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r-forge
robustbase:Basic Robust Statistics
"Essential" Robust Statistics. Tools allowing to analyze data with robust methods. This includes regression methodology including model selections and multivariate statistics where we strive to cover the book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Maintained by Martin Maechler. Last updated 4 months ago.
13.38 score 1.7k scripts 480 dependentsr-forge
RobExtremes:Optimally Robust Estimation for Extreme Value Distributions
Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and \doi{10.1007/s00184-011-0366-4}.
Maintained by Peter Ruckdeschel. Last updated 2 months ago.
3.67 score 39 scripts
asunlubiano
FuzzyStatTra:Statistical Methods for Trapezoidal Fuzzy Numbers
The aim of the package is to provide some basic functions for doing statistics with trapezoidal fuzzy numbers. In particular, the package contains several functions for simulating trapezoidal fuzzy numbers, as well as for calculating some central tendency measures (mean and two types of median), some scale measures (variance, ADD, MDD, Sn, Qn, Tn and some M-estimators) and one diversity index and one inequality index. Moreover, functions for calculating the 1-norm distance, the mid/spr distance and the (phi,theta)-wabl/ldev/rdev distance between fuzzy numbers are included, and a function to calculate the value phi-wabl given a sample of trapezoidal fuzzy numbers.
Maintained by Asun Lubiano. Last updated 8 years ago.
1.51 score 32 scripts
bouchranasri
GaussianHMM1d:Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models
Inference, goodness-of-fit test, and prediction densities and intervals for univariate Gaussian Hidden Markov Models (HMM). The goodness-of-fit is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Chapter 10.2 of Remillard (2013) <doi:10.1201/b14285>.
Maintained by Bouchra R. Nasri. Last updated 2 months ago.
1.08 score 12 scripts