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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 10 months ago 2150 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40hc72bb7e_1002.tar.bz2  3 years and 10 months ago 2153 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40h6115d3f_1002.tar.bz2  4 years and 11 months ago 3015 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1002.tar.bz2  4 years and 11 months ago 3002 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r35h6115d3f_1001.tar.bz2  5 years and 8 months ago 3860 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1001.tar.bz2  5 years and 8 months ago 3886 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_1000.tar.bz2  6 years and 3 months ago 4377 main cf202003 gcc7 cf201901
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_0.tar.bz2  6 years and 3 months ago 4432 main cf202003 gcc7 cf201901

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