Package: mixedCCA 1.6.2

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
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mixedCCA.pdf |mixedCCA.html
mixedCCA/json (API)

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

Peer review:

Bug tracker:https://github.com/irinagain/mixedcca/issues

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

On CRAN:

4.72 score 20 stars 26 scripts 373 downloads 1 mentions 12 exports 129 dependencies

Last updated 2 years agofrom:4c2b63f754. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 04 2024
R-4.5-win-x86_64NOTENov 04 2024
R-4.5-linux-x86_64NOTENov 04 2024
R-4.4-win-x86_64NOTENov 04 2024
R-4.4-mac-x86_64NOTENov 04 2024
R-4.4-mac-aarch64NOTENov 04 2024
R-4.3-win-x86_64NOTENov 04 2024
R-4.3-mac-x86_64NOTENov 04 2024
R-4.3-mac-aarch64NOTENov 04 2024

Exports:autocorblockcorestimateRestimateR_mixedfind_w12bicGenerateDataKendall_matrixKendallTaulambdaseq_generatemixedCCAmyrccstandardCCA

Dependencies:abindaskpassassertthatbase64encbslibcacachemcallrcliclustercodetoolscolorspacecpp11crosstalkcubaturecurldata.tabledendextenddigestdoFuturedoRNGdplyreggevaluatefansifarverfastmapfBasicsfMultivarfontawesomeforeachfsfuturefuture.applygclusgenericsgeometryggplot2globalsgluegridExtragssgtableheatmaplyhighrhtmltoolshtmlwidgetshttrirlbaisobanditeratorsjquerylibjsonliteknitrlabelinglatentcorlaterlatticelazyevallifecyclelinproglistenvlpSolvemagicmagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimemnormtmunsellmvtnormnlmenumDerivopensslparallellypcaPPpermutepillarpkgconfigplotlyplyrprocessxpromisespspurrrqapquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppProgressregistryreshape2rlangrmarkdownrngtoolssassscalesseriationsnSparseMspatialstablediststringistringrsurvivalsystibbletidyrtidyselecttimeDatetimeSeriestinytexTSPutf8vctrsveganviridisviridisLitewebshotwithrxfunyaml