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smurf (version 1.1.7)

Sparse Multi-Type Regularized Feature Modeling

Description

Implementation of the SMuRF algorithm of Devriendt et al. (2021) to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.

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Version

Install

install.packages('smurf')

Monthly Downloads

336

Version

1.1.7

License

GPL (>= 2)

Maintainer

Tom Reynkens

Last Published

February 23rd, 2025

Functions in smurf (1.1.7)

residuals_reest

Residuals of Re-estimated Model
predict_reest

Predictions Using Re-estimated Model
plot.glmsmurf

Plot Coefficients of Estimated Model
smurf-package

smurf: Sparse Multi-Type Regularized Feature Modeling
summary.glmsmurf

Summary of a Multi-Type Regularized GLM Fitted Using the SMuRF Algorithm
plot_reest

Plot Coefficients of Re-estimated Model
predict.glmsmurf

Predictions Using Estimated Model
residuals.glmsmurf

Residuals of Estimated Model
plot_lambda

Plot Goodness-of-Fit Statistics or Information Criteria
deviance_reest

Deviance of Re-estimated Model
p

Define Individual Subpenalties for a Multi-Type Regularized GLM
glmsmurf.control

Control Function for Fitting a Multi-Type Regularized GLM Using the SMuRF Algorithm.
fitted_reest

Fitted Values of Re-estimated Model
coef.glmsmurf

Coefficients of Estimated Model
glmsmurf-class

Class of Multi-Type Regularized GLMs Fitted Using the SMuRF Algorithm
deviance.glmsmurf

Deviance of Estimated Model
fitted.glmsmurf

Fitted Values of Estimated Model
glmsmurf

Fit a Multi-Type Regularized GLM Using the SMuRF Algorithm
coef_reest

Coefficients of Re-estimated Model