Computer simulations of classical ecological models as a
    learning resource.

Implements various procedures for finding 
	     multiple change-points from Matteson D. et al (2013)
	     <doi:10.1080/01621459.2013.849605>, Zhang W. et al (2017) 
	     <doi:10.1109/ICDMW.2017.44>, Arlot S. et al (2019).
	     Two methods make use of dynamic  
	     programming and pruning, with no distributional 
	     assumptions other than the existence of certain absolute 
	     moments in one method. Hierarchical and exact search methods 
	     are included. All methods return the set of estimated change-
	     points as well as other summary information.

Fit linear, logistic and Cox survival regression models penalised with adaptive multi-group ridge penalties.
  The multi-group penalties correspond to groups of covariates defined by (multiple) co-data sources.
  Group hyperparameters are estimated with an empirical Bayes method of moments, penalised with an extra level of hyper shrinkage.
  Various types of hyper shrinkage may be used for various co-data.
  Co-data may be continuous or categorical. 
  The method accommodates inclusion of unpenalised covariates, posterior selection of covariates and multiple data types.
  The model fit is used to predict for new samples.
  The name 'ecpc' stands for Empirical Bayes, Co-data learnt, Prediction and Covariate selection.
  See Van Nee et al. (2020) <arXiv:2005.04010>.

Computes the Extended Chen-Poisson (ecp) distribution, survival, 
    density, hazard, cumulative hazard and quantile functions. It also allows 
    to generate a pseudo-random sample from this distribution. The corresponding 
    graphics are available. Functions to obtain measures of skewness and 
    kurtosis, k-th raw moments, conditional k-th moments and mean 
    residual life function were added. For details about ecp distribution, see 
    Sousa-Ferreira, I., Abreu, A.M. & Rocha, C. (2023). 
    <doi:10.57805/revstat.v21i2.405>.

Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.