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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 2194 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40hc72bb7e_1002.tar.bz2  3 years and 11 months ago 2203 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r40h6115d3f_1002.tar.bz2  4 years and 11 months ago 3060 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1002.tar.bz2  4 years and 11 months ago 3051 main
conda 1.6 MB | noarch/r-esabcv-1.2.1-r35h6115d3f_1001.tar.bz2  5 years and 9 months ago 3906 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r36h6115d3f_1001.tar.bz2  5 years and 9 months ago 3935 main cf202003
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_1000.tar.bz2  6 years and 4 months ago 4426 main cf202003 gcc7 cf201901
conda 1.6 MB | noarch/r-esabcv-1.2.1-r351h6115d3f_0.tar.bz2  6 years and 4 months ago 4480 main cf202003 gcc7 cf201901

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