ESTER: Efficient Sequential Testing with Evidence Ratios

An implementation of sequential testing that uses evidence ratios computed from the Akaike weights of a set of models. These weights are being computed using either the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC), and following Burnham & Anderson (2004) recommendations. Burnham, K. P., & Anderson, D. R. (2004). Multimodel inference: Understanding AIC and BIC in model selection. Sociological Methods and Research, 33(2), 261-304. <doi:10.1177/0049124104268644>.

Version: 0.1.0
Depends: R (≥ 3.3.0)
Imports: lme4, dplyr, magrittr, ggplot2, rlang
Suggests: knitr, rmarkdown
Published: 2017-10-16
Author: Ladislas Nalborczyk [aut, cre]
Maintainer: Ladislas Nalborczyk <ladislas.nalborczyk at gmail.com>
BugReports: https://github.com/lnalborczyk/ESTER/issues
License: MIT + file LICENSE
URL: https://github.com/lnalborczyk/ESTER
NeedsCompilation: no
Materials: README
CRAN checks: ESTER results

Downloads:

Reference manual: ESTER.pdf
Vignettes: Efficient Sequential Testing with Evidence Ratios
Package source: ESTER_0.1.0.tar.gz
Windows binaries: r-devel: ESTER_0.1.0.zip, r-release: ESTER_0.1.0.zip, r-oldrel: ESTER_0.1.0.zip
OS X El Capitan binaries: r-release: ESTER_0.1.0.tgz
OS X Mavericks binaries: r-oldrel: ESTER_0.1.0.tgz

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