Module stats 

Traditional descriptive statistics for vector data.

This module provides high-level statistical operations built on top of Trueno’s SIMD-optimized primitives. Key features:

• Quantiles and percentiles using R-7 method (Hyndman & Fan 1996)
• Five-number summary (min, Q1, median, Q3, max)
• Histograms with multiple bin selection methods
• Hypothesis testing (t-tests, chi-square, ANOVA)
• Covariance and correlation matrices
• Optimized with Toyota Way principles (QuickSelect for O(n) quantiles)

Examples

use aprender::stats::DescriptiveStats;
use trueno::Vector;

let data = Vector::from_slice(&[1.0, 2.0, 3.0, 4.0, 5.0]);
let stats = DescriptiveStats::new(&data);

assert_eq!(stats.quantile(0.5).expect("median should be computable for valid data"), 3.0); // median
assert_eq!(stats.quantile(0.0).expect("min quantile should be computable for valid data"), 1.0); // min
assert_eq!(stats.quantile(1.0).expect("max quantile should be computable for valid data"), 5.0); // max

Modules

covariance

Covariance and correlation computations.

hypothesis

Statistical Hypothesis Testing

Structs

AnovaResult

Result of an ANOVA F-test.

ChiSquareResult

Result of a chi-square test.

DescriptiveStats

Descriptive statistics computed on a vector of f32 values.

FiveNumberSummary

Five-number summary: minimum, Q1, median, Q3, maximum.

Histogram

Histogram representation with bin edges and counts.

TTestResult

Result of a t-test.

Enums

BinMethod

Bin selection methods for histogram construction.

Functions

chisquare

Chi-square goodness-of-fit test: Tests if observed frequencies match expected.

corr

Computes the Pearson correlation coefficient between two vectors.

corr_matrix

Computes the Pearson correlation matrix for a data matrix.

cov

Computes the covariance between two vectors.

cov_matrix

Computes the covariance matrix for a data matrix.

f_oneway

One-way ANOVA: Tests if multiple groups have the same mean.

ttest_1samp

One-sample t-test: Tests if sample mean differs from population mean.

ttest_ind

Independent two-sample t-test: Tests if two independent samples have different means.

ttest_rel

Paired t-test: Tests if paired samples have different means.