Integrating multiple observational studies for meta-analysis has sparked much interest. The presented R package WMAP (Weighted Meta-Analysis with Pseudo-Populations) (Guha et al., 2026) addresses a critical gap in the implementation of integrative weighting approaches for multiple observational studies and causal inferences about various groups of subjects, such as disease subtypes. The package features three weighting approaches, each representing a special case of the unified weighting framework introduced by Guha and Li (2024), which includes an extension of inverse probability weights for data integration settings. It performs meta-analysis on user-inputted datasets as follows: (i) it first estimates the propensity scores for study-group combinations, calculates subject balancing weights, and determines the effective sample size (ESS) for a user-specified weighting method; and (ii) it then estimates various features of multiple counterfactual group outcomes, such as group medians and differences in group means for the mRNA expression of eight genes. Additionally, bootstrap variability estimates are provided. Among the implemented weighting methods, we highlight the FLEXible, Optimized, and Realistic (FLEXOR) method, which is specifically designed to maximize the ESS within the unified framework. The use of the software is illustrated through simulations and a multi-site breast cancer case study based on a simulated dataset modeled after real TCGA data from seven medical centers.

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