File R-oddstream.spec of Package R-oddstream

# Automatically generated by CRAN2OBS
# 
# Spec file for package oddstream 
# This file is auto-generated using information in the package source, 
# esp. Description and Summary. Improvements in that area should be 
# discussed with upstream. 
# 
# Copyright (c) 2025 SUSE LINUX GmbH, Nuernberg, Germany. 
# 
# All modifications and additions to the file contributed by third parties 
# remain the property of their copyright owners, unless otherwise agreed 
# upon. The license for this file, and modifications and additions to the 
# file, is the same license as for the pristine package itself (unless the 
# license for the pristine package is not an Open Source License, in which 
# case the license is the MIT License). An "Open Source License" is a 
# license that conforms to the Open Source Definition (Version 1.9) 
# published by the Open Source Initiative. 
#  
# Please submit bugfixes or comments via http://bugs.opensuse.org/ 
# 
 
%global packname  oddstream 
%global rlibdir   %{_libdir}/R/library 
 
Name:           R-%{packname} 
Version:        0.5.0 
Release:        0 
Summary:        Outlier Detection in Data Streams 
Group:          Development/Libraries/Other 
License:        GPL-3 
URL:            http://cran.r-project.org/web/packages/%{packname} 
Source:         oddstream_0.5.0.tar.gz 
Requires:       R-base 
Requires:	R-pcaPP
Requires:	R-ggplot2
Requires:	R-ks
Requires:	R-RcppRoll
Requires:	R-moments
Requires:	R-RColorBrewer
Requires:	R-mvtsplot
Requires:	R-tibble
Requires:	R-reshape
Requires:	R-dplyr
Requires:	R-tidyr
Requires:	R-kernlab
Requires:	R-magrittr
Requires:	R-cli
Requires:	R-generics
Requires:	R-glue
Requires:	R-lifecycle
Requires:	R-pillar
Requires:	R-R6
Requires:	R-rlang
Requires:	R-tidyselect
Requires:	R-vctrs
Requires:	R-gtable
Requires:	R-isoband
Requires:	R-S7
Requires:	R-scales
Requires:	R-withr
Requires:	R-FNN
Requires:	R-mclust
Requires:	R-multicool
Requires:	R-mvtnorm
Requires:	R-pracma
Requires:	R-Rcpp
Requires:	R-plyr
Requires:	R-pkgconfig
Requires:	R-purrr
Requires:	R-stringr
Requires:	R-cpp11
Requires:	R-utf8
Requires:	R-farver
Requires:	R-labeling
Requires:	R-viridisLite
Requires:	R-stringi
 
# %%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-pcaPP
BuildRequires: 	R-ggplot2
BuildRequires: 	R-ks
BuildRequires: 	R-RcppRoll
BuildRequires: 	R-moments
BuildRequires: 	R-RColorBrewer
BuildRequires: 	R-mvtsplot
BuildRequires: 	R-tibble
BuildRequires: 	R-reshape
BuildRequires: 	R-dplyr
BuildRequires: 	R-tidyr
BuildRequires: 	R-kernlab
BuildRequires: 	R-magrittr
BuildRequires: 	R-cli
BuildRequires: 	R-generics
BuildRequires: 	R-glue
BuildRequires: 	R-lifecycle
BuildRequires: 	R-pillar
BuildRequires: 	R-R6
BuildRequires: 	R-rlang
BuildRequires: 	R-tidyselect
BuildRequires: 	R-vctrs
BuildRequires: 	R-gtable
BuildRequires: 	R-isoband
BuildRequires: 	R-S7
BuildRequires: 	R-scales
BuildRequires: 	R-withr
BuildRequires: 	R-FNN
BuildRequires: 	R-mclust
BuildRequires: 	R-multicool
BuildRequires: 	R-mvtnorm
BuildRequires: 	R-pracma
BuildRequires: 	R-Rcpp-devel
BuildRequires: 	R-plyr
BuildRequires: 	R-pkgconfig
BuildRequires: 	R-purrr
BuildRequires: 	R-stringr
BuildRequires: 	R-cpp11-devel
BuildRequires: 	R-utf8
BuildRequires: 	R-farver
BuildRequires: 	R-labeling
BuildRequires: 	R-viridisLite
BuildRequires: 	R-stringi
 
Suggests:	R-testthat
Suggests:	R-tidyverse
%description 
We proposes a framework that provides real time support for early 
detection of anomalous series within a large collection of streaming 
time series data. By definition, anomalies are rare in comparison to a 
system's typical behaviour. We define an anomaly as an observation that 
is very unlikely given the forecast distribution. The algorithm first 
forecasts a boundary for the system's typical behaviour using a 
representative sample of the typical behaviour of the system. An 
approach based on extreme value theory is used for this boundary 
prediction process. Then a sliding window is used to test for anomalous 
series within the newly arrived collection of series. Feature based 
representation of time series is used as the input to the model. To 
cope with concept drift, the forecast boundary for the system's typical 
behaviour is updated periodically.  More details regarding the 
algorithm can be found in Talagala, P. D., Hyndman, R. J., Smith-Miles, 
K., et al. (2019) <doi:10.1080/10618600.2019.1617160>. 
 
%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
%{rlibdir}/%{packname}/data
%doc %{rlibdir}/%{packname}/help
%doc %{rlibdir}/%{packname}/html
 
%changelog