Multinomial outcomes arise in numerous fields---from sports and species counts to genomics---yet existing software often focuses on simpler fixed-effects or purely multinomial (logistic) frameworks. The MMLN package introduces a suite of functions to fit more complex multinomial logistic-normal regression models, including incorporation of random effects, and evaluate the fit of all multinomial regression models using the squared Mahalanobis distance residuals. The MMLN() function fits mixed-effects multinomial logistic-normal models via MCMC sampling, while the MDres() function computes the squared Mahalanobis distance residuals to comprehensively evaluate model adequacy. Users can visualize or formally test these using quantile-quantile plots and Kolmogorov-Smirnov tests. We describe the design and usage of the functions provided in the MMLN package and demonstrate the package's capabilities for modeling multinomial data by integrating flexible modeling tools, summaries, visualization, and robust diagnostics.