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