u-analytics

Statistical process control, process capability analysis, Weibull reliability, change-point detection, correlation, regression, distribution analysis, and hypothesis testing for industrial quality engineering.

Modules

Features

Statistical Process Control (SPC)

Control charts for monitoring process stability:

• Variables charts: X̄-R, X̄-S, Individual-MR
• Attributes charts: P, NP, C, U
• Overdispersion-adjusted: Laney P' and U' (φ coefficient corrects for between-subgroup variation)
• Rare events: G chart (geometric distribution) and T chart (exponential) for low-defect processes
• Run rules: Nelson (8 rules), Western Electric (4 rules)

use u_analytics::spc::{XBarRChart, ControlChart};

let mut chart = XBarRChart::new(5).expect("5 is a supported subgroup size");
chart.add_sample(&[25.0, 26.0, 24.5, 25.5, 25.0]).unwrap();
chart.add_sample(&[25.2, 24.8, 25.1, 24.9, 25.3]).unwrap();
chart.add_sample(&[25.1, 25.0, 24.7, 25.3, 24.9]).unwrap();

if chart.is_in_control() {
    println!("Process is stable");
}

use u_analytics::spc::{laney_p_chart, g_chart};

// Laney P' chart for overdispersed proportion data
// samples: (defective count, subgroup size)
let samples = vec![(3u64, 100u64), (5, 120), (2, 95)];
let chart = laney_p_chart(&samples, None).unwrap(); // Some(LaneyStandard { .. }) for Phase II
println!("p̄ = {:.4}, φ = {:.4}", chart.p_bar, chart.phi);

// G chart for rare events (e.g., days between nonconformances)
let inter_event_counts = vec![12.0, 8.0, 25.0, 5.0, 18.0];
let gchart = g_chart(&inter_event_counts).unwrap();

Process Capability

Capability indices quantifying process performance against specifications:

• Short-term: Cp, Cpk, Cpu, Cpl
• Long-term: Pp, Ppk, Ppu, Ppl
• Taguchi: Cpm
• Sigma level: PPM ↔ sigma conversion (1.5σ shift convention)
• Non-normal: Box-Cox transformation + capability on transformed scale

use u_analytics::capability::{ProcessCapability, sigma_to_ppm};

let spec = ProcessCapability::new(Some(220.0), Some(200.0)).unwrap();
let data = [210.0, 209.5, 210.2, 209.8, 210.1, 210.3, 209.7, 210.0];
let indices = spec.compute(&data, 0.15).unwrap();

println!("Cp = {:.2}, Cpk = {:.2}", indices.cp.unwrap(), indices.cpk.unwrap());
println!("6σ PPM = {:.1}", sigma_to_ppm(6.0)); // 3.4

// No within-subgroup sigma (a flat vector): long-term indices only.
let overall = spec.compute_overall(&data).unwrap();
assert!(overall.pp.is_some() && overall.cp.is_none());

compute takes the within-subgroup sigma from a control chart (R-bar/d2, S-bar/c4, MR-bar/d2); compute_overall has none and so reports only Pp/Ppk/Ppu/Ppl and Cpm, leaving cp, cpk, cpu, cpl and std_dev_within None. Filling the short-term names from the overall sigma would make cp equal pp for every input.

use u_analytics::capability::{boxcox_capability, DEFAULT_LAMBDA_RANGE};

// Non-normal data: estimate λ over [-5, 5], transform spec limits, compute Ppk
let skewed_data = vec![0.5, 1.2, 0.8, 2.1, 0.3, 1.7, 0.9, 1.4];
let result = boxcox_capability(&skewed_data, Some(5.0), Some(0.1), DEFAULT_LAMBDA_RANGE).unwrap();
if result.lambda_at_bound {
    // the likelihood was still rising at an end of the range: λ is that limit
}
let ppk = result.indices.and_then(|i| i.ppk); // `indices` is None without limits
println!("λ = {:.3}, Ppk = {:?}", result.lambda, ppk);

Weibull Reliability

Parameter estimation and reliability engineering metrics:

• MLE: Maximum Likelihood Estimation (Newton-Raphson)
• MRR: Median Rank Regression (Bernard's approximation)
• Reliability: R(t), hazard rate, MTBF, B-life

use u_analytics::weibull::{weibull_mle, ReliabilityAnalysis};

let failure_times = [150.0, 200.0, 250.0, 300.0, 350.0, 400.0];
let fit = weibull_mle(&failure_times).unwrap();

let ra = ReliabilityAnalysis::from_mle(&fit);
println!("R(200h) = {:.1}%", ra.reliability(200.0) * 100.0);
println!("MTBF = {:.0}h", ra.mtbf());
println!("B10 life = {:.0}h", ra.b_life(0.10).unwrap());

Change-Point Detection

Algorithms for detecting process mean shifts:

• CUSUM: Cumulative Sum chart (Page, 1954)
• EWMA: Exponentially Weighted Moving Average (Roberts, 1959)

use u_analytics::detection::Cusum;

let cusum = Cusum::new(10.0, 1.0).unwrap();
let data = [10.1, 9.9, 10.0, 10.2, 12.0, 12.1, 11.9, 12.3];
let signals = cusum.signal_points(&data);

Spectral residual — where does the series do something its structure does not explain?

SpectralResidual scores every point without a trained model and without assuming a period (Ren et al. 2019, the method behind the Microsoft anomaly service): the log amplitude spectrum minus its moving average, transformed back with the original phase, is the saliency map, large at spikes, steps and dropouts. A point's score is its saliency relative to the judgement window before it; it is an anomaly above the threshold (τ = 3) when it also stands min_zscore standard deviations from the level before it. Each point also carries an expected value — the series with anomalies replaced by their neighbours, reconstructed from its low frequencies — and a lower/upper band whose coverage is sensitivity percent; the band is for the chart, the decision is the score.

use u_analytics::detection::SpectralResidual;

let mut series: Vec<f64> = (0..60).map(|t| (t as f64 * 0.3).sin()).collect();
series[40] += 4.0;
let points = SpectralResidual::new().analyze(&series).unwrap();
assert!(points[40].is_anomaly);
let flagged: Vec<usize> = points.iter().filter(|p| p.is_anomaly).map(|p| p.index).collect();
assert_eq!(flagged, vec![40]);

Defaults are the paper's (q = 3, z = 40, τ = 3, gate 1.5, 70% band); with_* builders change them, and with_batch_size scores a long series in consecutive batches against their own context. At least 12 points.

Seasonality — which period does the series repeat on?

Holt-Winters needs the seasonal period as an input; estimate_period finds it from the data. Two stages (Vlachos, Yu & Castelli 2005, AutoPeriod): the linearly detrended, zero-padded periodogram is searched for peaks above what the same values in random order produce (99th percentile over 100 seeded permutations — deterministic), then each peak's frequency band is refined on the autocorrelation function to the integer lag that is a local maximum above the 1.96/√n white-noise bound. The answer says explicitly when there is no period.

use u_analytics::seasonality::estimate_period;

let sawtooth: Vec<f64> = (0..40).map(|i| (i % 7) as f64).collect();
let r = estimate_period(&sawtooth).unwrap();
assert_eq!(r.period, Some(7));            // every validated candidate is in r.candidates

let line: Vec<f64> = (0..40).map(|i| 2.0 * i as f64).collect();
assert_eq!(estimate_period(&line).unwrap().period, None);

Only periods from 2 to n/2 are admissible — a cycle has to be seen twice — and a series shorter than 8 points is refused (None from the function, as opposed to period: None inside a result). Series under ~16 points rarely beat the permutation threshold: too few orderings differ from the observed one.

JavaScript / WASM (npm)

npm install @iyulab/u-analytics

The npm package ships two entry points, selected automatically via the exports map:

// Both work in Node — no bundler, no experimental flags:
const { anderson_darling_normality } = require("@iyulab/u-analytics");
// or: import { anderson_darling_normality } from "@iyulab/u-analytics";

anderson_darling_normality(new Float64Array([4.9, 5.1, 5.0, 5.2, 4.8, 5.05]));
// → { statistic, statistic_modified, p_value }

Errors. A refused input throws an Error with two extra properties:

interface AnalyticsError extends Error {
  code: string          // stable reason — branch on this, not on `message`
  index: number | null  // position of the offending element in its input array
}
// try { c_chart([1, 2, 2.5]) } catch (e) { e.code === "count_not_whole"; e.index === 2 }

message is written for people and may change between releases; code does not.

process_capability(input)

process_capability({
  data: number[],
  usl?: number,          // at least one of usl / lsl is required
  lsl?: number,
  sigma_within?: number, // short-term sigma, e.g. R-bar / d2
  target?: number,       // Cpm target; without it `cpm` is null
}): {
  mean: number,
  sigma_source: "within" | "overall",
  std_dev_within: number | null,
  std_dev_overall: number,
  cp: number | null, cpk: number | null, cpu: number | null, cpl: number | null,
  pp: number | null, ppk: number | null, ppu: number | null, ppl: number | null,
  cpm: number | null,
}

Short-term sigma has to be supplied. Cp/Cpk are defined against the within-subgroup standard deviation, which is estimated from a control chart (R-bar/d2 or S-bar/c4) and is not recoverable from a flat measurement vector — the subgroup structure is gone. Omit sigma_within and sigma_source comes back as "overall" with cp, cpk, cpu, cpl and std_dev_within all null: only the long-term indices (Pp/Ppk) are reported. Filling the short-term names with the long-term sigma instead would make cp equal pp for every input.

Cpm needs a target. It measures how closely the data cluster about the target -- min(T - LSL, USL - T) / (3 * sqrt(sum((x - T)^2) / (n - 1))) -- so it uses neither sigma and is reported with or without sigma_within. Without a target it is null; pass the specification midpoint if that is the target.

xbar_r_chart returns sigma_hat (R-bar / d2) for exactly this purpose, so the two compose:

const chart = xbar_r_chart(subgroups);
const cap = process_capability({
  data: subgroups.flat(), usl: 11, lsl: 9, sigma_within: chart.sigma_hat,
});
// cap.sigma_source === "within", cap.cp !== cap.pp

One-sided specifications are supported: pass only usl or only lsl. The indices that need both limits (cp, pp, cpm) come back null.

Choosing which run tests apply

All eight Nelson tests run by default. A process that is known to trip one of them for a benign reason -- a deliberately drifting tool, a bimodal fixture -- otherwise has to have that signal filtered out downstream, after it has already been counted as out of control. xbar_r_chart takes an optional second argument naming the tests to apply:

// Western Electric only
xbar_r_chart(subgroups, { rules: [
  "BeyondLimits", "NineOneSide", "TwoOfThreeBeyond2Sigma", "FourOfFiveBeyond1Sigma",
]});

// Control limits and nothing else
xbar_r_chart(subgroups, { rules: [] });

The names are the same values each point's violations reports, so the set is written in the vocabulary the output already uses: BeyondLimits, NineOneSide, SixTrend, FourteenAlternating, TwoOfThreeBeyond2Sigma, FourOfFiveBeyond1Sigma, FifteenWithin1Sigma, EightBeyond1Sigma.

Omitting the argument -- or passing undefined, null, or an object without rules -- applies all eight, so existing calls are unaffected. In Rust the same choice is RuleSet, passed to a chart with with_rules.

X-bar-S and Individual-MR charts

type RuleName = "BeyondLimits" | "NineOneSide" | "SixTrend" | "FourteenAlternating"
              | "TwoOfThreeBeyond2Sigma" | "FourOfFiveBeyond1Sigma"
              | "FifteenWithin1Sigma" | "EightBeyond1Sigma";
type Point = { index: number, value: number, violations: RuleName[] };

xbar_s_chart(subgroups: number[][], options?: { rules?: RuleName[] }): {
  xbar_cl: number, xbar_ucl: number, xbar_lcl: number,
  s_cl: number, s_ucl: number, s_lcl: number,
  sigma_hat: number | null,      // S-bar / c4
  xbar_points: Point[], s_points: Point[], in_control: boolean,
}

imr_chart(values: number[], options?: { rules?: RuleName[] }): {
  i_cl: number, i_ucl: number, i_lcl: number,
  mr_cl: number, mr_ucl: number, mr_lcl: number,
  sigma_hat: number | null,      // MR-bar / d2(2)
  i_points: Point[],
  mr_points: Point[],            // starts at index 1: the first value has no moving range
  in_control: boolean,
}

xbar_s_chart takes the same subgroup matrix as xbar_r_chart; above about ten values per subgroup the standard deviation is the better estimate of spread, because the range uses only the two extremes. Both return sigma_hat for process_capability's sigma_within, like xbar_r_chart. Subgroups must all have the same size; a ragged one is reported by its row rather than skipped, since skipping it would shift the index of every point after it.

Applying run tests to your own series

run_rules(
  values: number[],
  limits: { ucl: number, cl: number, lcl: number },   // lcl <= cl <= ucl
  options?: { rules?: RuleName[] },
): Point[]   // one entry per value, in input order

The same engine the charts use, on its own -- for a statistic the crate does not chart, or for limits fixed from an earlier study:

run_rules([10.1, 10.4, 9.8, 12.9], { ucl: 12, cl: 10, lcl: 8 });
// → [{ index: 0, value: 10.1, violations: [] }, ...,
//    { index: 3, value: 12.9, violations: ["BeyondLimits"] }]

Given a chart's own points and limits, it finds exactly the violations the chart reported.

Attributes charts

type AttrPoint = { index: number, value: number, ucl: number, cl: number, lcl: number,
                   out_of_control: boolean,
                   z: number | null };   // (value − cl) / σᵢ — null when σᵢ is 0

p_chart(samples: [defectives: number, size: number][], options?: { p_bar?: number }):
  { p_bar: number, points: AttrPoint[], in_control: boolean }
laney_p_chart(samples: [defectives: number, size: number][],
              options?: { p_bar?: number, phi?: number }):   // both or neither
  { p_bar: number, phi: number, points: AttrPoint[] }
np_chart(defectives: number[], sample_size: number):
  { cl: number, ucl: number, lcl: number, points: AttrPoint[], in_control: boolean }
c_chart(defects: number[]):
  { cl: number, ucl: number, lcl: number, points: AttrPoint[], in_control: boolean }
u_chart(samples: [defects: number, units: number][], options?: { u_bar?: number }):
  { u_bar: number, points: AttrPoint[], in_control: boolean }
laney_u_chart(samples: [defects: number, units: number][],
              options?: { u_bar?: number, phi?: number }):   // both or neither
  { u_bar: number, phi: number, points: AttrPoint[] }

Phase I and Phase II. Without options the centre (and φ) are estimated from samples — a Phase I study. To judge later samples against it, pass that study's p_bar/u_bar (and phi for the Laney charts): every limit then uses the Phase I values with each sample's own size, and a single sample is enough. Charting Phase I and Phase II together instead would let a Phase II shift pull the centre toward itself and widen its own limits. A p_bar outside (0, 1), a u_bar ≤ 0 or a negative phi is refused as standard_out_of_range; a Laney standard with only one of its two parts, or a key the chart does not take, is refused rather than completed or ignored.

const phaseOne = laney_p_chart(firstTwelve)              // { p_bar, phi, points }
laney_p_chart(later, { p_bar: phaseOne.p_bar, phi: phaseOne.phi })

Run rules when the sample size varies. P, U and the Laney charts have a different pair of limits at every point, so zones on the original scale do not exist. Each point carries z, its distance from the centre line in its own standard errors; on that scale every limit is ±3 (the standardized control chart, Montgomery §7.2.2):

const chart = p_chart(samples)
run_rules(chart.points.map(p => p.z), { ucl: 3, cl: 0, lcl: -3 })

These charts judge each point against its limits only; for an NP or C chart, whose limits are constant, pass values and the chart's limits to run_rules directly.

A row the chart cannot use -- a count that is not a whole number, more defectives than the sample size, a sample size of zero, units that are not positive -- is rejected with its code and index (see Errors above). The charts would otherwise drop it, and every later point would carry the index of the wrong row.

CUSUM and EWMA — sequential shift detection

The Shewhart charts above judge each point on its own, which makes them slow to notice a small but persistent shift. These two accumulate evidence across points instead, and are the standard answer for that case.

cusum(input: {
  data: number[],          // observations, in order; must be non-empty
  target: number,          // process target mean (mu_0)
  sigma: number,           // known process sigma; must be > 0
  k?: number,              // reference value / allowance, default 0.5
  h?: number,              // decision interval, default 5.0
}): {
  h: number,               // echoed, so the boundary can be drawn without restating the input
  points: { index: number, s_upper: number, s_lower: number, signal: boolean }[],
  signal_indices: number[],
  in_control: boolean,
}

ewma(input: {
  data: number[],
  target: number,
  sigma: number,
  lambda?: number,         // smoothing constant in (0, 1], default 0.2
  l_factor?: number,       // limit width factor, default 3.0
}): {
  points: { index: number, ewma: number, ucl: number, lcl: number, signal: boolean }[],
  signal_indices: number[],
  in_control: boolean,
}

// A 2-sigma shift that begins at observation 10
const data = [...Array(10).fill(10), ...Array(10).fill(12)];
cusum({ data, target: 10, sigma: 1 }).signal_indices;  // → indices inside the shifted half

CUSUM's limit is a single number, EWMA's is per point. Both cumulative sums are on the standardized scale (z = (x - target) / sigma) and start at zero, so they compare against h directly — hence h on the chart rather than on each point. EWMA's limits are the exact (not asymptotic) ones, so they widen with the index and are returned per point.

The defaults are the values their sources recommend: k = 0.5 is optimal for a 1-sigma shift and h = 5 gives ARL_0 ≈ 465 (Page 1954); lambda = 0.2 with L = 3 is sensitive across 0.5–2.0 sigma shifts (Roberts 1959). Both reject an empty data and any parameter outside its domain, naming the offending one.

Non-normal capability, and sigma level ↔ PPM

boxcox_capability(input: {
  data: number[],          // >= 4 observations, all strictly positive
  usl?: number,            // optional; each must be positive
  lsl?: number,            // with neither limit, only lambda is estimated (indices null)
  lambda_range?: [number, number],  // search range, default [-5, 5] (Minitab's)
}): {
  lambda: number,          // ML-estimated optimal Box-Cox parameter within lambda_range
  lambda_at_bound: boolean,  // true: likelihood still rising at an end of the range —
                             // lambda is that limit, not an interior optimum
  pp: number | null, ppk: number | null, ppu: number | null, ppl: number | null,
  cp: null, cpk: null, cpu: null, cpl: null,   // always null — see below
  cpm: number | null,
}

sigma_to_ppm(sigma: number): number   // 6 → ~3.4,  3 → ~66807
ppm_to_sigma(ppm: number): number     // inverse; ppm must be inside (0, 1e6)

Only the long-term indices are reported. data is a flat vector, so there is no rational subgrouping and no within-subgroup sigma to estimate. Computing cp/cpk from the overall sigma instead would make cp equal pp for every input — a long-term number wearing a short-term name — so they come back null, the same choice process_capability makes when sigma_within is omitted.

Every index is on the transformed scale, which is where the normal-theory formulas hold; they are not comparable to indices computed on the raw non-normal data. The specification limits are transformed with the same lambda.

sigma_to_ppm uses the Motorola convention, including the 1.5-sigma shift (PPM = 10^6 · (1 − Φ(σ − 1.5))). Six sigma therefore reports ~3.4 PPM rather than the ~0.002 PPM of an unshifted normal tail — if you are checking against a table, check which convention it uses. ppm_to_sigma is the exact inverse on the same convention and rejects both ends of (0, 1e6), which the sigma scale does not reach. The round trip closes to ~3·10⁻⁴ (the inverse normal CDF is a rational approximation).

The remaining exports, briefly

Every export takes plain JSON values and returns one; a rejected input throws an Error carrying code and index (see Errors). The shapes below are the ones the sections above have not already spelled out.

// Rare-event charts
g_chart(gaps: number[]):  { g_bar: number, points: AttrPoint[] }    // events between occurrences
t_chart(times: number[]): { t_bar: number, points: AttrPoint[] }    // time between occurrences

// Normality
anderson_darling_normality(data: number[]):
  { statistic: number, statistic_modified: number, p_value: number }

// Changepoints — PELT (Killick et al., 2012)
detect_changepoints(input: {
  data: number[],
  cost?: "l2" | "normal",        // default "l2": mean shift; "normal": mean + variance
  penalty?: "bic" | number,      // default "bic"
  min_segment_len?: number,      // default 2, must be >= 2
}): { changepoints: number[], n_segments: number }
detect_changepoints_multi(input: { signals: number[][], cost?, penalty?, min_segment_len? }):
  { changepoints: number[], n_segments: number }   // channels of equal length

// Seasonality — AutoPeriod (Vlachos et al., 2005); >= 8 finite values
estimate_period(input: { data: number[] }): {
  period: number | null,          // null = no periodicity passed both stages (explicit, not an error)
  candidates: { period: number, acf: number, bin: number, power: number, power_share: number }[],
  n: number, acf_threshold: number, power_threshold: number,
}

// Spectral residual anomaly scoring — Ren et al. (2019); >= 12 finite values
spectral_residual(input: {
  data: number[],
  averaging_window?: number,      // default 3  (q)
  judgement_window?: number,      // default 40 (z)
  threshold?: number,             // default 3  (τ, on the relative saliency score)
  min_zscore?: number,            // default 1.5; 0 disables the gate
  sensitivity?: number,           // default 70: coverage % of the expected-value band
  batch_size?: number,            // score in consecutive batches (>= 12)
}): {
  points: { index, value, saliency, score, expected, lower, upper: number, is_anomaly: boolean }[],
  anomalies: number[],            // indices with is_anomaly
}

// Gage R&R — measurements[part][operator][trial]
gage_rr_xbar_r(input: { measurements: number[][][], tolerance?: number }): {
  ev, av, grr, pv, tv, percent_ev, percent_av, percent_grr, percent_pv: number,
  percent_tolerance: number | null, ndc: number, status: "Acceptable" | "Marginal" | "Unacceptable",
}
gage_rr_anova(input: { measurements: number[][][], tolerance?: number }): {
  anova_table: { source, df, ss, ms, f_value: number | null, p_value: number | null }[],
  variance_components: { part, operator, interaction, repeatability, reproducibility, total },
  ev, av, grr, pv, tv, percent_grr: number, percent_tolerance: number | null,
  ndc: number, status: string, interaction_significant: boolean, interaction_pooled: boolean,
}

// Non-parametric (percentile) capability — >= 20 observations, at least one limit
percentile_capability(input: { data: number[], usl?: number, lsl?: number }): {
  cp_star, cpk_star, cpu_star, cpl_star: number | null,
  median: number, percentile_lower: number, percentile_upper: number,
}

C FFI (NuGet UAnalytics)

The ffi feature builds a cdylib with a C ABI for hosts that cannot load WebAssembly — .NET through the UAnalytics NuGet package, or anything that can call a C function.

cargo build --release --features ffi

Every entry point is JSON-in / JSON-out with one calling convention:

int32_t uanalytics_<name>(const char *request_json, char **result_ptr);
void    uanalytics_free_string(char *ptr);   // release any string the library returned
char   *uanalytics_version(void);            // crate version; free with uanalytics_free_string

code and index are the same values the JavaScript Error carries (see Errors in the JavaScript section).

Unknown request fields are rejected (-2, naming the field) rather than ignored, so a misspelt option cannot silently change which analysis you get. The string written to *result_ptr is owned by the caller and must be released with uanalytics_free_string.

The FFI and the WASM binding share one contract. Every entry point that both transports carry parses the same request type and serialises the same response type (src/wire.rs), and a test pins that the FFI body is the serde_json rendering of the value the WASM binding emits. So the schemas in the JavaScript section above are the FFI schemas: the request is the WASM function's arguments as one JSON object, the response is the same JSON.

The composition documented for JavaScript holds here too: imr_chart (or xbar_s_chart) returns sigma_hat, which is what process_capability needs as sigma_within to report the short-term indices — the FFI does not guess a within sigma from a flat vector any more than the WASM binding does.

Four entry points exist only on the FFI:

uanalytics_weibull_mle        { failure_times: number[] }  → { shape: number, scale: number }
uanalytics_correlation_matrix { variables: number[][] }    → { rows: number, cols: number, data: number[] } // row-major
uanalytics_simple_regression  { x: number[], y: number[] } → { slope, intercept, r_squared, adjusted_r_squared,
                                                                slope_se, intercept_se: number }
uanalytics_fit_best           { data: number[] }           → { distribution: string, parameters: [name: string, value: number][],
                                                                log_likelihood, aic, bic: number }[] // ascending AIC

The .NET client (bindings/csharp/UAnalytics, package UAnalytics) wraps each entry point as a method on AnalyticsClient — XbarRChart, ImrChart, ProcessCapability, DetectChangepoints, … — serialising the arguments to the request above and returning the response as a JsonElement. A non-zero status surfaces as AnalyticsException carrying the status (Code), the error message, and the body's code and index as Reason and Index. The package follows its own version line (it is a binding, not the crate), noted in the CHANGELOG entry that changes it.

Test Status

560 lib tests (576 with `ffi`, 604 with `wasm`) + 88 doc-tests
0 clippy warnings

Dependencies

• u-numflow -- statistics, special functions, probability distributions

References

• Montgomery, D.C. (2019). Introduction to Statistical Quality Control, 8th ed.
• Nelson, L.S. (1984). "The Shewhart Control Chart -- Tests for Special Causes"
• Abernethy, R.B. (2006). The New Weibull Handbook, 5th ed.
• Page, E.S. (1954). "Continuous Inspection Schemes", Biometrika
• Roberts, S.W. (1959). "Control Chart Tests Based on Geometric Moving Averages"
• Laney, D.B. (2002). "Improved Control Charts for Attributes", Quality Engineering 14(4), 531–537
• Stephens, M.A. (1974). "EDF Statistics for Goodness of Fit", JASA 69(347), 730–737
• Box, G.E.P. & Cox, D.R. (1964). "An Analysis of Transformations", JRSS-B 26(2), 211–252

Related

• u-numflow -- Mathematical primitives
• u-insight -- Statistical analysis engine with C FFI
• u-metaheur -- Metaheuristic algorithms
• u-geometry -- Computational geometry
• u-schedule -- Scheduling framework

License

MIT