● A data frame class for holding data of various types (numeric, string, boolean, datetime, and generic), with methods for appending, inserting, removing, sorting, and permuting rows and columns.
● Functions for computing descriptive statistics, such as mean, variance, standard deviation, percentile, median, quartiles, geometric mean, harmonic mean, RMS, kurtosis, skewness, and many more.
● Probability density function (PDF), cumulative distribution function (CDF), inverse CDF, and random variable moments for a variety of probability distributions.
● Multiple linear regression and logistic regression.
● Basic hypothesis tests, such as z-test, t-test, F-test, and Pearson's chi-square test, with calculation of p-values, critical values, and confidence intervals.
● One-way and two-way analysis of variance (ANOVA) and analysis of variance with repeated measures (RANOVA).
● Non-parametric tests, such as the Kolmogorov-Smirnov test and Kruskal-Wallis rank sum test.
● Multivariate statistical analyses, including principal component analysis, factor analysis, hierarchical cluster analysis, and k-means cluster analysis.
● Nonnegative matrix factorization (NMF), and data clustering using NMF.
● Partial least squares (PLS).
● Statistical process control.