LowRankModels.jl is a julia package for modeling and fitting generalized low rank models (GLRMs). GLRMs model a data array by a low rank matrix, and include many well known models in data analysis, such as principal components analysis (PCA), matrix completion, robust PCA, nonnegative matrix factorization, k-means, and many more. For more information on GLRMs, see our paper. There is a python interface to this package, and a GLRM implementation in the H2O machine learning platform with interfaces in a variety of languages. LowRankModels.jl makes it easy to mix and match loss functions and regularizers to construct a model suitable for a particular data set. In particular, it supports: using different loss functions for different columns of the data array, which is useful when data types are heterogeneous (eg, real, boolean, and ordinal columns); fitting the model to only some of the entries in the table, which is useful for data tables with many missing (unobserved) entries; and adding offsets and scalings to the model without destroying sparsity, which is useful when the data is poorly scaled.