r-cran-brms 2.10.0-1 source package in Ubuntu
Changelog
r-cran-brms (2.10.0-1) unstable; urgency=medium * New upstream version * dh-update-R to update Build-Depends * Set upstream metadata fields: Archive, Bug-Database, Repository. * Remove obsolete fields Name, Contact from debian/upstream/metadata. * Add Test-Depends: r-cran-splines2 -- Andreas Tille <email address hidden> Thu, 19 Sep 2019 17:24:47 +0200
Upload details
- Uploaded by:
- Debian R Packages Maintainers
- Uploaded to:
- Sid
- Original maintainer:
- Debian R Packages Maintainers
- Architectures:
- all
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| r-cran-brms_2.10.0-1.dsc | 2.4 KiB | c3c7b74baa7da6824dcc72217be0c12c5203385b8c746d06bd7f2d1c4194860c |
| r-cran-brms_2.10.0.orig.tar.gz | 4.1 MiB | 90466f45179142a9b7bafa6745ae1831a402786bd80856a409638694777c6983 |
| r-cran-brms_2.10.0-1.debian.tar.xz | 2.7 KiB | cf4dab6a34d89706ba1ef618bf24845dc51842496f0011448972c92b25a26f5a |
No changes file available.
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.
