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

Flexible Modeling of Count Data

For Bayesian and classical inference and prediction with count-valued data, Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable, and easy-to-use approach. STAR models the observed count data using a rounded continuous data model and incorporates a transformation for greater flexibility. Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of (i) transforming count-valued data and subsequently (ii) modeling the transformed data using Gaussian models. STAR is well-defined for count-valued data, which is reflected in predictive accuracy, and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. Importantly, STAR is easy to combine with existing MCMC or point estimation methods for continuous data, which allows seamless adaptation of continuous data models (such as linear regressions, additive models, BART, random forests, and gradient boosting machines) for count-valued data. The package also includes several methods for modeling count time series data, namely via warped Dynamic Linear Models. For more details and background on these methodologies, see the works of Kowal and Canale (2020) <doi:10.1214/20-EJS1707>, Kowal and Wu (2022) <doi:10.1111/biom.13617>, King and Kowal (2022) <arXiv:2110.14790>, and Kowal and Wu (2023) <arXiv:2110.12316>.