Finite Mixture of Censored Regression Models with Skewed Distributions // Provides an implementation of finite mixture regression models for censored data under four distributional families: Normal (FM-NCR), Student t (FM-TCR), skew-Normal (FM- SNCR), and skew-t (FM-STCR). The package enables flexible modeling of skewness and heavy tails often observed in real- world data, while explicitly accounting for censoring. Functions are included for parameter estimation via the Expectation-Maximization (EM) algorithm, computation of standard errors, and model comparison criteria such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the Efficient Determination Criterion (EDC). The underlying methodology is described in Park et al. (2024) doi:10.1007/s00180-024-01459-4.