# GFM

    library(GFM)
    #> 载入需要的程辑包：doSNOW
    #> 载入需要的程辑包：foreach
    #> 载入需要的程辑包：iterators
    #> 载入需要的程辑包：snow
    #> 载入需要的程辑包：parallel
    #>
    #> 载入程辑包：'parallel'
    #> The following objects are masked from 'package:snow':
    #>
    #>     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    #>     clusterExport, clusterMap, clusterSplit, makeCluster, parApply,
    #>     parCapply, parLapply, parRapply, parSapply, splitIndices,
    #>     stopCluster
    #> GFM :  Generalized factor model is implemented for ultra-high dimensional data with mixed-type variables.
    #> Two algorithms, variational EM and alternate maximization, are designed to implement the generalized factor model,
    #> respectively. The factor matrix and loading matrix together with the number of factors can be well estimated.
    #> This model can be employed in social and behavioral sciences, economy and finance, and  genomics,
    #> to extract interpretable nonlinear factors. More details can be referred to
    #> Wei Liu, Huazhen Lin, Shurong Zheng and Jin Liu. (2021) <doi:10.1080/01621459.2021.1999818>.   Check out our Package website (https://feiyoung.github.io/GFM/docs/index.html) for a more complete description of the methods and analyses