The centralized empirical cumulative average deviation function is utilized to develop
             both Ada-plot and Uda-plot as alternatives to Ad-plot and Ud-plot introduced by the author.
             Analogous to Ad-plot, Ada-plot can identify symmetry, skewness, and outliers of the data
             distribution. The Uda-plot is as exceptional as Ud-plot in assessing normality. The d-value 
             that quantifies the degree of proximity between the Uda-plot and the graph of the estimated 
             normal density function helps guide to make decisions on confirmation of normality. Extreme 
             values in the data can be eliminated using the 1.5IQR rule to create its robust version if user
             demands. Full description of the methodology can be found in the article by Wijesuriya (2025a)
             <doi:10.1080/03610926.2025.2558108>. Further, the development of Ad-plot and Ud-plot is 
             contained in both article and the 'adplots' R package by Wijesuriya (2025b & 2025c) 
             <doi:10.1080/03610926.2024.2440583> and <doi:10.32614/CRAN.package.adplots>.

For distributions whose probability density functions are log-concave, the adaptive rejection sampling algorithm can be used to build envelope functions for sampling. For others, we can use the modified adaptive rejection sampling algorithm, the concave-convex adaptive rejection sampling algorithm and the adaptive slice sampling algorithm. So we designed an R package mainly including 4 functions: rARS(), rMARS(), rCCARS() and rASS(). These functions can realize sampling based on the algorithms above.

Runs projections of groups of matrix projection models (MPMs),
             allowing density dependence mechanisms to work across MPMs. This
             package was developed to run both adaptive dynamics simulations
             such as pairwise and multiple invasibility analyses, and community
             projections in which species are represented by MPMs. All forms of
             MPMs are allowed, including integral projection models (IPMs). Also
             includes individual-based modeling (IBM) versions of these.