This package is considered a duplicate. The official version of this package is found at:https://treynkens.r-universe.dev/smurf

Package: smurf 1.1.5

Tom Reynkens

smurf: Sparse Multi-Type Regularized Feature Modeling

Implementation of the SMuRF algorithm of Devriendt et al. (2021) <doi:10.1016/j.insmatheco.2020.11.010> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.

Authors:Tom Reynkens [aut, cre], Sander Devriendt [aut], Katrien Antonio [aut]

smurf_1.1.5.tar.gz
smurf_1.1.5.tar.gz(r-4.5-noble)smurf_1.1.5.tar.gz(r-4.4-noble)
smurf_1.1.5.tgz(r-4.4-emscripten)smurf_1.1.5.tgz(r-4.3-emscripten)
smurf.pdf |smurf.html✨
smurf/json (API)
NEWS

# Install 'smurf' in R:

install.packages('smurf', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://gitlab.com/treynkens/smurf

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

2.94 score 1 packages 29 scripts 342 downloads 13 exports 16 dependencies

Last updated 2 years agofrom:354cf00e4f. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 15 2024
R-4.5-linux-x86_64	OK	Nov 15 2024

Exports:coef_reest coefficients_reest deviance_reest fitted_reest glmsmurf glmsmurf.control glmsmurf.fit p plot_lambda plot_reest predict_reest resid_reest residuals_reest

Dependencies:catdata codetools foreach glmnet iterators lattice MASS Matrix mgcv nlme RColorBrewer Rcpp RcppArmadillo RcppEigen shape survival

Introduction to the smurf package

| Tom Reynkens, Sander Devriendt and Katrien Antonio | LRisk, Faculty of Economics and Business, KU Leuven

Rendered fromsmurf.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2021-03-12
Started: 2018-12-09

Citation

Readme and manuals

Help Manual

Help page	Topics
smurf: Sparse Multi-Type Regularized Feature Modeling	smurf-package smurf
Coefficients of Re-estimated Model	coefficients_reest coefficients_reest.glmsmurf coef_reest coef_reest.glmsmurf
Coefficients of Estimated Model	coef.glmsmurf coefficients.glmsmurf
Deviance of Re-estimated Model	deviance_reest deviance_reest.glmsmurf
Deviance of Estimated Model	deviance.glmsmurf
Fitted Values of Re-estimated Model	fitted_reest fitted_reest.glmsmurf
Fitted Values of Estimated Model	fitted.glmsmurf
Fit a Multi-Type Regularized GLM Using the SMuRF Algorithm	glmsmurf glmsmurf.fit
Class of Multi-Type Regularized GLMs Fitted Using the SMuRF Algorithm	glmsmurf-class
Control Function for Fitting a Multi-Type Regularized GLM Using the SMuRF Algorithm.	glmsmurf.control
Define Individual Subpenalties for a Multi-Type Regularized GLM	p
Plot Goodness-of-Fit Statistics or Information Criteria	plot_lambda plot_lambda.glmsmurf
Plot Coefficients of Re-estimated Model	plot_reest plot_reest.glmsmurf
Plot Coefficients of Estimated Model	plot.glmsmurf
Predictions Using Re-estimated Model	predict_reest predict_reest.glmsmurf
Predictions Using Estimated Model	predict.glmsmurf
Residuals of Re-estimated Model	residuals_reest residuals_reest.glmsmurf resid_reest resid_reest.glmsmurf
Residuals of Estimated Model	resid.glmsmurf residuals.glmsmurf
Summary of a Multi-Type Regularized GLM Fitted Using the SMuRF Algorithm	summary.glmsmurf