The package includes methods for imputation
of compositional data including robust methods, methods to impute rounded zeros,
(robust) outlier detection for compositional data,
(robust) principal component
analysis for compositional data, (robust) factor analysis for compositional
data, (robust) discriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors and (robust)
Anderson-Darling normality tests
for compositional data as well as popular log-ratio transformations (addLR, cenLR, isomLR,
and their inverse transformations).
In addition, visualisation and diagnostic tools are implemented as well as high and low-level plot functions for the
ternary diagram.

R (>= 2.10), utils, robustbase, rrcov, GGally, MASS, pls
The package includes methods for imputation of compositional data
including robust methods, methods to impute rounded zeros, (robust) outlier
detection for compositional data, (robust) principal component analysis for
compositional data, (robust) factor analysis for compositional data,
(robust) discriminant analysis for compositional data (Fisher rule), robust
regression with compositional predictors and (robust) Anderson-Darling
normality tests for compositional data as well as popular log-ratio
transformations (addLR, cenLR, isomLR, and their inverse transformations).
In addition, visualisation and diagnostic tools are implemented as well as
high and low-level plot functions for the ternary diagram.

Robust Estimation for Compositional Data
Methods for analysis of compositional data
including robust methods, imputation, methods to replace rounded zeros, (robust)
outlier detection for compositional data, (robust) principal component analysis for
compositional data, (robust) factor analysis for compositional data,
(robust) discriminant analysis for compositional data (Fisher rule), robust
regression with compositional predictors and (robust) Anderson-Darling
normality tests for compositional data as well as popular log-ratio
transformations (addLR, cenLR, isomLR, and their inverse transformations).
In addition, visualisation and diagnostic tools are implemented as well as
high and low-level plot functions for the ternary diagram.