Statistical analysis utilities. Pure functions with zero dependencies - can be used standalone for any data analysis.
23 declarations
(values: number[], options?: StatsConfidenceIntervalOptions | undefined): [number, number] Calculate confidence interval for the mean.
valuesarray of numbers
number[]options?configuration options
StatsConfidenceIntervalOptions | undefined[number, number] [lower_bound, upper_bound]
(mean: number, std_dev: number, sample_size: number, options?: StatsConfidenceIntervalOptions | undefined): [number, number] Calculate confidence interval from summary statistics (mean, std_dev, sample_size). Useful when raw data is not available.
meanmean of the data
numberstd_devstandard deviation of the data
numbersample_sizenumber of samples
numberoptions?configuration options
StatsConfidenceIntervalOptions | undefined[number, number] [lower_bound, upper_bound]
(level: number): number Convert a confidence level (0-1) to a z-score. Uses a lookup table for common values, approximates others.
levelnumbernumber stats_confidence_level_to_z_score(0.95); // 1.96
stats_confidence_level_to_z_score(0.99); // 2.576Record<number, number> Common z-scores for confidence intervals.
(mean: number, std_dev: number): number Calculate the coefficient of variation (CV). CV = standard deviation / mean, expressed as a ratio. Useful for comparing relative variability between datasets.
meannumberstd_devnumbernumber (x: number, a: number, b: number): number Approximate regularized incomplete beta function for p-value calculation. Uses continued fraction expansion for reasonable accuracy.
xnumberanumberbnumbernumber (z: number): number Log gamma function approximation (Lanczos approximation).
znumbernumber (values: number[]): number Calculate the mean (average) of an array of numbers.
valuesnumber[]number (values: number[]): number Calculate the median of an array of numbers.
valuesnumber[]number (values: number[]): { min: number; max: number; } Calculate min and max values.
valuesnumber[]{ min: number; max: number; } (x: number): number Standard normal CDF approximation (Abramowitz and Stegun formula 7.1.26).
xnumbernumber (values: number[], options?: StatsOutliersIqrOptions | undefined): StatsOutlierResult Detect outliers using the IQR (Interquartile Range) method. Values outside [Q1 - multiplier*IQR, Q3 + multiplier*IQR] are considered outliers.
valuesnumber[]options?StatsOutliersIqrOptions | undefinedStatsOutlierResult (values: number[], options?: StatsOutliersMadOptions | undefined): StatsOutlierResult Detect outliers using the MAD (Median Absolute Deviation) method. More robust than IQR for skewed distributions. Uses modified Z-score: |0.6745 * (x - median) / MAD| Values with modified Z-score > threshold are considered outliers.
valuesnumber[]options?StatsOutliersMadOptions | undefinedStatsOutlierResult (values: number[], p: number): number Calculate a percentile of an array of numbers using linear interpolation. Uses the "R-7" method (default in R, NumPy, Excel) which interpolates between data points for more accurate percentile estimates, especially with smaller samples.
valuesarray of numbers
number[]ppercentile (0-1, e.g., 0.95 for 95th percentile)
numbernumber (values: number[], mean?: number | undefined): number Calculate the standard deviation of an array of numbers. Uses population standard deviation (divides by n, not n-1). For benchmarks with many samples, this is typically appropriate.
valuesnumber[]mean?number | undefinednumber (t: number, df: number): number Approximate two-tailed p-value from t-distribution. For large df (>100), uses normal approximation. For smaller df, uses incomplete beta function.
tabsolute value of t-statistic
numberdfdegrees of freedom
numbernumber two-tailed p-value
(values: number[], mean?: number | undefined): number Calculate the variance of an array of numbers.
valuesnumber[]mean?number | undefinednumber (mean1: number, std1: number, n1: number, mean2: number, std2: number, n2: number): StatsWelchTTestResult Calculate Welch's t-test statistic and degrees of freedom. Welch's t-test is more robust than Student's t-test when variances are unequal.
mean1mean of first sample
numberstd1standard deviation of first sample
numbern1size of first sample
numbermean2mean of second sample
numberstd2standard deviation of second sample
numbern2size of second sample
numberStatsWelchTTestResult StatsConfidenceIntervalOptions Configuration options for confidence interval calculation.
z_scoreZ-score for confidence level (default: 1.96 for 95% CI)
numberconfidence_levelConfidence level (0-1), alternative to z_score. If both provided, z_score takes precedence.
numberStatsOutlierResult Result from outlier detection.
cleanedValues after removing outliers
Array<number>outliersDetected outlier values
Array<number>StatsOutliersIqrOptions Configuration options for IQR outlier detection.
iqr_multiplierMultiplier for IQR bounds (default: 1.5)
numbermin_sample_sizeMinimum sample size to perform outlier detection (default: 3)
numberStatsOutliersMadOptions Configuration options for MAD outlier detection.
z_score_thresholdModified Z-score threshold for outlier detection (default: 3.5)
numberz_score_extremeExtreme Z-score threshold when too many outliers detected (default: 5.0)
numbermad_constantMAD constant for normal distribution (default: 0.6745)
numberoutlier_ratio_highRatio threshold to switch to extreme mode (default: 0.3)
numberoutlier_ratio_extremeRatio threshold to switch to keep-closest mode (default: 0.4)
numberoutlier_keep_ratioRatio of values to keep in keep-closest mode (default: 0.8)
numbermin_sample_sizeMinimum sample size to perform outlier detection (default: 3)
numberiqr_optionsOptions to pass to IQR fallback when MAD is zero
StatsWelchTTestResult Result from Welch's t-test calculation.
t_statisticThe t-statistic
numberdegrees_of_freedomWelch-Satterthwaite degrees of freedom
number