The paper introduces new models for the STRUCTURE program that incorporate sample group information to improve the detection of population structure, especially in scenarios with limited data. The models modify the prior distribution for each individual's population assignment, allowing the proportion of individuals assigned to a cluster to vary by location. These models are tested on simulated data and applied to microsatellite data from the CEPH Human Genome Diversity Panel. The results show that the new models can detect structure at lower levels of divergence or with less data compared to the original STRUCTURE models or principal components methods, while not biasing the detection of structure when it is not present. The new models are implemented in a new version of STRUCTURE, which is freely available online.The paper introduces new models for the STRUCTURE program that incorporate sample group information to improve the detection of population structure, especially in scenarios with limited data. The models modify the prior distribution for each individual's population assignment, allowing the proportion of individuals assigned to a cluster to vary by location. These models are tested on simulated data and applied to microsatellite data from the CEPH Human Genome Diversity Panel. The results show that the new models can detect structure at lower levels of divergence or with less data compared to the original STRUCTURE models or principal components methods, while not biasing the detection of structure when it is not present. The new models are implemented in a new version of STRUCTURE, which is freely available online.