The present and future of large scale studies of human brain and behavior—in typical and disease populations—is “mutli-omics”, “deep-phenotyping”, or other types of multi-source and multi-domain data collection initiatives. These massive studies rely on highly interdisciplinary teams that collect extremely diverse types of data across numerous systems and scales of measurement (e.g., genetics, brain structure, behavior, and demographics). Such large, complex, and heterogeneous data requires relatively simple methods that allow for flexibility in analyses without the loss of the inherent properties of various data types. Here we introduce a method designed specifically to address these problems: partial least squares-correspondence analysis-regression (PLS-CA-R). PLS-CA-R generalizes PLS regression for use with virtually any data type (e.g., continuous, ordinal, categorical, non-negative values). Though the primary emphasis here is on a PLS-regression approach generalized for data types, we also show that PLS-CA-R leads to additional generalizations of many routine “two-table” multivariate techniques and their respective algorithms, such as various PLS approaches, canonical correlation analysis, and redundancy analysis (a.k.a. reduced rank regression).