The following are some R packages I have developed and uploaded to CRAN. Click on the package name to go to the corresponding page on CRAN.
welchADF
WelchJames Statistic for Robust Hypothesis Testing under Heterocedasticity and NonNormality
Implementation of Johansen's general formulation of WelchJames's statistic with Approximate Degrees of Freedom, which makes it
suitable for testing any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the
data pose nonnormality and nonhomogeneous variance. Some improvements, namely trimmed means and Winsorized variances, and
bootstrapping for calculating an empirical critical value, have been added to the classical formulation. The code departs from
a previous SAS implementation available here, presented in:
[1] Keselman, H.J., Wilcox, R.R., and Lix, L.M. A generally robust approach to hypothesis testing in independent and correlated groups designs.
Psychophysiology 40(4):586  596 (2003).
Related publications:

The welchADF Package for Robust Hypothesis Testing in Unbalanced Multivariate Mixed Models with Heteroscedastic and Nonnormal Data
[Link to the journal]
P.J. Villacorta. The R Journal 9 (2), pp. 309328, 2017
MultinomialCI
Simultaneous confidence intervals for multinomial proportions according to the method by Sison and Glaz
An implementation of a method for building simultaneous confidence intervals for the probabilities of a multinomial
distribution given a set of observations, presented in:
[1] Sison, C.P and J. Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions.
Journal of the American Statistical Association, 90:366369 (1995).
[2] May, W.L. and W.D. Johnson. Constructing twosided simultaneous confidence intervals for multinomial proportions
for small counts in a large number of cells. Journal of Statistical Software 5(6) (2000).
FuzzyStatProb
Fuzzy stationary probabilities from a sequence of observations of an unknown Markov chain
Related publications:
 FuzzyStatProb: an R package for the estimation of fuzzy stationary probabilities
from a sequence of observations of an unknown Markov chain
[Link to the journal]
P.J. Villacorta and J.L. Verdegay. Journal of Statistical Software 71(8), pp. 127 (2016).  Towards fuzzy linguistic Markov chains
P.J. Villacorta, J.L. Verdegay and D.A. Pelta
Proceedings of the 8th Conf. of the European Society for Fuzzy Logic and Technology (EUSFLAT 2013). Milano (Italy), september 11  13, 2013. [Atlantis Press Proceedings]
ART
Aligned Rank Transform for nonparametric factorial analysis
An implementation of the Aligned Rank Transform technique for factorial analysis presented in
[1] Higgins, J. J. and Tashtoush, S. An aligned rank transform test for interaction. Nonlinear World 1(2), pp. 201211 (1994).
[2] Wobbrock, J.O., Findlater, L., Gergle, D. and Higgins, J.J. The Aligned Rank Transform for nonparametric factorial
analyses using only ANOVA procedures. Proc. of CHI 2011, pp. 143146 (2011).
SRCS
Statistical Ranking Color Scheme for Multiple Pairwise Comparisons
Related publications:

SRCS: A Technique for Comparing Multiple Algorithms under Several Factors in Dynamic Optimization Problems
I.G. del Amo and D.A. Pelta. In: E. Alba, A. Nakib, P. Siarry (Eds.), Metaheuristics for Dynamic Optimization, pp. 66  77. Series: Studies in Computational Intelligence 433, Springer, Berlin/Heidelberg (2012).  SRCS: Statistical Ranking Color Scheme for Visualizing Parameterized Multiple Pairwise Comparisons with R
P.J. Villacorta and J.A. Sáez. The R Journal 7(2), pp. 89104 (2015).
Standalone script to create a video sequence here (requires ImageMagick software installed).
NOTE: Under Linux, adjust the path.to.converter argument to (most likely) /usr/bin/convert
Standalone script and datasets to generate the data of the Machine Learning experiment in R here.
FuzzyLP
Fuzzy Linear Programming in R
Related publications:

FuzzyLP: an R package for solving Fuzzy Linear Programming problems
P.J. Villacorta, C.A. Rabelo, D.A. Pelta and J.L. Verdegay
In: J. Kacprzyk et al. (eds.). Granular, Soft and Fuzzy Approaches for Intelligent Systems. Series: Studies in Fuzziness and Soft Computing 344, pp. 209  230. Springer International Publishing Switzerland (2017).