mvrsquared: Compute the Coefficient of Determination for Vector or Matrix Outcomes

Compute the coefficient of determination for outcomes in n-dimensions. May be useful for multidimensional predictions (such as a multinomial model) or calculating goodness of fit from latent variable models such as probabilistic topic models like latent Dirichlet allocation or deterministic topic models like latent semantic analysis. Based on Jones (2019) <doi:10.48550/arXiv.1911.11061>.

Version: 0.1.5
Depends: R (≥ 3.0.2)
Imports: Matrix, methods, Rcpp (≥ 1.0.2)
LinkingTo: Rcpp, RcppArmadillo, RcppThread (≥ 2.1.3)
Suggests: dplyr, furrr, knitr, MASS, nnet, parallel, rmarkdown, stats, stringr, testthat, textmineR, tidytext, spelling
Published: 2023-07-15
DOI: 10.32614/CRAN.package.mvrsquared
Author: Tommy Jones ORCID iD [aut, cre], Thomas Nagler ORCID iD [ctb]
Maintainer: Tommy Jones <jones.thos.w at>
License: MIT + file LICENSE
NeedsCompilation: yes
Language: en-US
Materials: README NEWS
CRAN checks: mvrsquared results


Reference manual: mvrsquared.pdf
Vignettes: Getting Started With mvrsquared


Package source: mvrsquared_0.1.5.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mvrsquared_0.1.5.tgz, r-oldrel (arm64): mvrsquared_0.1.5.tgz, r-release (x86_64): mvrsquared_0.1.5.tgz, r-oldrel (x86_64): mvrsquared_0.1.5.tgz
Old sources: mvrsquared archive

Reverse dependencies:

Reverse imports: tidylda


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