CaliCo: Code Calibration in a Bayesian Framework

Calibration of every computational code. It uses a Bayesian framework to rule the estimation. With a new data set, the prediction will create a prevision set taking into account the new calibrated parameters. The choices between several models is also available. The methods are described in the paper Carmassi et al. (2018) <arXiv:1801.01810>.

Version: 0.1.0
Imports: R6, ggplot2, FactoMineR, DiceKriging, DiceDesign, MASS, coda, parallel, testthat
LinkingTo: Rcpp, RcppArmadillo, Matrix
Suggests: knitr, rmarkdown
Published: 2018-03-15
Author: Mathieu Carmassi [aut, cre]
Maintainer: Mathieu Carmassi <mathieu.carmassi at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
CRAN checks: CaliCo results

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Reference manual: CaliCo.pdf
Vignettes: Introduction to CaliCo
Package source: CaliCo_0.1.0.tar.gz
Windows binaries: r-prerel: CaliCo_0.1.0.zip, r-release: CaliCo_0.1.0.zip, r-oldrel: CaliCo_0.1.0.zip
OS X binaries: r-prerel: CaliCo_0.1.0.tgz, r-release: CaliCo_0.1.0.tgz

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