It provides the density, distribution function, quantile function, 
             random number generator, likelihood function, moments and Maximum Likelihood estimators for a given sample, all this for
             the three parameter Asymmetric Laplace Distribution defined 
             in Koenker and Machado (1999). This is a special case of the skewed family of distributions
             available in Galarza et.al. (2017) <doi:10.1002/sta4.140> useful for quantile regression.

Provides scalable generalized linear and mixed effects models tailored for sequence count data analysis (e.g., analysis of 16S or RNA-seq data). Uses Dirichlet-multinomial sampling to quantify uncertainty in relative abundance or relative expression conditioned on observed count data. 
    Implements scale models as a generalization of normalizations which account for uncertainty in scale (e.g., total abundances) as described in Nixon et al. (2025) <doi:10.1186/s13059-025-03609-3> and McGovern et al. (2025) <doi:10.1101/2025.08.05.668734>.

EM algorithm for estimation of parameters and other methods in a quantile regression.

The goal of the package 'aldvmm' is to fit adjusted limited 
    dependent variable mixture models of health state utilities. Adjusted 
    limited dependent variable mixture models are finite mixtures of normal 
    distributions with an accumulation of density mass at the limits, and a gap 
    between 100% quality of life and the next smaller utility value. The 
    package 'aldvmm' uses the likelihood and expected value functions proposed 
    by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using 
    normal component distributions and a multinomial logit model of 
    probabilities of component membership.

Accumulated Local Effects (ALE) were initially developed as a model-agnostic approach for global explanations of the results of black-box machine learning algorithms. ALE has a key advantage over other approaches like partial dependency plots (PDP) and SHapley Additive exPlanations (SHAP): its values represent a clean functional decomposition of the model. As such, ALE values are not affected by the presence or absence of interactions among variables in a mode. Moreover, its computation is relatively rapid. This package reimplements the algorithms for calculating ALE data and develops highly interpretable visualizations for plotting these ALE values. It also extends the original ALE concept to add bootstrap-based confidence intervals and ALE-based statistics that can be used for statistical inference. For more details, see Okoli, Chitu. 2023. “Statistical Inference Using Machine Learning and Classical Techniques Based on Accumulated Local Effects (ALE).” arXiv. <doi:10.48550/arXiv.2310.09877>.