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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  4 years and 3 months ago 2410 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40hc72bb7e_1002.tar.bz2  4 years and 3 months ago 2428 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40h6115d3f_1002.tar.bz2  5 years and 3 months ago 3277 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1002.tar.bz2  5 years and 3 months ago 3259 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r35h6115d3f_1001.tar.bz2  6 years and 1 month ago 4129 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1001.tar.bz2  6 years and 1 month ago 4169 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_1000.tar.bz2  6 years and 8 months ago 4655 main cf202003 gcc7 cf201901
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_0.tar.bz2  6 years and 8 months ago 4708 main cf202003 gcc7 cf201901

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