Package: mixedCCA 1.6.2

Irina Gaynanova

mixedCCA: Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data

Semi-parametric approach for sparse canonical correlation analysis which can handle mixed data types: continuous, binary and truncated continuous. Bridge functions are provided to connect Kendall's tau to latent correlation under the Gaussian copula model. The methods are described in Yoon, Carroll and Gaynanova (2020) <doi:10.1093/biomet/asaa007> and Yoon, Mueller and Gaynanova (2021) <doi:10.1080/10618600.2021.1882468>.

Authors:Grace Yoon [aut], Mingze Huang [ctb], Irina Gaynanova [aut, cre]

mixedCCA_1.6.2.tar.gz
mixedCCA_1.6.2.tar.gz(r-4.5-noble)mixedCCA_1.6.2.tar.gz(r-4.4-noble)
mixedCCA_1.6.2.tgz(r-4.4-emscripten)mixedCCA_1.6.2.tgz(r-4.3-emscripten)
mixedCCA.pdf |mixedCCA.html
mixedCCA/json (API)

# Install 'mixedCCA' in R:
install.packages('mixedCCA', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

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

openblascpp

2.13 score 27 scripts 344 downloads 1 mentions 12 exports 129 dependencies

Last updated 2 years agofrom:853a853ee9. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 05 2024
R-4.5-linux-x86_64NOTEDec 05 2024

Exports:autocorblockcorestimateRestimateR_mixedfind_w12bicGenerateDataKendall_matrixKendallTaulambdaseq_generatemixedCCAmyrccstandardCCA

Dependencies:abindaskpassassertthatbase64encbslibcacachemcallrcliclustercodetoolscolorspacecpp11crosstalkcubaturecurldata.tabledendextenddigestdoFuturedoRNGdplyreggevaluatefansifarverfastmapfBasicsfMultivarfontawesomeforeachfsfuturefuture.applygclusgenericsgeometryggplot2globalsgluegridExtragssgtableheatmaplyhighrhtmltoolshtmlwidgetshttrirlbaisobanditeratorsjquerylibjsonliteknitrlabelinglatentcorlaterlatticelazyevallifecyclelinproglistenvlpSolvemagicmagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimemnormtmunsellmvtnormnlmenumDerivopensslparallellypcaPPpermutepillarpkgconfigplotlyplyrprocessxpromisespspurrrqapquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppProgressregistryreshape2rlangrmarkdownrngtoolssassscalesseriationsnSparseMspatialstablediststringistringrsurvivalsystibbletidyrtidyselecttimeDatetimeSeriestinytexTSPutf8vctrsveganviridisviridisLitewebshotwithrxfunyaml