File R-TransGraph.spec of Package R-TransGraph
# Automatically generated by CRAN2OBS
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# Spec file for package TransGraph
# This file is auto-generated using information in the package source,
# esp. Description and Summary. Improvements in that area should be
# discussed with upstream.
#
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%global packname TransGraph
%global rlibdir %{_libdir}/R/library
Name: R-%{packname}
Version: 1.1.0
Release: 0
Summary: Transfer Graph Learning
Group: Development/Libraries/Other
License: GPL-2
URL: http://cran.r-project.org/web/packages/%{packname}
Source: TransGraph_1.1.0.tar.gz
Requires: R-base
Requires: R-glasso
Requires: R-clime
Requires: R-HeteroGGM
Requires: R-dcov
Requires: R-huge
Requires: R-EvaluationMeasures
Requires: R-lpSolve
Requires: R-Rcpp
Requires: R-RcppArmadillo
Requires: R-igraph
Requires: R-RcppEigen
Requires: R-cli
Requires: R-lifecycle
Requires: R-magrittr
Requires: R-pkgconfig
Requires: R-rlang
Requires: R-vctrs
Requires: R-cpp11
Requires: R-glue
# %%if 0%%{?sle_version} > 120400 || 0%%{?is_opensuse}
# # Three others commonly needed
# BuildRequires: tex(ae.sty)
# BuildRequires: tex(fancyvrb.sty)
# BuildRequires: tex(inconsolata.sty)
# BuildRequires: tex(natbib.sty)
# %else
# BuildRequires: texlive
# %endif
# BuildRequires: texinfo
BuildRequires: fdupes
BuildRequires: R-base
BuildRequires: R-glasso
BuildRequires: R-clime
BuildRequires: R-HeteroGGM
BuildRequires: R-dcov
BuildRequires: R-huge
BuildRequires: R-EvaluationMeasures
BuildRequires: R-lpSolve
BuildRequires: R-Rcpp-devel
BuildRequires: R-RcppArmadillo-devel
BuildRequires: R-igraph
BuildRequires: R-RcppEigen-devel
BuildRequires: R-cli
BuildRequires: R-lifecycle
BuildRequires: R-magrittr
BuildRequires: R-pkgconfig
BuildRequires: R-rlang
BuildRequires: R-vctrs
BuildRequires: R-cpp11-devel
BuildRequires: R-glue
Suggests: R-knitr
Suggests: R-rmarkdown
%description
Transfer learning, aiming to use auxiliary domains to help improve
learning of the target domain of interest when multiple heterogeneous
datasets are available, has been a hot topic in statistical machine
learning. The recent transfer learning methods with statistical
guarantees mainly focus on the overall parameter transfer for
supervised models in the ideal case with the informative auxiliary
domains with overall similarity. In contrast, transfer learning for
unsupervised graph learning is in its infancy and largely follows the
idea of overall parameter transfer as for supervised learning. In this
package, the transfer learning for several complex graphical models is
implemented, including Tensor Gaussian graphical models, non-Gaussian
directed acyclic graph (DAG), and Gaussian graphical mixture models.
Notably, this package promotes local transfer at node-level and
subgroup-level in DAG structural learning and Gaussian graphical
mixture models, respectively, which are more flexible and robust than
the existing overall parameter transfer. As by-products, transfer
learning for undirected graphical model (precision matrix) via D-trace
loss, transfer learning for mean vector estimation, and single
non-Gaussian learning via topological layer method are also included in
this package. Moreover, the aggregation of auxiliary information is an
important issue in transfer learning, and this package provides
multiple user-friendly aggregation methods, including sample weighting,
similarity weighting, and most informative selection. (Note: the
transfer for tensor GGM has been temporarily removed in the current
version as its dependent R package Tlasso has been archived. The
historical version TransGraph_1.0.0.tar.gz can be downloaded at
<https://cran.r-project.org/src/contrib/Archive/TransGraph/>)
Reference: Ren, M., Zhen Y., and Wang J. (2024)
<https://jmlr.org/papers/v25/22-1313.html> "Transfer learning for
tensor graphical models". Ren, M., He X., and Wang J. (2023)
<doi:10.48550/arXiv.2310.10239> "Structural transfer learning of
non-Gaussian DAG". Zhao, R., He X., and Wang J. (2022)
<https://jmlr.org/papers/v23/21-1173.html> "Learning linear
non-Gaussian directed acyclic graph with diverging number of nodes".
%prep
%setup -q -c -n %{packname}
# the next line is needed, because we build without --clean in between two packages
rm -rf ~/.R
%build
%install
mkdir -p %{buildroot}%{rlibdir}
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname}
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so)
rm -f %{buildroot}%{rlibdir}/R.css
%fdupes -s %{buildroot}%{rlibdir}
#%%check
#%%{_bindir}/R CMD check %%{packname}
%files
%dir %{rlibdir}/%{packname}
%doc %{rlibdir}/%{packname}/DESCRIPTION
%{rlibdir}/%{packname}/INDEX
%{rlibdir}/%{packname}/Meta
%{rlibdir}/%{packname}/NAMESPACE
%{rlibdir}/%{packname}/R
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
%changelog
