The aim of the package is to provide some basic functions
for doing statistics with one dimensional Fuzzy Data (in the
form of polygonal fuzzy numbers). In particular, the package
contains functions for the basic operations on the class of
fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference)
as well as for calculating (Bertoluzza) distance,
sample variance, sample covariance, and the
Dempster-Shafer (levelwise) histogram. Moreover a function to
simulate fuzzy random variables, bootstrap tests for the
equality of means, and a function to do linear regression given
trapezoidal fuzzy data is included. Version 1.0 fixes some bugs
of previous versions.

The aim of the package is to provide some basic functions for doing statistics with one-dimensional Fuzzy Data (in the form of polygonal fuzzy numbers).

The aim of the package is to provide some basic functions
for doing statistics with one dimensional Fuzzy Data (in the
form of polygonal fuzzy numbers). In particular, the package
contains functions for the basic operations on the class of
fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference)
as well as for calculating (Bertoluzza) distance and sample variance.
Moreover a function to simulate fuzzy random variables and bootstrap tests
for the equality of means is included. Version 2.1 fixes some bugs
of previous versions.

Software for the Analysis of Genetic Architecture
Implements an information theory approach to the analysis of line
cross data providing model averaged results of parameter estimates and
unconditional standard errors.  Also includes a function to provide a
visualization of models space.

Implements an information theory approach to the analysis of line
cross data providing model averaged results of parameter estimates and
unconditional standard errors.  Also includes functions to provide a
visualization of models space, custom plots of multi-model inference results, and traditional
line cross analysis plots.

Clustering via Stochastic Approximation and Gaussian Mixture
Models
Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process.
Rcpp (>= 0.12.13), MixSim, mvtnorm, mclust, stats,
lowmemtkmeans