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These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (http://arxiv.org/abs/1503.03515).

copied from cf-staging / r-esabcv
Type Size Name Uploaded Downloads Labels
conda 1.6 MB | noarch/r-esabcv-1.2.1-r41hc72bb7e_1002.tar.bz2  3 years and 11 months ago 2201 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40hc72bb7e_1002.tar.bz2  3 years and 11 months ago 2209 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40h6115d3f_1002.tar.bz2  4 years and 11 months ago 3064 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1002.tar.bz2  4 years and 11 months ago 3059 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r35h6115d3f_1001.tar.bz2  5 years and 9 months ago 3912 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1001.tar.bz2  5 years and 9 months ago 3940 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_1000.tar.bz2  6 years and 4 months ago 4432 main cf202003 gcc7 cf201901
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_0.tar.bz2  6 years and 4 months ago 4487 main cf202003 gcc7 cf201901

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