A collection of R functions to perform nonparametric 
    analysis of covariance for regression curves or surfaces. 
    Testing the equality or parallelism of nonparametric curves 
    or surfaces is equivalent to analysis of variance (ANOVA) or 
    analysis of covariance (ANCOVA) for one-sample functional data. 
    Three different testing methods are available in the package, 
    including one based on L-2 distance, one based on an ANOVA 
    statistic, and one based on variance estimators.

Provides the function fancycut() which is like cut() except
  you can mix left open and right open intervals with point values,
  intervals that are closed on both ends and intervals that are open on both ends.

A neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. The method is currently under peer review but an earlier draft is available in Dahl, Johnson, and Andros (2022+) <doi:10.48550/arXiv.2207.13824>.

Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a Kriging model modified by the identified structure. The interactive function plotManipulate() can only be run on the 'RStudio IDE' with 'RStudio' package 'manipulate' loaded. 'RStudio' is freely available (<https://rstudio.com/>), and includes package 'manipulate'. The equivalent function plotTk() bases on CRAN Repository packages only. For further information on the method see Fruth, J., Roustant, O., Kuhnt, S. (2014) <doi:10.1016/j.jspi.2013.11.007>.

Visualise sequential distributions using a range of plotting
    styles. Sequential distribution data can be input as either simulations or
    values corresponding to percentiles over time. Plots are added to
    existing graphic devices using the fan function. Users can choose from four
    different styles, including fan chart type plots, where a set of coloured
    polygon, with shadings corresponding to the percentile values are layered
    to represent different uncertainty levels. Full details in R Journal article; Abel (2015) <doi:10.32614/RJ-2015-002>.