Description: Contains functions that are useful for probing moderation effects including techniques such as pick-a-point (also known as spotlight analysis), Johnson-Neyman (also known as floodlight analysis). Plot functions are also provided to facilitate visualization of results.

Description: Computes nonparametric probability distributions (probability density functions, cumulative distribution functions and quantile functions) using kernel smoothing. Supports univariate, multivariate and conditional distributions, and weighted data (possibly useful mixed with fuzzy clustering or frequency data). Also, supports empirical continuous cumulative distribution functions and their inverses, and random number generation.
Suggests: intoo, bivariate, mvtnorm, fclust, moments, scatterplot3d

Title: Multivariate Generalized Kernel Smoothing and Related
Description: Constructs, plots and evaluates probability distributions (probability mass/density functions, cumulative distribution functions and quantile functions) with continuous kernel smoothing, and to a lesser extent, discrete kernel smoothing. Supports univariate, multivariate and conditional distributions, including multivariate-conditional distributions. Also, supports other probability distributions (categorical, frequency and empirical-like) and weighted data, which is possibly useful mixed with fuzzy clustering. Furthermore, there are extensions for computing multivariate probabilities and multivariate random numbers, and for parameter and mode estimation.
Suggests: bivariate, fclust, scatterplot3d, MASS

Description: Mass functions, density functions, distribution functions and quantile functions via continuous kernel smoothing, and to a lesser extent, discrete kernel smoothing. Also, supports categorical distributions and smooth empirical-like distributions. There are univariate, multivariate and conditional distributions, including multivariate-conditional distributions and conditional distributions with mixed input types, along with functions for plotting univariate, bivariate and trivariate distributions. Conditional categorical distributions with mixed input types can be used for statistical classification purposes. And there are extensions for computing multivariate probabilities, multivariate random numbers, moment-based statistics, robust-based statistics and mode estimates.
Suggests: vectools, bivariate, fclust, scatterplot3d