# pense 2.5.2

* Fix division by a value close to 0 on certain LAPACK implementations, leading to residuals without variation and a failure in RIS-CV.

# pense 2.5.0

* Use Robust Information Sharing Cross-Validation (RIS-CV) by default for `pense_cv()` and `adapense_cv()`.
  Details about RIS-CV can be found in [Kepplinger & Wei (2025)](https://doi.org/10.1080/00401706.2025.2540970).
* Add support for the optimal rho function (Maronna et al. 2018, Section 5.8.1), using the polynomial approximation from the robustbase package.
* Fix undefined behavior identified by the extended CRAN checks.

# pense 2.2.2
 * Fix build warnings from GCC 14.1.0 on CRAN

# pense 2.2.1
 * Fix build error on Windows using GCC version < 9.

# pense 2.2.0
  * Fix bug where argument `max_solutions` is not used correctly.
  * Add new numerical coordinate-descent algorithms for LS-EN (`en_cd_options()`).
  * Revamp the regularization path and how starting points are used and shared.
  * Share starting points across CV folds and replications.
  * Address changes in the upcoming Rcpp release.

# pense 2.1.0
  * Penalty loadings are now applied to both the L1 and L2 parts of the EN penalty.
    This will lead to different results for adaptive PENSE and other adaptive estimators when fitted with *alpha < 1*!
  * PENSE and regularized M-estimators now accept multiple `alpha` values and automatic hyper-parameter selection will also choose the best `alpha` value.
  * New support for specifying the more general "1-SE" rules for the penalization level as string.
    All methods which support `lambda = "min"` to extract the best fit, also support the syntax `lambda = "{m}-se"` to extract the most parsimonious fit within *m* standard-errors of the best fit.
  * Adaptively choose the actual breakdown point based on the number of observations. 
    The chosen breakdown point is close to the user-specified breakdown point, but avoids numerical instabilities in the S-loss and excessive computation time caused by these instabilities.
  * Simplify the DAL algorithm to fully rely on linear algebra routines from the BLAS/LAPACK library linked to R.
    To improve the speed of the DAL algorithm, optimized BLAS/LAPACK libraries are recommended.
  * Fix memory issues from edge-cases and OpenMP problems with Intel compilers

# pense 2.0.3
  * Fix a bug causing PENSE-Ridge, i.e., `pense(..., alpha = 0)`, to take a long time to compute.
  * Fix a compilation error on RHEL due to an error in the autoconf script.
  * Fix problems in `prediction_performance()` related to the non-standard evaluation of objects.
  * Also return standardized coefficients as `std_beta` and `std_intercept`.
# pense 2.0.2
  * Fix mishandling of response variables with a robust scale of 0, e.g., 0-inflated responses or responses with more than 50% identical values.
# pense 2.0.1
  * Add new functions for compute adaptive PENSE estimates (`adapense()` and `adapense_cv()`).
  * Functions for fitting the model (`pense()`, `adapense()`, `regmest()`, etc.) are not estimating prediction performance via cross-validation anymore.
    This can now be done using the corresponding functions `pense_cv()`, `adapense_cv()`, and so on.
  * New function `prediction_performance()` to summarize the prediction performance of several fits.
  * The `plot()`, `coef()`, `summary()`, and `predict()` methods for cross-validated fits also implement the "one-standard-error rule" (with the "1" adjustable by the user).
  * Decrease computation time for most problems.
  * New ADMM algorithm for (weighted) elastic net problems with many observations and many predictors.
    The new algorithm can be selected with `en_admm_options()`.
  * Argument `correct` in `pense()`, `pensem()`, `coef()`, etc., is not supported anymore and will be ignored with a warning.
    All estimates are now **uncorrected** (i.e., `correct=FALSE` in previous versions of the package).
  * Make interface more consistent and deprecate the following methods:
    - `pensem()` is now called `pensem_cv()`.
    - `initest_options()` is replaced by `enpy_options()` using better naming of arguments.
    - `en_options_aug_lars()` and `en_options_dal()` are replaced by `en_lars_options()` and `en_dal_options()` for more consistent naming.
    - `pense_options()` and `mstep_options()` are superseded by `mm_algorithm_options()` and arguments specified in the calls to `pense()` and companions.
    - `enpy()` is replaced by `enpy_initial_estimates()` which has different default argument values.
  * Deprecated functions can still be used (for now) with a warning.

# pense 1.2.9
  * Fix LTO warnings reported in CRAN checks
  * Update autoconf script to address deprecation warnings in r-devel.

# pense 1.2.5
  * Fix compatibility of BLAS/LAPACK prototypes with RcppArmadillo 0.9.500.

# pense 1.2.4
  * Fix autoconf script.

# pense 1.2.1
  * Prepare for changes to the upcoming _Rcpp_ (make compatible with `STRICT_R_HEADERS`)
  * Fix a bug in computing PSCs when using the augmented ridge algorithm for EN.

# pense 1.2.0
  * Changed the internal scaling of the regularization parameter for `pense` and `pensem`.
    **Note**: The _lambda_ values in this release are not the same as in previous releases!
  * Fixed a bug when standardizing predictor variables with a MAD of 0 (thanks @hadjipantelis for reporting).
  * The maximum value for the regularization parameter lambda is now chosen exactly.
  * Fixed a bug when computing "exact" principal sensitivity components.
# pense 1.0.8
  * Fix error with robustbase-0.92-8 as reported by Martin Maechler.
  * Fix undefined behavior in C++ code resulting in build error on Solaris (x86).
  * Fix `predict()` function for `pensem` objects if computed from a fitted `pense` object.
  * Always use `delta` and `cc` specified in `pense_options()` for the initial estimator. Remove `delta` and `cc` arguments from `initest_options()` and instead add them to `enpy()`.
  * Add further measure of the prediction performance (`resid_size`) to `obj$cv_lambda_grid`, where `obj` is of class `pense` or `pensem`.
# pense 1.0.6:
  * Initial stable release of the package.
