# Peter Ruckdeschel

#### 19 packages on CRAN

Provides documentation in form of a common vignette to packages 'distr', 'distrEx', 'distrMod', 'distrSim', 'distrTEst', 'distrTeach', and 'distrEllipse'.

Distribution (S4-)classes for elliptically contoured distributions (based on package 'distr').

S4-distribution classes based on package distr for distributions from packages 'fBasics' and 'fGarch'.

S4-classes for setting up a coherent framework for simulation within the distr family of packages.

Provides flexible examples of LLN and CLT for teaching purposes in secondary school.

Evaluation (S4-)classes based on package distr for evaluating procedures (estimators/tests) at data/simulation in a unified way.

Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is currently used by package 'RobExtremes' only.

Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst').

Provides utilities for defining R / Rd as "language" for TeX-package "listings" and for including R / Rd source file (snippets) copied from R-forge in its most recent version (or another URL) thereby avoiding inconsistencies between vignette and documented source code.

An R6 object oriented distributions package. Unified interface for 42 probability distributions and 11 kernels including functionality for multiple scientific types. Additionally functionality for composite distributions and numerical imputation. Design patterns including wrappers and decorators are described in Gamma et al. (1994, ISBN:0-201-63361-2). For quick reference of probability distributions including d/p/q/r functions and results we refer to McLaughlin, M. P. (2001). Additionally Devroye (1986, ISBN:0-387-96305-7) for sampling the Dirichlet distribution, Gentle (2009) <doi:10.1007/978-0-387-98144-4> for sampling the Multivariate Normal distribution and Michael et al. (1976) <doi:10.2307/2683801> for sampling the Wald distribution.

Functions for the determination of optimally robust influence curves and estimators in case of normal location and/or scale.

Optimally robust estimation in general smoothly parameterized models using S4 classes and methods.