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-brokenstick

Broken Stick Model for Irregular Longitudinal Data

Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See <doi:10.18637/jss.v106.i07> for additional documentation on background, methodology and applications.