[ ソース: r-cran-bridgesampling ]
パッケージ: r-cran-bridgesampling (1.1-2-1) [universe]
r-cran-bridgesampling に関するリンク
Ubuntu の資源:
r-cran-bridgesampling ソースパッケージをダウンロード:
- [r-cran-bridgesampling_1.1-2-1.dsc]
- [r-cran-bridgesampling_1.1-2.orig.tar.gz]
- [r-cran-bridgesampling_1.1-2-1.debian.tar.xz]
メンテナ:
Please consider filing a bug or asking a question via Launchpad before contacting the maintainer directly.
Original Maintainers (usually from Debian):
- Debian R Packages Maintainers
- Andreas Tille
It should generally not be necessary for users to contact the original maintainer.
外部の資源:
- ホームページ [cran.r-project.org]
類似のパッケージ:
GNU R bridge sampling for marginal likelihoods and Bayes factors
その他の r-cran-bridgesampling 関連パッケージ
|
|
|
|
-
- dep: r-api-4.0
- 以下のパッケージによって提供される仮想パッケージです: r-base-core
-
- dep: r-base-core (>= 4.1.1-2)
- GNU R core of statistical computation and graphics system
-
- dep: r-cran-brobdingnag
- Very Large Numbers in R
-
- dep: r-cran-coda
- Output analysis and diagnostics for MCMC simulations in R
-
- dep: r-cran-matrix
- GNU R package of classes for dense and sparse matrices
-
- dep: r-cran-mvtnorm
- GNU R package to compute multivariate Normal and T distributions
-
- dep: r-cran-scales
- Scale functions for visualization
-
- dep: r-cran-stringr
- Make it easier to work with strings
-
- rec: r-cran-testthat
- GNU R testsuite
-
- sug: r-cran-bayesfactor
- GNU R Bayes factors for t-tests, ANOVAs and contingency tables
-
- sug: r-cran-knitr
- GNU R package for dynamic report generation using Literate Programming
-
- sug: r-cran-mcmcpack
- R routines for Markov chain Monte Carlo model estimation
-
- sug: r-cran-rcpp
- GNU R package for Seamless R and C++ Integration
-
- sug: r-cran-rcppeigen
- GNU R package for Eigen templated linear algebra
-
- sug: r-cran-rjags
- R interface to the JAGS Bayesian statistics package
-
- sug: r-cran-rmarkdown
- convert R markdown documents into a variety of formats
-
- sug: r-cran-rstan
- GNU R interface to Stan
-
- sug: r-cran-rstanarm
- GNU R bayesian applied regression modeling via stan