Package: SpatialBSS 0.16-0

Klaus Nordhausen

SpatialBSS: Blind Source Separation for Multivariate Spatial Data

Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.

Authors:Christoph Muehlmann [aut], Mika Sipil<e4> [aut], Claudia Cappello [aut], Sandra De Iaco [aut], Klaus Nordhausen [aut, cre], Sara Taskinen [aut], Joni Virta [aut]

SpatialBSS_0.16-0.tar.gz
SpatialBSS_0.16-0.tar.gz(r-4.7-arm64)SpatialBSS_0.16-0.tar.gz(r-4.7-x86_64)SpatialBSS_0.16-0.tar.gz(r-4.6-arm64)SpatialBSS_0.16-0.tar.gz(r-4.6-x86_64)
SpatialBSS_0.16-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SpatialBSS/json (API)

# Install 'SpatialBSS' in R:

install.packages('SpatialBSS', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

veneto_weather - Weekly Aggregated Climate and Meteorological Data in Veneto, Italy

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas cpp openmp

2.48 score 1 packages 346 downloads 13 exports 11 dependencies

Last updated from:8d003c23ae. Checks:4 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	WARNING	158
linux-devel-x86_64	WARNING	168
source / vignettes	OK	250
linux-release-arm64	WARNING	175
linux-release-x86_64	WARNING	148
wasm-release	OK	728

Exports:gen_glob_outl gen_loc_outl local_covariance_matrix local_gss_covariance_matrix robsbss sbss sbss_asymp sbss_boot snss_jd snss_sd snss_sjd spatial_kernel_matrix white_data

Dependencies:clue cluster DEoptimR distances JADE lattice Rcpp RcppArmadillo robustbase sp SpatialNP

Spatial Blind Source Separation

Rendered fromSBSS.Rmdusingknitr::rmarkdown

Last update: 2025-03-27
Started: 2020-03-26

Help page	Topics
Blind Source Separation for Multivariate Spatial Data	SpatialBSS-package
Coef Method for an Object of Class 'sbss'	coef.sbss
Contamination with Global Outliers	gen_glob_outl
Contamination with Local Outliers	gen_loc_outl
Computation of Local Covariance Matrices	local_covariance_matrix
Computation of Robust Local Covariance Matrices	local_gss_covariance_matrix
Plot Method for an Object of Class 'sbss'	plot.sbss
Predict Method for an Object of Class 'sbss'	predict.sbss
Print Method for an Object of Class 'sbss'	print.sbss
Robust Spatial Blind Source Separation	robsbss robsbss.default robsbss.sf robsbss.SpatialPointsDataFrame
Spatial Blind Source Separation	sbss sbss.default sbss.sf sbss.SpatialPointsDataFrame
Asymptotic Test for the White Noise Dimension in a Spatial Blind Source Separation Model	sbss_asymp sbss_asymp.default sbss_asymp.sf sbss_asymp.SpatialPointsDataFrame
Different Bootstrap Tests for the White Noise Dimension in a Spatial Blind Source Separation Model	sbss_boot sbss_boot.default sbss_boot.sf sbss_boot.SpatialPointsDataFrame
Spatial Non-Stationary Source Separation Joint Diagonalization	snss_jd snss_jd.default snss_jd.list snss_jd.sf snss_jd.SpatialPointsDataFrame
Spatial Non-Stationary Source Separation Simultaneous Diagonalization	snss_sd snss_sd.default snss_sd.list snss_sd.sf snss_sd.SpatialPointsDataFrame
Spatial Non-Stationary Source Separation Spatial Joint Diagonalization	snss_sjd snss_sjd.default snss_sjd.list snss_sjd.sf snss_sjd.SpatialPointsDataFrame
Computation of Spatial Kernel Matrices	spatial_kernel_matrix
Weekly Aggregated Climate and Meteorological Data in Veneto, Italy	veneto_weather
Different Approaches of Data Whitening	white_data

Package: SpatialBSS 0.16-0

SpatialBSS: Blind Source Separation for Multivariate Spatial Data

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)