Differential Evolution Optimization in Pure R

Differential Evolution (DE) stochastic heuristic algorithms for global optimization of problems with and without general constraints. The aim is to curate a collection of its variants that (1) do not sacrifice simplicity of design, (2) are essentially tuning-free, and (3) can be efficiently implemented directly in the R language. Currently, it provides implementations of the algorithms 'jDE' by Brest et al. (2006) for single-objective optimization and 'NCDE' by Qu et al. (2012) for multimodal optimization (single-objective problems with multiple solutions).

Local Polynomial (Ridge) Regression

Local Polynomial Regression with Ridging.

Density, Probability, Quantile ('DPQ') Computations

Computations for approximations and alternatives for the 'DPQ' (Density (pdf), Probability (cdf) and Quantile) functions for probability distributions in R. Primary focus is on (central and non-central) beta, gamma and related distributions such as the chi-squared, F, and t. -- For several distribution functions, provide functions implementing formulas from Johnson, Kotz, and Kemp (1992) and Johnson, Kotz, and Balakrishnan (1995) for discrete or continuous distributions respectively. This is for the use of researchers in these numerical approximation implementations, notably for my own use in order to improve standard R pbeta(), qgamma(), ..., etc: {'"dpq"'-functions}.

Bitmap Images / Pixel Maps

Functions for import, export, visualization and other manipulations of bitmapped images.

Rmetrics - Modeling of Multivariate Financial Return Distributions

A collection of functions inspired by Venables and Ripley (2002) and Azzalini and Capitanio (1999) to manage, investigate and analyze bivariate and multivariate data sets of financial returns.

Methods for Graphical Models and Causal Inference

Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational data without hidden variables), FCI and RFCI (for observational data with hidden variables), and GIES (for a mix of data from observational studies (i.e. observational data) and data from experiments involving interventions (i.e. interventional data) without hidden variables). For causal inference the IDA algorithm, the Generalized Backdoor Criterion (GBC), the Generalized Adjustment Criterion (GAC) and some related functions are implemented. Functions for incorporating background knowledge are provided.

Robust Statistics: Theory and Methods

Companion package for the book: "Robust Statistics: Theory and Methods, second edition", < http://www.wiley.com/go/maronna/robust>. This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.

R Commander

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

Examples from Multilevel Modelling Software Review

Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.

Critical Line Algorithm in Pure R

Implements 'Markowitz' Critical Line Algorithm ('CLA') for classical mean-variance portfolio optimization, see Markowitz (1952) . Care has been taken for correctness in light of previous buggy implementations.