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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 6 months ago 1906 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40hc72bb7e_1002.tar.bz2  3 years and 6 months ago 1920 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40h6115d3f_1002.tar.bz2  4 years and 6 months ago 2783 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1002.tar.bz2  4 years and 6 months ago 2765 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r35h6115d3f_1001.tar.bz2  5 years and 4 months ago 3638 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1001.tar.bz2  5 years and 4 months ago 3651 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_1000.tar.bz2  5 years and 11 months ago 4146 main cf202003 gcc7 cf201901
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_0.tar.bz2  5 years and 11 months ago 4202 main cf202003 gcc7 cf201901

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