A collection of privacy-preserving distributed algorithms (PDAs) for conducting federated statistical learning across multiple data sites. The PDA framework includes models for various tasks such as regression, trial emulation, causal inference, design-specific analysis, and clustering. The PDA algorithms run on a lead site and only require summary statistics from collaborating sites, with one or few iterations. The package can be used together with the online data transfer system (< https://pda-ota.pdamethods.org/>) for safe and convenient collaboration. For more information, please visit our software websites: < https://github.com/Penncil/pda>, and < https://pdamethods.org/>.