Robust Linear Regression with Compositional Data as Covariates

Robust regression methods for compositional data.
The distribution of the estimates can be approximated with various bootstrap
methods. These bootstrap methods are available for the compositional as well
as for standard robust regression estimates. This allows for direct
comparison between them.

Provides routines for density and moments of the
Conway-Maxwell-Poisson distribution as well as functions for
fitting the COM-Poisson model for over/under-dispersed count
data.
Orphaned on 2020-02-02 as no response to requested
deprecated licence update.

K. Gerald van den Boogaart <boogaart@uni-greifswald.de>, Raimon Tolosana
K. Gerald van den Boogaart <boogaart@uni-greifswald.de>
The package provides functions for the consistent analysis of
compositional data (e.g. portions of substances) and positive numbers
(e.g. concentrations) in the way proposed by Aitchison.

K. Gerald van den Boogaart <boogaart@uni-greifswald.de>,
Raimon Tolosana, with contributions of Matevz Bren

K. Gerald van den Boogaart <boogaart@uni-greifswald.de>,
Raimon Tolosana, Matevz Bren
The package provides functions for the consistent analysis of
compositional data (e.g. portions of substances) and positive numbers
(e.g. concentrations) in the way proposed by Aitchison and Pawlowsky-Glahn.

K. Gerald van den Boogaart <support@boogaart.de>,
Raimon Tolosana, Matevz Bren
K. Gerald van den Boogaart <support@boogaart.de>

K. Gerald van den Boogaart <support@boogaart.de>, Raimon
Tolosana, Matevz Bren
R (>= 2.2.0), rgl, tensorA, robustbase, energy
The package provides functions for the consistent analysis
of compositional data (e.g. portions of substances) and
positive numbers (e.g. concentrations) in the way proposed by
Aitchison and Pawlowsky-Glahn.

K. Gerald van den Boogaart <boogaart@hzdr.de>,
Raimon Tolosana, Matevz Bren
R (>= 2.2.0), tensorA, robustbase, energy, bayesm

K. Gerald van den Boogaart <boogaart@hzdr.de>,
Raimon Tolosana-Delgado, Matevz Bren
Provides functions for the consistent analysis of compositional
data (e.g. portions of substances) and positive numbers (e.g. concentrations)
in the way proposed by J. Aitchison and V. Pawlowsky-Glahn.