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]

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

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

On CRAN:

Conda:r-mixedcca-1.6.2(2025-03-25)

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

openblascpp

1.70 score 405 downloads 1 mentions 12 exports 129 dependencies

Last updated 3 years agofrom:853a853ee9. Checks:1 OK, 2 NOTE. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 05 2025
R-4.5-linux-x86_64NOTEMar 05 2025
R-4.4-linux-x86_64NOTEMar 05 2025

Exports:autocorblockcorestimateRestimateR_mixedfind_w12bicGenerateDataKendall_matrixKendallTaulambdaseq_generatemixedCCAmyrccstandardCCA

Dependencies:abindaskpassassertthatbase64encbslibcacachemcallrcliclustercodetoolscolorspacecpp11crosstalkcubaturecurldata.tabledendextenddigestdoFuturedoRNGdplyreggevaluatefansifarverfastmapfBasicsfMultivarfontawesomeforeachfsfuturefuture.applygclusgenericsgeometryggplot2globalsgluegridExtragssgtableheatmaplyhighrhtmltoolshtmlwidgetshttrirlbaisobanditeratorsjquerylibjsonliteknitrlabelinglatentcorlaterlatticelazyevallifecyclelinproglistenvlpSolvemagicmagrittrMASSMatrixMatrixModelsmemoisemgcvmicrobenchmarkmimemnormtmunsellmvtnormnlmenumDerivopensslparallellypcaPPpermutepillarpkgconfigplotlyplyrprocessxpromisespspurrrqapquantregR6rappdirsRColorBrewerRcppRcppArmadilloRcppProgressregistryreshape2rlangrmarkdownrngtoolssassscalesseriationsnSparseMspatialstablediststringistringrsurvivalsystibbletidyrtidyselecttimeDatetimeSeriestinytexTSPutf8vctrsveganviridisviridisLitewebshotwithrxfunyaml

Citation

To cite package ‘mixedCCA’ in publications use:

Yoon G, Gaynanova I (2022). mixedCCA: Sparse Canonical Correlation Analysis for High-Dimensional Mixed Data. R package version 1.6.2, https://CRAN.R-project.org/package=mixedCCA.

Corresponding BibTeX entry: 

  @Manual{,
    title = {mixedCCA: Sparse Canonical Correlation Analysis for
      High-Dimensional Mixed Data},
    author = {Grace Yoon and Irina Gaynanova},
    year = {2022},
    note = {R package version 1.6.2},
    url = {https://CRAN.R-project.org/package=mixedCCA},
  }

Readme and manuals

mixedCCA: sparse CCA for data of mixed types

The R package mixedCCA implements sparse canonical correlation analysis for data of mixed types: continuous, binary or zero-inflated (truncated continuous). The corresponding reference is

Yoon G., Carroll R.J. and Gaynanova I. (2020). “Sparse semiparametric canonical correlation analysis for data of mixed types”. Biometrika.

The faster version of latent correlation computation part is now fully available and implemented to the R package mixedCCA. The corresponding reference is available on arXiv:

Yoon G., Müller C.L. and Gaynanova I., “Fast computation of latent correlations” JCGS.

Installation

devtools::install_github("irinagain/mixedCCA")

Example

library(mixedCCA)

### Simple example

# Data setting
n <- 100; p1 <- 15; p2 <- 10 # sample size and dimensions for two datasets.
maxcancor <- 0.9 # true canonical correlation

# Correlation structure within each data set
set.seed(0)
perm1 <- sample(1:p1, size = p1);
Sigma1 <- autocor(p1, 0.7)[perm1, perm1]
blockind <- sample(1:3, size = p2, replace = TRUE);
Sigma2 <- blockcor(blockind, 0.7)
mu <- rbinom(p1+p2, 1, 0.5)

# true variable indices for each dataset
trueidx1 <- c(rep(1, 3), rep(0, p1-3))
trueidx2 <- c(rep(1, 2), rep(0, p2-2))

# Data generation
simdata <- GenerateData(n=n, trueidx1 = trueidx1, trueidx2 = trueidx2, maxcancor = maxcancor,
                        Sigma1 = Sigma1, Sigma2 = Sigma2,
                        copula1 = "exp", copula2 = "cube",
                        muZ = mu,
                        type1 = "trunc", type2 = "trunc",
                        c1 = rep(1, p1), c2 =  rep(0, p2)
)
X1 <- simdata$X1
X2 <- simdata$X2

# Sparse semiparametric CCA with BIC1 criterion
mixedCCAresult <- mixedCCA(X1, X2, type1 = "trunc", type2 = "trunc", BICtype = 1)
mixedCCAresult$KendallR # estimated latent correlation matrix
mixedCCAresult$w1 # estimated canonical vector for X1
mixedCCAresult$w2 # estimated canonical vector for X2
mixedCCAresult$cancor # estimated canonical correlation

# Separate estimation of latent correlation matrix
estimateR(X1, type = "trunc")$R # For X1 only
estimateR_mixed(X1, X2, type1 = "trunc", type2 = "trunc")$R12 # For X = (X1, X2)