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r-cran-mcmcpack package

r-cran-mcmcpack 1.4-6-1 source package in Ubuntu

Changelog

r-cran-mcmcpack (1.4-6-1) unstable; urgency=medium

  * Team upload.
  * New upstream version
  * Standards-Version: 4.5.0 (routine-update)

 -- Andreas Tille <email address hidden>  Wed, 19 Feb 2020 19:09:41 +0100

Upload details

Uploaded by:
Debian R Packages Maintainers
Uploaded to:
Sid
Original maintainer:
Debian R Packages Maintainers
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Focal release universe misc

Downloads

File Size SHA-256 Checksum
r-cran-mcmcpack_1.4-6-1.dsc 2.1 KiB ec9c000d9c4ed3c0ed92e4e1d07beb598d2719da8eef0c4c3a0bad066b1e386a
r-cran-mcmcpack_1.4-6.orig.tar.gz 663.6 KiB 6bcd018d6fa589a6854ee1bcea18b9d6c4095f3deae9058f69afbb09cba873c7
r-cran-mcmcpack_1.4-6-1.debian.tar.xz 4.7 KiB 6d97362c5e70bb7d4a76aff8da3a5e9c94024ed43fac6958f79739d5141991de

Available diffs

No changes file available.

Binary packages built by this source

r-cran-mcmcpack: R routines for Markov chain Monte Carlo model estimation

 This is a set of routines for GNU R that implement various
 statistical and econometric models using Markov chain Monte Carlo
 (MCMC) estimation, which allows "solving" models that would otherwise
 be intractable with traditional techniques, particularly problems in
 Bayesian statistics (where one or more "priors" are used as part of
 the estimation procedure, instead of an assumption of ignorance about
 the "true" point estimates), although MCMC can also be used to solve
 frequentist statistical problems with uninformative priors. MCMC
 techniques are also preferable over direct estimation in the presence
 of missing data.
 .
 Currently implemented are a number of ecological inference (EI)
 routines (for estimating individual-level attributes or behavior from
 aggregate data, such as electoral returns or census results), as well
 as models for traditional linear panel and cross-sectional data, some
 visualization routines for EI diagnostics, two item-response theory
 (or ideal-point estimation) models, metric, ordinal, and
 mixed-response factor analysis, and models for Gaussian (linear) and
 Poisson regression, logistic regression (or logit), and binary and
 ordinal-response probit models.
 .
 The suggested packages (r-cran-bayesm, -eco, and -mnp) contain
 additional models that may also be useful for those interested in
 this package.

r-cran-mcmcpack-dbgsym: debug symbols for r-cran-mcmcpack