This first version of the package includes methods for imputation
of compositional data including robust methods.
The package will be enhanced with other multivariate methods for compositional
data in near future.

The package includes methods for imputation of
compositional data including robust methods, (robust) outlier
detection for compositional data, (robust) principal component
analysis for compositional data, (robust) factor analysis for
compositional data and (robust) Anderson-Darling normality
tests for compositional data as well as popular log-ratio
transformations (alr, clr, ilr, and their inverse
transformations).

The package includes methods for imputation of
compositional data including robust methods, (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) and (robust) Anderson-Darling
normality tests for compositional data as well as popular
log-ratio transformations (alr, clr, ilr, and their inverse
transformations).

R (>= 2.0.0), utils, robustbase, rrcov, car (>= 2.0-0), MASS

R (>= 2.10), utils, robustbase, rrcov, car (>= 2.0-0), MASS

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 (alr, clr, ilr, 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, car (>= 2.0-0), 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 (alr, clr, ilr, 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.