It is very common nowadays for a study to collect multiple
    features and appropriately integrating multiple longitudinal features
    simultaneously for defining individual clusters becomes increasingly
    crucial to understanding population heterogeneity and predicting
    future outcomes.  'BCClong' implements a Bayesian consensus clustering
    (BCC) model for multiple longitudinal features via a generalized
    linear mixed model. Compared to existing packages, several key
    features make the 'BCClong' package appealing: (a) it allows
    simultaneous clustering of mixed-type (e.g., continuous, discrete and
    categorical) longitudinal features, (b) it allows each longitudinal
    feature to be collected from different sources with measurements taken
    at distinct sets of time points (known as irregularly sampled
    longitudinal data), (c) it relaxes the assumption that all features
    have the same clustering structure by estimating the feature-specific
    (local) clusterings and consensus (global) clustering.

Developed for the following tasks. Simulating, computing maximum likelihood 
             estimator, computing the Fisher information matrix, computing goodness-of-fit measures,
             and correcting bias of the ML estimator for a wide range of distributions fitted to units
             placed on progressive type-I interval censoring and progressive type-II censoring plans.  
             The methods of Cox and Snell (1968) <doi:10.1111/j.2517-6161.1968.tb00724.x> and
             bootstrap method for computing the bias-corrected maximum likelihood estimator.

Implementation of bivariate binomial, geometric, and Poisson distributions based on conditional specifications. The package also includes tools for data generation and goodness-of-fit testing for these three distribution families. For methodological details, see Ghosh, Marques, and Chakraborty (2025) <doi:10.1080/03610926.2024.2315294>, Ghosh, Marques, and Chakraborty (2023) <doi:10.1080/03610918.2021.2004419>, and Ghosh, Marques, and Chakraborty (2021) <doi:10.1080/02664763.2020.1793307>.