# kstats

> Kotlin Multiplatform statistics toolkit for descriptive analysis, distributions, hypothesis testing, correlation, and sampling.

## Docs

- [Descriptive Statistics](https://kstats.oremif.org/core/overview.md): Summary statistics, central tendency, dispersion, quantiles, shape measures, frequency analysis, and streaming calculations in kstats-core.
- [Correlation & Regression](https://kstats.oremif.org/correlation/overview.md): Pearson, Spearman, Kendall tau, partial correlation, point-biserial, matrices, covariance, and simple linear regression in kstats-correlation.
- [Probability Distributions](https://kstats.oremif.org/distributions/overview.md): 18 continuous and 10 discrete probability distributions with a shared API for density, CDF, quantiles, and sampling in kstats-distributions.
- [Installation](https://kstats.oremif.org/getting-started/installation.md): Add kstats to a Gradle KTS project with the BOM or individual module dependencies.
- [Introduction](https://kstats.oremif.org/getting-started/introduction.md): kstats is a Kotlin Multiplatform statistics library covering descriptive stats, distributions, hypothesis tests, correlation, and sampling.
- [Quickstart](https://kstats.oremif.org/getting-started/quickstart.md): Compute descriptive statistics, fit a distribution, run a hypothesis test, and measure correlation in five minutes.
- [A/B Testing — Compare Variants with Statistical Tests](https://kstats.oremif.org/guides/how-to/ab-testing.md): Run a complete A/B test workflow in Kotlin: summarize groups, check assumptions, choose the right test, measure effect size, and correct for multiple comparisons with kstats.
- [Building a Statistics Pipeline](https://kstats.oremif.org/guides/how-to/building-a-pipeline.md): Organize kstats calls into reusable functions for repeatable analysis workflows.
- [Choosing a Distribution](https://kstats.oremif.org/guides/how-to/choosing-a-distribution.md): Match your data to the right probability distribution using domain-driven examples and verification.
- [Exploratory Data Analysis](https://kstats.oremif.org/guides/how-to/exploratory-analysis.md): Walk through a complete EDA workflow on a single dataset using descriptive statistics, distributions, correlations, and comparisons.
- [Quality Control](https://kstats.oremif.org/guides/how-to/quality-control.md): Detect anomalies, set control limits, and monitor process stability using statistical methods.
- [Testing Assumptions](https://kstats.oremif.org/guides/how-to/testing-assumptions.md): Verify normality, variance homogeneity, and distributional fit before applying parametric methods.
- [A/B Testing with Real-World Data](https://kstats.oremif.org/guides/tutorials/ab-testing-real-world.md): Analyze two real A/B test datasets with kstats: proportion z-test, Bayesian posteriors, paired t-test, effect sizes, power analysis, and multiple comparison correction.
- [Hypothesis Tests](https://kstats.oremif.org/hypothesis/overview.md): t-tests, ANOVA, chi-squared, Fisher exact, Mann-Whitney, Wilcoxon, normality tests, and variance homogeneity tests in kstats-hypothesis.
- [Sampling & Transformation](https://kstats.oremif.org/sampling/overview.md): Ranking, z-score normalization, min-max scaling, binning, frequency tables, bootstrap, random sampling, and weighted dice in kstats-sampling.

## Optional

- [API Reference](https://oremif.github.io/kstats)
- [API Reference](https://oremif.github.io/kstats)