The empirical cumulative average deviation function introduced by the author is utilized to
             develop both Ad- and Ud-plots. The Ad-plot can identify symmetry, skewness, and outliers of the
             data distribution, including anomalies. The Ud-plot created by slightly modifying Ad-plot is
             exceptional in assessing normality, outperforming normal QQ-plot, normal PP-plot, and their 
             derivations. The d-value that quantifies the degree of proximity between the Ud-plot and the graph
             of the estimated normal density function helps guide to make decisions on confirmation of 
             normality. Full description of this methodology can be found in the article by Wijesuriya (2025) 
             <doi:10.1080/03610926.2024.2440583>.

Obtain overlapping clustering models for object-by-variable data
        matrices using the Additive Profile Clustering (ADPROCLUS) method. 
        Also contains the low dimensional ADPROCLUS method 
        for simultaneous dimension reduction and overlapping clustering. 
        For reference see Depril, Van Mechelen, Mirkin (2008) 
        <doi:10.1016/j.csda.2008.04.014> and Depril, Van Mechelen, Wilderjans 
        (2012) <doi:10.1007/s00357-012-9112-5>.

Provides the functions for planning and conducting a
  clinical trial with adaptive sample size determination. Maximal statistical
  efficiency will be exploited even when dramatic or multiple adaptations
  are made. Such a trial consists of adaptive determination of sample size
  at an interim analysis and implementation of frequentist statistical test at the
  interim and final analysis with a prefixed significance level. The required
  assumptions for the stage-wise test statistics are independent and stationary
  increments and normality. Predetermination of adaptation rule is not required.

Facilitates estimating, visualizing, and testing average dose-response functions (ADRFs) for characterizing the causal effect of a continuous (i.e., non-discrete) treatment or exposure. Includes support for frequentist and Bayesian regression models, analytical and bootstrap inference, and characterization of subgroup effects.