Package: mixedCCA 1.6.3

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_1.6.3.tar.gz
mixedCCA_1.6.3.zip(r-4.7)mixedCCA_1.6.3.zip(r-4.6)mixedCCA_1.6.3.zip(r-4.5)
mixedCCA_1.6.3.tgz(r-4.6-x86_64)mixedCCA_1.6.3.tgz(r-4.6-arm64)mixedCCA_1.6.3.tgz(r-4.5-x86_64)mixedCCA_1.6.3.tgz(r-4.5-arm64)
mixedCCA_1.6.3.tar.gz(r-4.7-arm64)mixedCCA_1.6.3.tar.gz(r-4.7-x86_64)mixedCCA_1.6.3.tar.gz(r-4.6-arm64)mixedCCA_1.6.3.tar.gz(r-4.6-x86_64)
mixedCCA_1.6.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
mixedCCA/json (API)

# Install 'mixedCCA' in R:

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

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

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

openblas cpp

4.76 score 22 stars 26 scripts 482 downloads 1 mentions 12 exports 129 dependencies

Last updated from:511c4af4b5. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	171
linux-devel-x86_64	OK	189
source / vignettes	OK	218
linux-release-arm64	OK	185
linux-release-x86_64	OK	178
macos-release-arm64	OK	119
macos-release-x86_64	OK	185
macos-oldrel-arm64	OK	133
macos-oldrel-x86_64	OK	185
windows-devel	OK	142
windows-release	OK	141
windows-oldrel	OK	142
wasm-release	OK	161

Exports:autocor blockcor estimateR estimateR_mixed find_w12bic GenerateData Kendall_matrix KendallTau lambdaseq_generate mixedCCA myrcc standardCCA

Dependencies:abind askpass assertthat base64enc bslib ca cachem callr cli cluster codetools colorspace cpp11 crosstalk cubature curl data.table dendextend digest doFuture doRNG dplyr egg evaluate farver fastmap fBasics fMultivar fontawesome foreach fs future future.apply gclus generics geometry ggplot2 globals glue gridExtra gss gtable heatmaply highr htmltools htmlwidgets httr irlba isoband iterators jquerylib jsonlite knitr labeling latentcor later lattice lazyeval lifecycle linprog listenv lpSolve magic magrittr MASS Matrix MatrixModels memoise mgcv microbenchmark mime mnormt mvtnorm nlme numDeriv openssl otel parallelly pcaPP permute pillar pkgconfig plotly plyr processx promises ps purrr qap quantreg R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppProgress registry reshape2 rlang rmarkdown rngtools S7 sass scales seriation sn SparseM spatial stabledist stringi stringr survival sys tibble tidyr tidyselect timeDate timeSeries tinytex TSP utf8 vctrs vegan viridis viridisLite webshot withr xfun yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Construct a correlation matrix	autocor blockcor CorrStructure
Estimate latent correlation matrix	estimateR estimateR_mixed
Internal mixedCCA function finding w1 and w2 given R1, R2 and R12	find_w12bic
Mixed type simulation data generator for sparse CCA	GenerateData
Kendall's tau correlation	KendallTau Kendall_matrix
Internal data-driven lambda sequence generating function.	lambdaseq_generate
Sparse CCA for data of mixed types with BIC criterion	mixedCCA
Internal RidgeCCA function	myrcc
Internal standard CCA function.	standardCCA