<metapackage xmlns:os="http://opensuse.org/Standards/One_Click_Install" xmlns="http://opensuse.org/Standards/One_Click_Install">
  <group distversion="openSUSE Tumbleweed">
    <repositories>
      <repository recommended="true">
        <name>devel:languages:R:autoCRAN</name>
        <summary>Large parts of CRAN (cran.r-project.org) mirrored to OBS in a fully automatic way.</summary>
        <description>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.</description>
        <url>https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/</url>
      </repository>
      <repository recommended="true">
        <name>devel:languages:R:autoCRANsupp</name>
        <summary>Supplements for the autoCRAN project</summary>
        <description>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.

</description>
        <url>https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRANsupp/openSUSE_Tumbleweed/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE:Factory</name>
        <summary>The next openSUSE distribution</summary>
        <description>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.</description>
        <url>https://download.opensuse.org/tumbleweed/repo/oss/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE:Tumbleweed</name>
        <summary>Tumbleweed</summary>
        <description>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.
</description>
        <url>https://download.opensuse.org/repositories/openSUSE:/Tumbleweed/standard/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE:Tumbleweed</name>
        <summary>Tumbleweed</summary>
        <description>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.
</description>
        <url>https://download.opensuse.org/tumbleweed/repo/oss/</url>
      </repository>
      <repository recommended="false">
        <name>openSUSE:Factory</name>
        <summary>The next openSUSE distribution</summary>
        <description>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.</description>
        <url>https://download.opensuse.org/repositories/openSUSE:/Factory/ports/</url>
      </repository>
    </repositories>
    <software>
      <item>
        <name>R-mable</name>
        <summary>Maximum Approximate Bernstein/Beta Likelihood Estimation</summary>
        <description>Fit data from a continuous population with a smooth density on finite
interval by an approximate Bernstein polynomial model which is a
mixture of certain beta distributions and find maximum approximate
Bernstein likelihood estimator of the unknown coefficients.
Consequently, maximum likelihood estimates of the unknown density,
distribution functions, and more can be obtained. If the support of the
density is not the unit interval then transformation can be applied.
This is an implementation of the methods proposed by the author of this
package published in the Journal of Nonparametric Statistics: Guan
(2016) &lt;doi:10.1080/10485252.2016.1163349&gt; and Guan (2017)
&lt;doi:10.1080/10485252.2017.1374384&gt;. For data with covariates, under
some semiparametric regression models such as Cox proportional hazards
model and the accelerated failure time model, the baseline survival
function can be estimated smoothly based on general interval censored
data.</description>
      </item>
    </software>
  </group>
</metapackage>
