Title: Bayesian Estimation in Generalized Linear Models
Author: Nicolas Molano-Gonzalez, Edilberto Cepeda-Cuervo
Description: Implementation of Bayesian estimation in generalized linear models following Gamerman (1997).

Title: Example BGM Files for the Atlantis Ecosystem Model
person("Michael D.", "Sumner", , "mdsumner@gmail.com", c("aut", "cre"))
Description: A collection of box-geometry model (BGM) files for the Atlantis ecosystem model. Atlantis is a deterministic, biogeochemical, whole-of-ecosystem model.

Description: A collection of box-geometry model (BGM) files for the Atlantis
ecosystem model. Atlantis is a deterministic, biogeochemical,
whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more information).

Title: Gaussian Mixture Modeling algorithms. Including the belief-based
Author: Przemyslaw Biecek \& Ewa Szczurek
Depends: R (>= 2.0), mvtnorm, car, lattice, combinat

Package: bgmm
Type: Package
Title: Gaussian Mixture Modeling algorithms. Including the belief-based
mixture modeling.
Version: 1.5
Date: 2012-03-15
Author: Przemyslaw Biecek \& Ewa Szczurek
Description: The bgmm package implements two partially supervised
mixture modeling methods: soft-label and belief-based modeling.
For completeness, we equipped the package also with the
functionality of unsupervised, semi- and fully supervised
mixture modeling.  The package can be applied also to selection
of the best-fitting from a set of models with different
component numbers or constraints on their structures.
Reference: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
Tiuryn (2012), The R Package bgmm: Mixture Modeling with
Uncertain Knowledge, Journal of Statistical Software.
Maintainer: Przemyslaw Biecek <P.Biecek@mimuw.edu.pl>
Depends: R (>= 2.0), mvtnorm, car, lattice, combinat
URL: http://bgmm.molgen.mpg.de/
License: GPL-3
Repository: CRAN
Packaged: 2012-03-29 14:47:00 UTC; pbiecek
Date/Publication: 2012-03-29 16:45:06

Package: bgmm
Type: Package
Title: Gaussian Mixture Modeling algorithms. Including the belief-based
mixture modeling.
Version: 1.6
Date: 2013-11-18
Author: Przemyslaw Biecek \& Ewa Szczurek
Description: The bgmm package implements two partially supervised
mixture modeling methods: soft-label and belief-based modeling.
For completeness, we equipped the package also with the
functionality of unsupervised, semi- and fully supervised
mixture modeling.  The package can be applied also to selection
of the best-fitting from a set of models with different
component numbers or constraints on their structures.
For detailed introduction see:
Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
Tiuryn (2012), The R Package bgmm: Mixture Modeling with
Uncertain Knowledge, Journal of Statistical Software.
Maintainer: Przemyslaw Biecek <P.Biecek@mimuw.edu.pl>
Depends: R (>= 2.0), mvtnorm, car, lattice, combinat
URL: http://bgmm.molgen.mpg.de/
License: GPL-3
Repository: CRAN
Packaged: 2013-11-18 23:46:06 UTC; Przemyslaw
Date/Publication: 2013-11-19 07:39:02
NeedsCompilation: no