# multiModTest 1.1

## New features

* 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`.


## Documentation

* 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.

## Dependencies

* 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.

## Bug fixes and improvements

* 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.

# multiModTest 1.0

* 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`.
