Package papaya

Interface Summary
PapayaConstants PapayaConstants stores some of the constants used in plotting.

Class Summary
BoxPlot BoxPlot class
Cast Static Class for casting Object arrays to their corresponding primitive type.
Comparison Contains a number of methods for comparing more than one dataset against each other.
Comparison.TTest Methods related to comparing two populations.
Correlation Contains utilities related to computing covariances, as well as linear and rank correlation.
Correlation.Significance Contains methods used to compute the significance, or pvalue of the input correlations.
Correlation.Weighted Contains methods related to computing the correlation and covariance of weighted datasets.
CorrelationPlot Takes in a matrix and plots the data in each of the columns versus each other.
Descriptive Basic descriptive statistics class for exploratory data analysis.
Descriptive.Mean Contains methods for computing the arithmetic, geometric, harmonic, trimmed, and winsorized means (among others).
Descriptive.Pooled Class for computing the pooled mean and variance of data sequences
Descriptive.Sum Methods for computing various different sums of datasets such as sum of inversions, logs, products, power deviations, squares, etc.
Descriptive.Weighted Contains methods related to weighted datasets.
Distance Contains methods for computing various "distance" metrics for multidimensional scaling.
Eigenvalue Eigenvalues and eigenvectors of a real matrix.
Find Static class for finding indices in an array corresponding to a given value/object.
Frequency Class for getting the frequency distribution, cumulative frequency distribution, and other distribution-related parameters of a given array of floats or ints.
Gamma Gamma and Beta functions.
Linear Contains methods related to determining the linear linear relationship between two datasets (of equal arrays) such as the best-fit linear line parameters, box-cox transformations, etc.
Linear.BoxCox Contains methods related to the Box-Cox transformation of a data set; useful in determining the best transformation that will yield the best method for converting a monotonic, non-linear relationship between x and y into a linear one.
Linear.Significance Contains methods used to compute the significance, or pvalue of the input correlations.
Linear.StdErr Contains methods related to computing the standard errors of the residuals, slope and intercept associated with the best-fit linear line.
LU LU Decomposition.
Mat Static class for performing some basic matrix operations.
MDS Contains methods for performing but classical and non-classical multidimensional scaling.
NaNs Contains various methods for dealing with NaNs in your data.
Normality Contains various utilities for checking if the dataset comes from a normal distribution.
Normality.Dago Methods for computing the skewnewss, kurtosis, and D'Agostino-Peasrson K^2 "omnibus" test-statistics (that combine the former two), and accompanying significance (or p-values) for testing the underlying population normality.
OneWayAnova Computes the one-way ANOVA p-value to test the equality of two or more sample means by analyzing the sample variances using the test statistic F = variance between samples / variance within samples.
Polynomial Static Class for casting one variable type to another.
Probability Cumulative distribution functions and corresponding inverses of certain probability distributions.
QR QR Decomposition.
Rank Ranking based on the natural ordering on floats for a sequence of data that may also contain NaNs.
ScatterPlot A simple class to plot x vs y data as a scatter plot.
Sorting Class for getting the array of indices that can be used to sort the array in ascending or descending order.
SubPlot Convenient class for drawing multiple scatter plots.
SVD Singular Value Decomposition.
Unique Class for getting and storing an unsorted array's unique elements, the indices of these elements, and the number of times the elements occur.
Visuals Visuals is the parent class behind most of the other plotting classes.

Processing library papaya by Adila Faruk. (C) 2014