devel:languages:R:autoCRAN

Large parts of CRAN (cran.r-project.org) mirrored to OBS in a fully automatic way.

This repo contains a large part of CRAN automatically converted to rpm packages. *ALL* packages in the repo are created and kept uptodate(!) in a fully automatic way using the R package CRAN2OBS (gitlab.com/dsteuer/CRAN2OBS). At the moment CRAN2OBS is still subject to many changes, but it already works well enough to bring about 15k packages from CRAN to Suse. If you find packages not working, please contact me. Do not push packages here by hand after manually altering anything in a spec file, please. If you find an important package still missing, send a note, please. May be it is easy to add fitting rules to the scripts. Attention: there are Prefer: lines in the project config. Should be rechecked from time to time. https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/ devel:languages:R:autoCRANsupp

Supplements for the autoCRAN project

autoCRANsupp contains *only* - libraries needed to build a worthy number of R packages that are not in factory/tumbleweed, i.e. udunits2-1 - a link to d:l:R:released/R-base to provide newer versions for older SuSE releases. A lot of packages need the latest R. This project will be as small as possible. In a best case scenario only R-base will remain here to be included for building autoCRAN. https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRANsupp/openSUSE_Tumbleweed/ openSUSE:Factory

The next openSUSE distribution

openSUSE Tumbleweed: The Bleeding Edge, Perfected. Tumbleweed is the ultimate rolling release distribution, providing the latest software as it’s released, built upon a foundation of world-class stability and testing. * Always Current: Get the newest kernel, IDEs, desktops, and applications automatically. * Powerfully Stable: Experience the velocity of a rolling release without sacrificing the reliability you depend on. * Engineered for Professionals: The top choice for Developers, Power Users, and openSUSE Contributors who need the best tools for the job. If you demand the latest stable software, your choice is Tumbleweed. Staging dashboard is located at: https://build.opensuse.org/staging_workflows/openSUSE:Factory List of known devel projects: https://build.opensuse.org/package/view_file/openSUSE:Factory:Staging/dashboard/devel_projects Have a look at http://en.opensuse.org/Portal:Factory for more details. https://download.opensuse.org/tumbleweed/repo/oss/ openSUSE:Tumbleweed

Tumbleweed

Tumbleweed is the openSUSE Rolling Release This OBS Project represents the content of the currently published snapshot. The newer repository for next publish can be found in openSUSE:Factory standard repository. https://download.opensuse.org/repositories/openSUSE:/Tumbleweed/standard/ openSUSE:Tumbleweed

Tumbleweed

The next openSUSE distribution

Bayesian Analyses for One- and Two-Sample Inference and Regression Methods

Perform fundamental analyses using Bayesian parametric and non-parametric inference (regression, anova, 1 and 2 sample inference, non-parametric tests, etc.). (Practically) no Markov chain Monte Carlo (MCMC) is used; all exact finite sample inference is completed via closed form solutions or else through posterior sampling automated to ensure precision in interval estimate bounds. Diagnostic plots for model assessment, and key inferential quantities (point and interval estimates, probability of direction, region of practical equivalence, and Bayes factors) and model visualizations are provided. Bayes factors are computed either by the Savage Dickey ratio given in Dickey (1971) <doi:10.1214/aoms/1177693507> or by Chib's method as given in xxx. Interpretations are from Kass and Raftery (1995) <doi:10.1080/01621459.1995.10476572>. ROPE bounds are based on discussions in Kruschke (2018) <doi:10.1177/2515245918771304>. Methods for determining the number of posterior samples required are described in Doss et al. (2014) <doi:10.1214/14-EJS957>. Bayesian model averaging is done in part by Feldkircher and Zeugner (2015) <doi:10.18637/jss.v068.i04>. Methods for contingency table analysis is described in Gunel et al. (1974) <doi:10.1093/biomet/61.3.545>. Variational Bayes (VB) methods are described in Salimans and Knowles (2013) <doi:10.1214/13-BA858>. Mediation analysis uses the framework described in Imai et al. (2010) <doi:10.1037/a0020761>. The loss-likelihood bootstrap used in the non-parametric regression modeling is described in Lyddon et al. (2019) <doi:10.1093/biomet/asz006>. Non-parametric survival methods are described in Qing et al. (2023) <doi:10.1002/pst.2256>. Methods used for the Bayesian Wilcoxon signed-rank analysis is given in Chechile (2018) <doi:10.1080/03610926.2017.1388402> and for the Bayesian Wilcoxon rank sum analysis in Chechile (2020) <doi:10.1080/03610926.2018.1549247>. Correlation analysis methods are carried out by Barch and Chechile (2023) <doi:10.32614/CRAN.package.DFBA>, and described in Lindley and Phillips (1976) <doi:10.1080/00031305.1976.10479154> and Chechile and Barch (2021) <doi:10.1016/j.jmp.2021.102638>. See also Chechile (2020, ISBN: 9780262044585).