Finds adaptive strategies for sequential symmetric 
    games using a genetic algorithm. Currently, any symmetric two by two matrix
    is allowed, and strategies can remember the history of an opponent's play
    from the previous three rounds of moves in iterated interactions between
    players. The genetic algorithm returns a list of adaptive strategies given
    payoffs, and the mean fitness of strategies in each generation.

Implementation of a common set of punctual solutions  for Cooperative Game Theory.

The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2017 JCGS), 'One-Step Estimator Paths for Concave Regularization', <arXiv:1308.5623>.

Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.

Interface for extra smooth functions including tensor products, 
             neural networks and decision trees.

This is an add-on package to GAMLSS. The purpose of this
        package is to allow users to fit interval response variables in
        GAMLSS models. The main function gen.cens() generates a
        censored version of an existing GAMLSS family distribution.

Data used as examples in the books on Generalized Additive Models for Location Scale and Shape:
  Stasinopoulos, Rigby, Heller, Voudouris, De Bastiani (2017). Flexible Regression and Smoothing: Using GAMLSS in R, <doi:10.1201/b21973>.
  Rigby, Stasinopoulos, Heller, De Bastiani (2019). Distributions for Modeling Location, Scale, and Shape Using GAMLSS in R, <doi:10.1201/9780429298547>.
  Stasinopoulos, Kneib, Klein, Mayr, Heller (2024). Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications, <doi:10.1017/9781009410076>.