Convex Least Squares Programming (CLSP) is a two-step estimator 
    for solving underdetermined, ill-posed, or structurally constrained 
    least-squares problems. It combines pseudoinverse-based estimation with 
    convex-programming correction methods inspired by Lasso, Ridge, and 
    Elastic Net to ensure numerical stability, constraint enforcement, and 
    interpretability. The package also provides numerical stability analysis 
    and CLSP-specific diagnostics, including partial R^2, normalized RMSE 
    (NRMSE), Monte Carlo t-tests for mean NRMSE, and condition-number-based 
    confidence bands.

Tool for providing access to the Java version 'CMAEvolutionStrategy' of
    Nikolaus Hansen. 'CMA-ES' is the Covariance Matrix Adaptation Evolution Strategy,
    see <https://www.lri.fr/~hansen/cmaes_inmatlab.html#java>.

Run 'R CMD check' from 'R' and capture the results
    of the individual checks. Supports running checks in the background,
    timeouts, pretty printing and comparing check results.

A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.

Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.

R Commander plug-in for repeated-measures and mixed-design
    ('split-plot') ANOVA.
    It adds a new menu entry for repeated measures that allows
    to deal with up to three within-subject factors and optionally with
    one or several between-subject factors.
    It also provides supplementary options to oneWayAnova() and
    multiWayAnova() functions, such as choice of ANOVA type, display of
    effect sizes and post hoc analysis for multiWayAnova().

Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details, refer to Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394>.