<p>Partially nested designs—where clustering occurs in some but not all study arms—are common in psychological and behavioral research. In these designs, clustering often arises in the treatment arm due to the treatment delivery, such as individuals clustered within treatment facilitators. This clustering structure raises substantive interest in exploring how treatment effects may vary across clusters. Yet, existing tools for estimating cluster-specific treatment effects in such designs remain limited, especially when clustering is treatment-induced, and the assignment of treatment arm individuals to clusters is nonrandomized. To address this gap and to make the estimation accessible, we introduce an R package, <i>PND.heter.cluster</i>, which implements methods described in Liu (2024) for estimating cluster-specific treatment effects in 2/1 partially nested designs with treatment-induced clustering. The package supports the use of machine learning methods to relax modeling assumptions and enhance estimation flexibility. This tutorial describes the main function of the package and illustrates its use through a step-by-step application example using data from a partially nested design evaluating a teacher professional development program.</p>