Connects to QBRC's EntrezToProbe engine system to handle
mappings between probes and genes and provide access to
information about probes and genes.

Statistical Tools for Probing Moderation Effects
person("Jiat Chow", "Tan", role = c("aut", "cre"), email = "w110013@ntu.edu.sg")
Contains functions that are useful for probing moderation effects including techniques such as pick-a-point, Johnson-Neyman. Includes plot function for Johnson-Neyman.

Contains functions that are useful for probing moderation effects (or interactions) including techniques such as pick-a-point (also known as spotlight analysis) and Johnson-Neyman (also known as floodlight analysis). Plot function is also provided to facilitate visualization of results from each of these techniques.

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.

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.
intoo, bivariate, mvtnorm, fclust, moments, scatterplot3d

Multivariate Generalized Kernel Smoothing and Related
Statistical Methods
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.