latentcor: Fast Computation of Latent Correlations for Mixed Data
The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated),
comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation.
The estimation is based on latent copula Gaussian models.
For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017).
For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <doi:10.48550/arXiv.1809.06255>.
For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>.
For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.
| Version: |
2.0.1 |
| Depends: |
R (≥ 3.0.0) |
| Imports: |
stats, pcaPP, fMultivar, mnormt, Matrix, MASS, heatmaply, ggplot2, plotly, graphics, geometry, doFuture, foreach, future, doRNG, microbenchmark |
| Suggests: |
rmarkdown, markdown, knitr, testthat (≥ 3.0.0), lattice, cubature, plot3D, covr |
| Published: |
2022-09-05 |
| Author: |
Mingze Huang 
[aut, cre],
Grace Yoon [aut],
Christian Müller
[aut],
Irina Gaynanova
[aut] |
| Maintainer: |
Mingze Huang <mingzehuang at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| CRAN checks: |
latentcor results |
Documentation:
Downloads:
Reverse dependencies:
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