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/15.5/ deleted

INTERNAL PROJECT

don't delete this project, it's used for internal purposes https://download.opensuse.org/repositories/deleted/deleted/ openSUSE:Leap:15.5

openSUSE Leap borrows packages from SLE. The content of the build media is almost the same as Leap:15.2, 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:/15.5/standard/ openSUSE:Backports:SLE-15-SP5

Backports project for SLE-15-SP5

Backports project for SLE-15-SP5 https://download.opensuse.org/repositories/openSUSE:/Backports:/SLE-15-SP5/standard/ 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/15.5/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/15.5/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/15.5/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/15.5/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/15.5/repo/oss/ SUSE:SLE-15:GA

SLE 15

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

Calibration of Computer-Coded Verbal Autopsy Algorithm

Calibrates cause-specific mortality fractions (CSMF) estimates generated by computer-coded verbal autopsy (CCVA) algorithms from WHO-standardized verbal autopsy (VA) survey data. It leverages data from the multi-country Child Health and Mortality Prevention Surveillance (CHAMPS) project <https://champshealth.org/>, which determines gold standard causes of death via Minimally Invasive Tissue Sampling (MITS). By modeling the CHAMPS data using the misclassification matrix modeling framework proposed in Pramanik et al. (2025, <doi:10.1214/24-AOAS2006>), the package includes an inventory of 48 uncertainty-quantified misclassification matrices for three CCVA algorithms (EAVA, InSilicoVA, InterVA), two age groups (neonates aged 0-27 days and children aged 1-59 months), and eight "countries" (seven countries in CHAMPS -- Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, South Africa -- and an estimate for countries not in CHAMPS). Given a VA-only data for an age group, CCVA algorithm, and country, the package uses the corresponding uncertainty-quantified misclassification matrix estimates as an informative prior, and utilizes the modular VA-calibration to produce calibrated CSMF estimates. It also supports ensemble calibration when VA-only data are provided for multiple algorithms. More generally, the package can be applied to calibrate predictions from a discrete classifier (or ensemble of classifiers) utilizing user-provided fixed or uncertainty-quantified misclassification matrices. This work is supported by the Bill and Melinda Gates Foundation Grant INV-034842.