## R Package Development

R is a free software environment for statistical computing and graphics. R is distributed through CRAN (Comprehensive R Archive Network) mirrors . I contribute as developer/maintainer of the following R packages:

#### fastmatrix: Fast computation of some matrices useful in statistics

Yet another R package for matrices. It contains a small set of functions to fast
computation of some matrices and operations useful in statistics.

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#### HEAVY: Robust estimation using heavy-tailed distributions

Functions to perform robust estimation considering heavy-tailed distributions.
Currently, the package includes linear regression, linear mixed-effect models,
multivariate location and scatter estimation, multivariate regression, penalized
splines and random variate generation.

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#### L1pack: Routines for L1 Estimation

Provides routines to perform L1 estimation for linear regression, evaluation
of density, distribution function, quantile function and random number generation
for univariate and multivariate Laplace distribution.

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#### MVT: Estimation and testing for the multivariate t-distribution

This package contains a set of routines to perform estimation and inference
under the multivariate *t*-distribution. These methods are a direct
generalization of the multivariate inference under the gaussian assumption.
In addition, these procedures provide robust methods useful against outliers.

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#### SpatialPack: Tools for assessment the association between two spatial processes

This package provides tools to assess the association between two spatial
processes. Currently, four methodologies are implemented: A modified t-test
to perform hypothesis testing about the independence between the processes,
a suitable nonparametric correlation coefficient, the codispersion coefficient,
and an F test for assessing the multiple correlation between one spatial process
and several others. Functions for image processing and computing the spatial
association between images are also provided. SpatialPack gives methods to
complement methodologies that are available in geoR for one spatial process.

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## Find me at GitHub

Other codes/stuff are available at GitHub: github.com/faosorios