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dglars (version 2.1.7)

Differential Geometric Least Angle Regression

Description

Differential geometric least angle regression method for fitting sparse generalized linear models. In this version of the package, the user can fit models specifying Gaussian, Poisson, Binomial, Gamma and Inverse Gaussian family. Furthermore, several link functions can be used to model the relationship between the conditional expected value of the response variable and the linear predictor. The solution curve can be computed using an efficient predictor-corrector or a cyclic coordinate descent algorithm, as described in the paper linked to via the URL below.

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Version

Install

install.packages('dglars')

Monthly Downloads

324

Version

2.1.7

License

GPL (>= 2)

Maintainer

Luigi Augugliaro

Last Published

October 9th, 2023

Functions in dglars (2.1.7)

cvdglars

Cross-Validation Method for dgLARS
plot.cvdglars

Plot from a cvdglars Object
summary.dglars

Summaryzing dgLARS Fits
print.dglars

Printing a dgLARS Object
print.cvdglars

Print a cvdglars Object
predict.dglars

Predict Method for dgLARS Fits.
logLik.dglars

Extract Log-Likelihood
gdf

Estimate the Generalized Degrees-of-Freedom
phihat

Estimate the Dispersion Parameter
grcv

General Refitted Cross-Validation Estimator
plot.dglars

Plot from a dglars Object
alon

Data from the microarray experiment done by Alon et al. (1999)
AIC.dglars

Akaike's An Information Criterion
breast

Breast Cancer microarray experiment
duke

Duke breast cancer microarray experiment
coef.cvdglars

Extract the Coefficients Estimated by cvdglars
dglars

dgLARS Solution Curve for GLM
coef.dglars

Extract the dgLARS Coefficient Path
dglars-internal

Internal dglars Functions
dglars-package

Differential Geometric Least Angle Regression