• 提供了一个数据架构类来保证多种不同的数据类型（数值型、字符串型、时间数据型和通配符型），并提供了多种操作方法设置、插入、移除、排序和改变行和列

• 提供了描述统计的功能，包括求平均值、求方差、求标准差、求百分率、求中值、求四分点值、求几何平均数、求调和平均值、求均方根值、求峰值、求偏斜度等

• 提供了专门的处理功能，比如阶乘、对数阶乘、二项式系数、对数二项式、对数γ分布、不完全γ分布、β分布和不完全β分布

• 提供了求解概率密度(PDF)功能、以及累积分布(CDF)功能、求逆累积分布(CDF)功能和针对多种概率分布生成随机变量矩，支持的概率分布有：正态分布（高斯分布）、泊松分布、χ2分布、γ分布、β分布、多元线性回归、t分布、F分布、二项式分布和负二项式分布

• 提供了基本的假设检验功能，比如z检验、t检验、F检验，并支持计算p值、临界值和置信区间

• 提供了单因素变量和双因素变量的方差分析(ANOVA)和定量数据重复测量的方差分析(RANOVA)功能

• 提供了多变量统计分析功能，包括主成分分析和聚类分析

• 数据过滤，包括移动平均滤波器和Savitzky-Golay平滑滤波器

• 偏最小二乘法（PLS），包括交叉验证和SIMPLS和NIPALS算法

• 多元线性回归

• 数据聚类使用非负矩阵分解（NMF）

>>>查看NMath Stats Code Example

• 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.
• Special functions, such as factorial, log factorial, binomial coefficient, log binomial, log gamma, incomplete gamma, beta, and incomplete beta.
• Probability density function (PDF), cumulative distribution function (CDF), inverse CDF, and random variable moments for a variety of probability distributions, including beta, binomial, chi-square, exponential, F, gamma, geometric, logistic, log-normal, negative binomial, normal (Gaussian), Poisson, Student's t, triangular, and Weibull distributions.
• Multiple linear regression.
• Basic hypothesis tests, such as z test, t-test, and F-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).
• Multivariate statistical analyses, including principal component analysis, hierarchical cluster analysis, and k-means cluster analysis.
• Nonnegative matrix factorization (NMF), and data clustering using NMF.
• Partial least squares (PLS), including cross validation and the SIMPLS and NIPALS algorithms.
• Data filtering, including a moving average filter and a Savitzky-Golay smoothing filter.