**Automatic Calibration by Evolutionary Multi Objective Algorithm** (**caRamel**)

Multi-objective optimizer initially developed for the calibration of hydrological models. The algorithm is a hybrid of the MEAS algorithm (Efstratiadis and Koutsoyiannis (2005) <doi:10.13140/RG.2.2.32963.81446>) by using the directional search method based on the simplexes of the objective space and the epsilon-NGSA-II algorithm with the method of classification of the parameter vectors archiving management by epsilon-dominance (Reed and Devireddy <doi:10.1142/9789812567796_0004>).

**Power Calculations for Two-Sample Wilcoxon-Mann-Whitney Test** (**wmwpow**)

Software for power calculations for two-sample Wilcoxon-Mann-Whitney test for a continuous outcome (Mann and Whitney 1947) <doi:10.1214/aoms/1177730491>, (Shieh, Jan, and Randles 2006) <doi:10.1080/10485250500473099>.

**Bayesian Analysis of a Circular GLM** (**circglmbayes**)

Perform a Bayesian analysis of a circular outcome General Linear Model (GLM), which allows regressing a circular outcome on linear and categorical predictors. Posterior samples are obtained by means of an MCMC algorithm written in ‘C++’ through ‘Rcpp’. Estimation and credible intervals are provided, as well as hypothesis testing through Bayes Factors. See Mulder and Klugkist (2017) <doi:10.1016/j.jmp.2017.07.001>.

**Fast Multivariate Log-Concave Density Estimation** (**fmlogcondens**)

A fast solver for the maximum likelihood estimator (MLE) of a multivariate log-concave probability function. Given a sample X, it estimates a non-parametric density function whose logarithm is a concave function. Many well-known parametric densities belong to that class, among them the normal density, the uniform density, the exponential distribution and many more. This package provides functions for the estimation of a log-concave density and a mixture of log-concave densities in multiple dimensions. While being similar to the package LogConcDEAD, fmlogcondens provides much fast run times for large samples (>= 250 points). As a reference see Fabian Rathke, Christoph Schnörr (2015), <doi:10.1515/auom-2015-0053>.

**Joint Segmentation of Correlated Time Series** (**FASeg**)

It contains a function designed to the joint segmentation in the mean of several correlated series. The method is described in the paper X. Collilieux, E. Lebarbier and S. Robin. A factor model approach for the joint segmentation with between-series correlation (2015) <arXiv:1505.05660>.

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