ESS: Emacs Speaks Statistics
A Multiplatform, Multipackage Development Environment for Statistical Analysis.
ESS is a GNU Emacs and XEmacs mode for interactive statistical programming and data analysis. Languages supported: the S family (S, S-PLUS and R), SAS, BUGS/JAGS, Stata and XLispStat.
... [More] ESS grew out of the desire for bug fixes and extensions to S-mode and SAS-mode as well as a consistent union of their features in one package. [Less]
Copulas for R:
Classes (S4) of commonly used elliptical, Archimedean, extreme value and some more copula families. Methods for density, distribution, random number generation, bivariate dependence measures, perspective and contour plots. Fitting copula models including variance estimates.
... [More] Independence and serial (univariate and multivariate) independence tests, and other copula related tests. Empirical copula and multivariate CDF.
Goodness-of-fit tests for copulas based on multipliers, the parametric bootstrap with several transformation options.
Merged former package 'nacopula' for nested Archimedean copulas: Efficient sampling algorithms, various estimators, goodness-of-fit tests and related
tools and special functions. [Less]
R Package robustbase: "Essential" Robust Statistics. Providing tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics.
Rmpfr provides (S4 classes and methods for)
arithmetic including transcendental ("special") functions for
arbitrary precision floating point numbers. To this end, it interfaces to
the LGPL'ed MPFR (Multiple Precision Floating-Point Reliable) Library
which itself is based on the GMP (GNU Multiple Precision) Library.
Classes and methods for dense and sparse matrices and operations on them using Lapack and SuiteSparse (CHOLMOD etc).
Hierarchical (S4) class system and 1000s of methods, using *multiple* dispatch for most matrix and many other standard R functions.
Extra matrix functionality complementing the basics in "base" R.