Package: eBsc 4.17

Francisco Rosales

eBsc: "Empirical Bayes Smoothing Splines with Correlated Errors"

Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Krivobokova", "Serra", "Rosales" and "Klockmann" (2021) <arxiv:1812.06948> for details.

Authors:Francisco Rosales, Tatyana Krivobokova, Paulo Serra.

eBsc_4.17.tar.gz
eBsc_4.17.tar.gz(r-4.7-arm64)eBsc_4.17.tar.gz(r-4.7-x86_64)eBsc_4.17.tar.gz(r-4.6-arm64)eBsc_4.17.tar.gz(r-4.6-x86_64)
eBsc_4.17.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
eBsc/json (API)

# Install 'eBsc' in R:
install.packages('eBsc', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

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

openblascpp

1.15 score 14 scripts 279 downloads 9 exports 8 dependencies

Last updated from:bb5decbc9b. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK144
linux-devel-x86_64OK139
source / vignettesOK157
linux-release-arm64OK142
linux-release-x86_64OK143
wasm-releaseOK109

Exports:drbasisEBCparallelEBCqeBscplot.eBscprint.eBscrcpparma_innerproductrcpparma_outerproductsummary.eBsc

Dependencies:BrobdingnaglatticeMASSMatrixmvtnormnlmeRcppRcppArmadillo