MultiRFM

High-dimensional multi-study robust factor model

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To robustly extract meaningful features from data derived from multiple heterogeneous sources, we introduce a high-dimensional multi-study robust factor model, called MultiRFM, which learns latent features and accounts for the heterogeneity among sources.

Installation

“MultiRFM” depends on the ‘Rcpp’ and ‘RcppArmadillo’ package, which requires appropriate setup of computer. For the users that have set up system properly for compiling C++ files, the following installation command will work. ```{Rmd}

Install from CRAN

install.packages(“MultiRFM”)

Install from github

if (!require(“remotes”, quietly = TRUE)) install.packages(“remotes”) remotes::install_github(“feiyoung/MultiRFM”)

```

Usage

For usage examples and guided walkthroughs, check the vignettes directory of the repo.

• Low Dimensional Example

• High Dimensional Example

Simulated codes

For the codes in simulation study, check the simuCodes directory of the repo.

News

MultiRFM version 1.1 released! (2024-12-11)