Title: Hierarchical Bayesian Space-Time models for Gaussian space-time
Author: Pilar Munyoz, Alberto Lopez Moreno
Maintainer: Alberto Lopez Moreno <bertolomo@gmail.com>
Description: This package fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.

Title: Hierarchical Bayesian Space-Time Models for Gaussian Space-Time
Description: Fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.
Depends: methods, MASS, fBasics, maps, stats, graphics,grDevices,utils

Depends: methods, MASS, msm, fBasics, maps

Title: Decoupled Hydrological Model for Research and Education Purposes
Description: The HBV (Hydrologiska Byråns Vattenbalansavdelning) hydrological model is decoupled to allow the user
to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia
y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA
incorporates modules for precipitation and temperature interpolation,  and also for clean and debris covered ice melt estimations.

Description: The HBV hydrological model (Bergström, S. and Lindström, G., (2015) <doi:10.1002/hyp.10510>) has been split in modules to allow the user to build his/her own model. This version was developed by the author in IANIGLA-CONICET (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales - Consejo Nacional de Investigaciones Cientificas y Tecnicas) for hydroclimatic studies in the Andes. HBV.IANIGLA incorporates routines for clean and debris covered glacier melt simulations.

Title: Hierarchical Bayesian Regression for GLMs
Imports: Formula, bayesm, sns, MfUSampler, stats
Description: Convenient and efficient functions for performing 2-level hierarchical Bayesian regression analysis for multi-group data. The lowest level may belong to the generalized linear model (GLM) family while the prior level, which effects pooling, allows for linear regression on lower level covariates. Constraints on all or part of the parameter set maybe specified with ease. A rich set of methods is included to visualize and analyze results.