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

Excess Hazard Modelling Considering Inappropriate Mortality Rates

Fits relative survival regression models with or without proportional excess hazards and with the additional possibility to correct for background mortality by one or more parameter(s). These models are relevant when the observed mortality in the studied group is not comparable to that of the general population or in population-based studies where the available life tables used for net survival estimation are insufficiently stratified. In the latter case, the proposed model by Touraine et al. (2020) <doi:10.1177/0962280218823234> can be used. The user can also fit a model that relaxes the proportional expected hazards assumption considered in the Touraine et al. excess hazard model. This extension was proposed by Mba et al. (2020) <doi:10.1186/s12874-020-01139-z> to allow non-proportional effects of the additional variable on the general population mortality. In non-population-based studies, researchers can identify non-comparability source of bias in terms of expected mortality of selected individuals. An excess hazard model correcting this selection bias is presented in Goungounga et al. (2019) <doi:10.1186/s12874-019-0747-3>. This class of model with a random effect at the cluster level on excess hazard is presented in Goungounga et al. (2023) <doi:10.1002/bimj.202100210>.