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

Contingency Measure-Based Networks for Binary Time Series

The ConNEcT approach investigates the pairwise association strength of binary time series by calculating contingency measures and depicts the results in a network. The package includes features to explore and visualize the data. To calculate the pairwise concurrent or temporal sequenced relationship between the variables, the package provides seven contingency measures (proportion of agreement, classical & corrected Jaccard, Cohen's kappa, phi correlation coefficient, odds ratio, and log odds ratio), however, others can easily be implemented. The package also includes non-parametric significance tests, that can be applied to test whether the contingency value quantifying the relationship between the variables is significantly higher than chance level. Most importantly this test accounts for auto-dependence and relative frequency.See Bodner et al.(2021) <doi: 10.1111/bmsp.12222>.Finally, a network can be drawn. Variables depicted the nodes of the network, with the node size adapted to the prevalence. The association strength between the variables defines the undirected (concurrent) or directed (temporal sequenced) links between the nodes. The results of the non-parametric significance test can be included by depicting either all links or only the significant ones. Tutorial see Bodner et al.(2021) <doi:10.3758/s13428-021-01760-w>.