r-cran-brms 2.12.0-2build1 source package in Ubuntu

Changelog

r-cran-brms (2.12.0-2build1) groovy; urgency=medium

  * No-change rebuild against r-api-4.0

 -- Steve Langasek <email address hidden>  Sun, 31 May 2020 06:12:11 +0000

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Uploaded by:
Steve Langasek
Uploaded to:
Groovy
Original maintainer:
Ubuntu Developers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

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Series Pocket Published Component Section

Builds

Groovy: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
r-cran-brms_2.12.0.orig.tar.gz 4.8 MiB fa21505dca65d027f1cf1c573258de5f3c51ca8b94abd6dcf9123a3a27a72999
r-cran-brms_2.12.0-2build1.debian.tar.xz 3.1 KiB acf6882ae48bc7ccc5c0c68f704496f088dbe47dae1f352d5b226ab36c34ab44
r-cran-brms_2.12.0-2build1.dsc 2.5 KiB c165e0ce6b545a0e5afcc26516ace8b27bae84fc0ed77232da3469f2d0fd4b89

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Binary packages built by this source

r-cran-brms: GNU R Bayesian regression models using 'Stan'

 Fit Bayesian generalized (non-)linear multivariate multilevel models
 using 'Stan' for full Bayesian inference. A wide range of distributions
 and link functions are supported, allowing users to fit -- among others
  -- linear, robust linear, count data, survival, response times, ordinal,
 zero-inflated, hurdle, and even self-defined mixture models all in a
 multilevel context. Further modeling options include non-linear and
 smooth terms, auto-correlation structures, censored data, meta-analytic
 standard errors, and quite a few more. In addition, all parameters of
 the response distribution can be predicted in order to perform
 distributional regression. Prior specifications are flexible and
 explicitly encourage users to apply prior distributions that actually
 reflect their beliefs. Model fit can easily be assessed and compared
 with posterior predictive checks and leave-one-out cross-validation.