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/15.6/ deleted

INTERNAL PROJECT

don't delete this project, it's used for internal purposes https://download.opensuse.org/repositories/deleted/deleted/ openSUSE:Leap:15.6

openSUSE Leap borrows packages from SLE. The content of the build media is almost the same as the previous Leap, but the development is drastic different. It includes the binaries (instead of the sources) directly from SLE. https://lists.opensuse.org/opensuse-factory/2020-04/msg00165.html https://download.opensuse.org/repositories/openSUSE:/Leap:/15.6/standard/ openSUSE:Backports:SLE-15-SP6

Backports project for SLE-15-SP6

Backports project for SLE-15-SP6 https://download.opensuse.org/repositories/openSUSE:/Backports:/SLE-15-SP6/standard/ SUSE:SLE-15-SP6:GA

https://download.opensuse.org/repositories/SUSE:/SLE-15-SP6:/GA/pool/ SUSE:SLE-15-SP5:Update

SLE 15 SP5

SLE 15 SP5 https://download.opensuse.org/distribution/leap/15.6/repo/oss/ SUSE:SLE-15-SP5:GA

https://download.opensuse.org/repositories/SUSE:/SLE-15-SP5:/GA/pool/ SUSE:SLE-15-SP4:Update

SLE 15 SP4

SLE 15 SP4 https://download.opensuse.org/distribution/leap/15.6/repo/oss/ SUSE:SLE-15-SP4:GA

https://download.opensuse.org/repositories/SUSE:/SLE-15-SP4:/GA/pool/ SUSE:SLE-15-SP3:Update

SLE 15 SP3

SLE 15 SP3 https://download.opensuse.org/distribution/leap/15.6/repo/oss/ SUSE:SLE-15-SP3:GA

https://download.opensuse.org/repositories/SUSE:/SLE-15-SP3:/GA/pool/ SUSE:SLE-15-SP2:Update

SLE 15 SP2

SLE 15 SP2 https://download.opensuse.org/distribution/leap/15.6/repo/oss/ SUSE:SLE-15-SP2:GA

SLE 15 SP2

SLE 15 SP2 https://download.opensuse.org/repositories/SUSE:/SLE-15-SP2:/GA/pool/ SUSE:SLE-15-SP1:Update

SLE 15 SP1

SLE 15 SP1 https://download.opensuse.org/distribution/leap/15.6/repo/oss/ SUSE:SLE-15-SP1:GA

SLE 15 SP1

SLE 15 SP1 https://download.opensuse.org/repositories/SUSE:/SLE-15-SP1:/GA/pool/ SUSE:SLE-15:Update

SLE 15

SLE 15 https://download.opensuse.org/distribution/leap/15.6/repo/oss/ SUSE:SLE-15:GA

SLE 15

SLE 15 https://download.opensuse.org/repositories/SUSE:/SLE-15:/GA/pool/ R-bayespm

Bayesian Statistical Process Monitoring

The R-package bayespm implements Bayesian Statistical Process Control and Monitoring (SPC/M) methodology. These methods utilize available prior information and/or historical data, providing efficient online quality monitoring of a process, in terms of identifying moderate/large transient shifts (i.e., outliers) or persistent shifts of medium/small size in the process. These self-starting, sequentially updated tools can also run under complete absence of any prior information. The Predictive Control Charts (PCC) are introduced for the quality monitoring of data from any discrete or continuous distribution that is a member of the regular exponential family. The Predictive Ratio CUSUMs (PRC) are introduced for the Binomial, Poisson and Normal data (a later version of the library will cover all the remaining distributions from the regular exponential family). The PCC targets transient process shifts of typically large size (a.k.a. outliers), while PRC is focused in detecting persistent (structural) shifts that might be of medium or even small size. Apart from monitoring, both PCC and PRC provide the sequentially updated posterior inference for the monitored parameter. Bourazas K., Kiagias D. and Tsiamyrtzis P. (2022) "Predictive Control Charts (PCC): A Bayesian approach in online monitoring of short runs" <doi:10.1080/00224065.2021.1916413>, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs" <doi:10.1080/00224065.2022.2161434>, Bourazas K., Sobas F. and Tsiamyrtzis, P. 2023. "Design and properties of the predictive ratio cusum (PRC) control charts" <doi:10.1080/00224065.2022.2161435>.