mmBPFA-package: Multi-Modal Beta-Process Factor Analysis
In EandrewJones/mmBPFA: Multi-Modal Beta-Process Factor Analysis
Description
Package Content
Maintainer
Author(s)
Description
A semi-parametric, multidimensional Bayesian factor analysis for multimodal data.
By combining a beta-process prior (Knowles and Ghahramani 2011) on the factor loadings matrix
and treating the observed margins as arbitrary manifestations of a latent Gaussian copula (Murray et al. 2013),
this package allows the user to perform a factor analysis on data with any
combination of marginal distributions (continuous, discrete, or both)
without first specifying the dimensionality (K) of the data. The
non-parametric prior automatically learns the true dimensionality.
Potential applications include traditionaldimension reduction, multidimensional
scaling (item-response theory), and missing data imputation. The sampler is
implemented in C++ for improved speed and includes post-processing functions
that are amenable to a tidy workflow.
Package Content
Index: This package was not yet installed at build time.
Maintainer
Evan Jones <evan.a.jones3@gmail.com>
Author(s)
Evan Jones [aut, cre]
EandrewJones/mmBPFA documentation built on Feb. 14, 2021, 11:17 p.m.
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Description Package Content Maintainer Author(s)
Description
A semi-parametric, multidimensional Bayesian factor analysis for multimodal data. By combining a beta-process prior (Knowles and Ghahramani 2011) on the factor loadings matrix and treating the observed margins as arbitrary manifestations of a latent Gaussian copula (Murray et al. 2013), this package allows the user to perform a factor analysis on data with any combination of marginal distributions (continuous, discrete, or both) without first specifying the dimensionality (K) of the data. The non-parametric prior automatically learns the true dimensionality. Potential applications include traditionaldimension reduction, multidimensional scaling (item-response theory), and missing data imputation. The sampler is implemented in C++ for improved speed and includes post-processing functions that are amenable to a tidy workflow.
Package Content
Index: This package was not yet installed at build time.
Maintainer
Evan Jones <evan.a.jones3@gmail.com>
Author(s)
Evan Jones [aut, cre]
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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