<metapackage xmlns:os="http://opensuse.org/Standards/One_Click_Install" xmlns="http://opensuse.org/Standards/One_Click_Install">
  <group>
    <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/15.5/</url>
      </repository>
      <repository recommended="true">
        <name>deleted</name>
        <summary>INTERNAL PROJECT</summary>
        <description>don't delete this project, it's used for internal purposes</description>
        <url>https://download.opensuse.org/repositories/deleted/deleted/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE:Leap:15.5</name>
        <summary></summary>
        <description>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</description>
        <url>https://download.opensuse.org/repositories/openSUSE:/Leap:/15.5/standard/</url>
      </repository>
      <repository recommended="true">
        <name>openSUSE:Backports:SLE-15-SP5</name>
        <summary>Backports project for SLE-15-SP5</summary>
        <description>Backports project for SLE-15-SP5</description>
        <url>https://download.opensuse.org/repositories/openSUSE:/Backports:/SLE-15-SP5/standard/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15-SP5:GA</name>
        <summary></summary>
        <description></description>
        <url>https://download.opensuse.org/repositories/SUSE:/SLE-15-SP5:/GA/pool/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15-SP4:Update</name>
        <summary>SLE 15 SP4</summary>
        <description>SLE 15 SP4</description>
        <url>https://download.opensuse.org/distribution/leap/15.5/repo/oss/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15-SP4:GA</name>
        <summary></summary>
        <description></description>
        <url>https://download.opensuse.org/repositories/SUSE:/SLE-15-SP4:/GA/pool/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15-SP3:Update</name>
        <summary>SLE 15 SP3</summary>
        <description>SLE 15 SP3</description>
        <url>https://download.opensuse.org/distribution/leap/15.5/repo/oss/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15-SP3:GA</name>
        <summary></summary>
        <description></description>
        <url>https://download.opensuse.org/repositories/SUSE:/SLE-15-SP3:/GA/pool/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15-SP2:Update</name>
        <summary>SLE 15 SP2</summary>
        <description>SLE 15 SP2</description>
        <url>https://download.opensuse.org/distribution/leap/15.5/repo/oss/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15-SP2:GA</name>
        <summary>SLE 15 SP2</summary>
        <description>SLE 15 SP2</description>
        <url>https://download.opensuse.org/repositories/SUSE:/SLE-15-SP2:/GA/pool/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15-SP1:Update</name>
        <summary>SLE 15 SP1</summary>
        <description>SLE 15 SP1</description>
        <url>https://download.opensuse.org/distribution/leap/15.5/repo/oss/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15-SP1:GA</name>
        <summary>SLE 15 SP1</summary>
        <description>SLE 15 SP1</description>
        <url>https://download.opensuse.org/repositories/SUSE:/SLE-15-SP1:/GA/pool/</url>
      </repository>
      <repository recommended="true">
        <name>SUSE:SLE-15:Update</name>
        <summary>SLE 15</summary>
        <description>SLE 15</description>
        <url>https://download.opensuse.org/distribution/leap/15.5/repo/oss/</url>
      </repository>
      <repository recommended="false">
        <name>SUSE:SLE-15:GA</name>
        <summary>SLE 15</summary>
        <description>SLE 15</description>
        <url>https://download.opensuse.org/repositories/SUSE:/SLE-15:/GA/pool/</url>
      </repository>
    </repositories>
    <software>
      <item>
        <name>R-ergmito</name>
        <summary>Exponential Random Graph Models for Small Networks</summary>
        <description>Simulation and estimation of Exponential Random Graph Models (ERGMs)
for small networks using exact statistics as shown in Vega Yon et al.
(2020) &lt;DOI:10.1016/j.socnet.2020.07.005&gt;. As a difference from the
'ergm' package, 'ergmito' circumvents using Markov-Chain Maximum
Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood
Estimator (MLE) to fit ERGMs for small networks. As exhaustive
enumeration is computationally feasible for small networks, this R
package takes advantage of this and provides tools for calculating
likelihood functions, and other relevant functions, directly, meaning
that in many cases both estimation and simulation of ERGMs for small
networks can be faster and more accurate than simulation-based
algorithms.</description>
      </item>
    </software>
  </group>
</metapackage>
