCompilation: yes
Materials: NEWS
CRAN checks: smurf results

Downloads:

Reference manual: smurf.pdf
Vignettes: Introduction to the smurf package
Package source: smurf_1.0.2.tar.gz
Windows binaries: r-devel: smurf_1.0.2.zip, r-devel-gcc8: smurf_1.0.2.zip, r-release: smurf_1.0.2.zip, r-oldrel: smurf_1.0.2.zip
OS X binaries: r-release: smurf_1.0.2.tgz, r-oldrel: smurf_1.0.2.tgz
Old sources: smurf archive

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