We introduce a high-dimensional multi-study elliptical factor model (MultiEFM), which learns latent features and accounts for the heterogeneity among sources. It provides robust estimation for heterogeneous high-dimensional data analysis, particularly suitable for multi-study RNA sequencing data and spatial multi-omics. The package implements efficient algorithms for model configuration, identifiability conditions, and parameter estimation.