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Module_Statistics
cyclone_dll edited this page Sep 13, 2026
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Location: Home / Statistics module
The Vorcyc.Mathematics.Statistics namespace provides comprehensive statistical analysis tools, including basic and advanced statistics, probability distributions, sequence comparison and search, covariance and correlation, extreme value finding, numeric extension methods, and time series analysis.
0.10.18: Holt / Holt–Winters expose terminal state (
HoltWithState/HoltWintersWithState);ForecastHoltuses the last level+trend. See Time series analysis.
Root namespace for all types in this module:
Vorcyc.Mathematics.Statistics
📒Contents
- 🔖 Basic statistics functions
- 🔖 Advanced statistics functions
- 🔖 Other statistics functions
- 🔖 Distribution functions
- 🔖 Time series analysis
- 🔖 IComparable extensions
- 🔖 ExtremeValueFinder
- 🔖 INumber extensions
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Vorcyc.Mathematics.Statistics.Basic: Basic statistics including mean, median, mode, variance, standard deviation, and coefficient of variation. -
Vorcyc.Mathematics.Statistics.Advanced: Advanced statistics including percentiles, quartiles, skewness, kurtosis, confidence intervals, hypothesis tests, ANOVA, chi-square tests, and nonparametric tests. -
Vorcyc.Mathematics.Statistics.Distribution: Probability distributions including normal, Poisson, exponential, binomial, multinomial, Gamma, and Beta. -
Vorcyc.Mathematics.Statistics.IComparableExtension: Extension methods to compare and find min/max in sequences, with parallel support. -
Vorcyc.Mathematics.Statistics.Others: Other statistics such as covariance, correlation, and simple linear regression. -
Vorcyc.Mathematics.Statistics.ExtremeValueFinder: Methods to find max and min values in sequences. -
Vorcyc.Mathematics.Statistics.INumberExtension: Numeric extension methods with SIMD optimization, including max/min search in spans. -
Vorcyc.Mathematics.Statistics.TimeSeriesAnalysis: Time series methods including smoothing, decomposition, forecasting, autocorrelation, partial autocorrelation, moving average, and exponential smoothing.
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- 🔖 ArrayExtension class
- 🔖 BaseConverter class
- 🔖 BitMathExtension class
- 🔖 Combinatorics class
- 🔖 ConstantsFp32 class
- 🔖 NumberMapper class
- 🔖 PinnableArray<T> class
- 🔖 SimpleRNG_Fp32 and SimpleRNG_Fp64 classes
- 🔖 TrigonometryHelper class
- 🔖 VMath class
- 🔖 Pinned buffers (Buffers)
- 🔖 IPinnedBuffer<T> interface
- 🔖 POHBuffer<T> class
- 🔖 NativeBuffer<T> class
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- 🔖 0.9 classifier walkthrough
- 🔖 KNN<T> class
- 🔖 MultipleLinearRegression<T> class
- 🔖 SimpleLinearRegression<T> class
- 🔖 SupportVectorMachine<TSelf> class
- 🔖 DBSCAN<T> class
- 🔖 ExpectationMaximization<T> class
- 🔖 GMM<T> class
- 🔖 HierarchicalClustering<T> class
- 🔖 KMeansClusterer<T> class
- 🔖 VectorQuantization<T> class
- 🔖 FactorAnalysis<T> class
- 🔖 PCA<TSelf> class
- 🔖 TSNE<T> class
- 🔖 Distance metric classes
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📒 Examples
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📒 GPU 策略说明
- 🔖 为何放弃主包 GPU
- 🔖 当前性能路径
- 🔖 若仍需要更强算力
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📒 计算上下文模块
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📒 核心模块
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📒 深度学习模块
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📒 线性代数模块
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📒 机器学习模块
- 🔖 0.9 分类器完整示例
- 🔖 KNN<T> 类
- 🔖 MultipleLinearRegression<T> 类
- 🔖 SimpleLinearRegression<T> 类
- 🔖 SupportVectorMachine<TSelf> 类
- 🔖 DBSCAN<T> 类
- 🔖 ExpectationMaximization<T> 类
- 🔖 GMM<T> 类
- 🔖 HierarchicalClustering<T> 类
- 🔖 KMeansClusterer<T> 类
- 🔖 VectorQuantization<T> 类
- 🔖 FactorAnalysis<T> 类
- 🔖 PCA<TSelf> 类
- 🔖 TSNE<T> 类
- 🔖 距离度量类
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📒 信号处理模块
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📒 数值模块
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📒 统计模块
- 🔖 基础统计函数
- 🔖 高级统计函数
- 🔖 稳健统计 / RunningMoments
- 🔖 其它统计函数
- 🔖 分布相关函数
- 🔖 时间序列分析
- 🔖 IComparable 扩展
- 🔖 ExtremeValueFinder
- 🔖 INumber 扩展
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📒 FFTW 扩展
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📒 示例