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R-KPC rpm build for : openSUSE Leap 15. For other distributions click R-KPC.

Name : R-KPC
Version : 0.1.2 Vendor : obs://build_opensuse_org/devel:languages:R
Release : lp153.3.7 Date : 2024-06-14 12:03:48
Group : Development/Libraries/Other Source RPM : R-KPC-0.1.2-lp153.3.7.src.rpm
Size : 0.09 MB
Packager : (none)
Summary : Kernel Partial Correlation Coefficient
Description :
Implementations of two empirical versions the kernel partial
correlation (KPC) coefficient and the associated variable selection
algorithms. KPC is a measure of the strength of conditional association
between Y and Z given X, with X, Y, Z being random variables taking
values in general topological spaces. As the name suggests, KPC is
defined in terms of kernels on reproducing kernel Hilbert spaces
(RKHSs). The population KPC is a deterministic number between 0 and 1;
it is 0 if and only if Y is conditionally independent of Z given X, and
it is 1 if and only if Y is a measurable function of Z and X. One
empirical KPC estimator is based on geometric graphs, such as K-nearest
neighbor graphs and minimum spanning trees, and is consistent under
very weak conditions. The other empirical estimator, defined using
conditional mean embeddings (CMEs) as used in the RKHS literature, is
also consistent under suitable conditions. Using KPC, a stepwise
forward variable selection algorithm KFOCI (using the graph based
estimator of KPC) is provided, as well as a similar stepwise forward
selection algorithm based on the RKHS based estimator. For more details
on KPC, its empirical estimators and its application on variable
selection, see Huang, Z., N. Deb, and B. Sen (2022). “Kernel partial
correlation coefficient – a measure of conditional dependence” (URL
listed below). When X is empty, KPC measures the unconditional
dependence between Y and Z, which has been described in Deb, N., P.
Ghosal, and B. Sen (2020), “Measuring association on topological spaces
using kernels and geometric graphs” < arXiv:2010.01768>, and it is
implemented in the functions KMAc() and Klin() in this package. The
latter can be computed in near linear time.

RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64

Content of RPM  Provides Requires

Download
ftp.icm.edu.pl  R-KPC-0.1.2-lp153.3.7.x86_64.rpm
     

Provides :
R-KPC
R-KPC(x86-64)

Requires :
R-RANN
R-Rcpp
R-RcppArmadillo
R-RcppEnsmallen
R-base
R-data.table
R-kernlab
R-mlpack
R-proxy
rpmlib(CompressedFileNames) <= 3.0.4-1
rpmlib(FileDigests) <= 4.6.0-1
rpmlib(PayloadFilesHavePrefix) <= 4.0-1
rpmlib(PayloadIsXz) <= 5.2-1


Content of RPM :
/usr/lib64/R/library/KPC
/usr/lib64/R/library/KPC/DESCRIPTION
/usr/lib64/R/library/KPC/INDEX
/usr/lib64/R/library/KPC/Meta
/usr/lib64/R/library/KPC/Meta/Rd.rds
/usr/lib64/R/library/KPC/Meta/data.rds
/usr/lib64/R/library/KPC/Meta/features.rds
/usr/lib64/R/library/KPC/Meta/hsearch.rds
/usr/lib64/R/library/KPC/Meta/links.rds
/usr/lib64/R/library/KPC/Meta/nsInfo.rds
/usr/lib64/R/library/KPC/Meta/package.rds
/usr/lib64/R/library/KPC/NAMESPACE
/usr/lib64/R/library/KPC/NEWS.md
/usr/lib64/R/library/KPC/R
/usr/lib64/R/library/KPC/R/KPC
/usr/lib64/R/library/KPC/R/KPC.rdb
/usr/lib64/R/library/KPC/R/KPC.rdx
/usr/lib64/R/library/KPC/data
/usr/lib64/R/library/KPC/data/Rdata.rdb
/usr/lib64/R/library/KPC/data/Rdata.rds
/usr/lib64/R/library/KPC/data/Rdata.rdx
/usr/lib64/R/library/KPC/help
/usr/lib64/R/library/KPC/help/AnIndex
/usr/lib64/R/library/KPC/help/KPC.rdb
/usr/lib64/R/library/KPC/help/KPC.rdx
/usr/lib64/R/library/KPC/help/aliases.rds
/usr/lib64/R/library/KPC/help/paths.rds
/usr/lib64/R/library/KPC/html
/usr/lib64/R/library/KPC/html/00Index.html
/usr/lib64/R/library/KPC/html/R.css

 
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