• 提供了一個數(shù)據(jù)架構類來保證多種不同的數(shù)據(jù)類型（數(shù)值型、字符串型、時間數(shù)據(jù)型和通配符型），并提供了多種操作方法設置、插入、移除、排序和改變行和列

• 提供了描述統(tǒng)計的功能，包括求平均值、求方差、求標準差、求百分率、求中值、求四分點值、求幾何平均數(shù)、求調和平均值、求均方根值、求峰值、求偏斜度等

• 提供了專門的處理功能，比如階乘、對數(shù)階乘、二項式系數(shù)、對數(shù)二項式、對數(shù)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、以及累積分布(CDF)功能、求逆累積分布(CDF)功能和針對多種概率分布生成隨機變量矩，支持的概率分布有：正態(tài)分布（高斯分布）、泊松分布、χ2分布、γ分布、β分布、多元線性回歸、t分布、F分布、二項式分布和負二項式分布

• 提供了基本的假設檢驗功能，比如z檢驗、t檢驗、F檢驗，并支持計算p值、臨界值和置信區(qū)間

• 提供了單因素變量和雙因素變量的方差分析(ANOVA)和定量數(shù)據(jù)重復測量的方差分析(RANOVA)功能

• 提供了多變量統(tǒng)計分析功能，包括主成分分析和聚類分析

• 數(shù)據(jù)過濾，包括移動平均濾波器和Savitzky-Golay平滑濾波器

• 偏最小二乘法（PLS），包括交叉驗證和SIMPLS和NIPALS算法

• 多元線性回歸

• 數(shù)據(jù)聚類使用非負矩陣分解（NMF）

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• 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.