Added support for the Cox proportional hazards model via family = "cox".
Feature screening for this family uses a new (iterative) sure independence
screening routine driven by the extended BIC of the marginal Cox fits.
y may now be supplied for the Cox family either as a survival::Surv
object or as a two-column (time, status) matrix or data frame.
Added a robust Median-of-Means gradient-descent (MoM-GD) estimator, selected
with robust = "mom", for heavy-tailed responses. It supports the Gaussian
and Negative-Binomial (family = "negative.binomial") families and reports
basic bootstrap confidence intervals.
New arguments accompanying the robust estimator:
nblock — number of Median-of-Means blocks (default 5).nboot — number of bootstrap resamples for the confidence interval
(default 200).theta — fixed Negative-Binomial dispersion; estimated from the screened
design when NULL.New argument CI controls whether confidence intervals are computed. The
default depends on the estimator: TRUE for robust = "none" (closed-form
intervals are cheap) and FALSE for robust = "mom" (the bootstrap is not
run unless requested). When CI = FALSE, only point estimates are returned
and the interval bounds are NA.
Added two example datasets: data_cox_model and data_nb_model.
Expanded the documentation for mglm.test() to describe the accepted forms
and constraints of every argument, and added runnable examples for the Cox
model and for robust MoM estimation.
The package description no longer advertises p-values; the function reports information-gain point estimates and confidence intervals.
Removed the dependency on tidyverse, which was imported in full but not necessary. This considerably reduces the installation footprint.
Added explicit imports of stats::BIC(), stats::sd(), and
survival::Surv().
Added testthat (>= 3.0.0) to Suggests and enabled testthat edition 3.
Substantially expanded input validation with informative error messages. X, y, mod.idx, family, robust, penalty, tune, CI.type, iter, trace, conf.level, lambda, nlambda, nboot, nblock, and theta are all checked for type, shape, finiteness, and admissible ranges. mod.idx must now be a non-empty list of disjoint, in-range column indices.
Fixed the handling of a user-supplied lambda sequence or confidence levels.
Initial release.
mglm.test() estimates the information gain contributed by individual
modalities in multimodal regression models, measured by Expected Relative
Entropy (ERE) or a pseudo-R-squared, with confidence intervals. Supports the
Gaussian and Binomial families with canonical links.
summary() method for objects of class mglm.test.
Example datasets data_linear_model and data_logistic_model.