GitHub - rodabt/vstats: A dependency-free Linear Algebra, Statistics, and Machine Learning library written from scratch in V

VStats 0.2.0

A dependency-free statistics, linear algebra, and machine learning library for the V programming language, with a focus on product analytics and experimentation. Includes A/B testing, funnel analysis, cohort retention, causal inference, and growth metrics alongside classical stats and ML — all built from scratch with no external dependencies.

Installation

v install https://github.com/rodabt/vstats

Quick Start

import vstats.stats
import vstats.linalg
import vstats.experiment
import vstats.growth

// Descriptive statistics — works with int or f64
mean_val := stats.mean([1, 2, 3, 4, 5])
std_dev := stats.standard_deviation([2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0])

// Linear algebra
dot := linalg.dot([1, 2, 3], [4, 5, 6])
result := linalg.matmul(matrix_a, matrix_b)

// A/B testing
ab := experiment.abtest([10.1, 9.8, 10.2], [13.1, 12.8, 13.2])
println('p-value: ${ab.p_value:.4f}, lift: ${ab.relative_lift:.2f}')

// Funnel analysis
funnel := growth.create_funnel(
    ['Visit', 'Signup', 'Purchase'],
    [1000, 350, 120],
)
println('Overall conversion: ${funnel.conversion_rate:.2f}')

Modules

Generic Type Support

Most functions accept generic numeric types (int or f64). The convention is:

• Same-type output (linalg): linalg.add[T](v []T, w []T) []T
• f64 output (stats, ml): stats.mean[T](x []T) f64 — ensures precision
• f64-only where required: median, quantile, mode (need sorting/hashing)

Documentation

Full API reference, conceptual guides, worked examples, and module docs are available in the docs/ directory. Open docs/index.html in your browser to get started.

Build & Test

make test              # run all tests
make fulltest          # run with verbose stats
v test tests/          # same as make test
v test tests/stats_test.v   # single test file

Changelog

v0.2.0

New modules

• experiment: A/B testing (Welch's t-test, Bayesian Beta-Binomial), CUPED variance reduction, power analysis, sample size calculators, SPRT sequential testing, proportion tests
• experiment: Propensity Score Matching with balance checks and ATE estimation
• experiment: Difference-in-Differences (2×2, regression, parallel trends, event study)
• growth: Funnel analysis (stage conversion/drop-off), cohort retention tables, marketing attribution (first-touch, last-touch, linear, time-decay, position-based), growth metrics (DAU/MAU ratios, retention rates)

New features

• hypothesis: Wilcoxon signed-rank test, Mann-Whitney U test
• stats: ANOVA, confidence intervals, Cohen's d, Cramér's V, skewness, kurtosis
• utils: ROC/AUC curves, confusion matrix, feature normalization, early stopping, LR decay, grid search, built-in datasets (Iris, Wine, Breast Cancer, Boston Housing, Titanic)
• nn: Full set of loss functions (MSE, MAE, Huber, hinge, cross-entropy, KL divergence, triplet, contrastive)
• ml: SVM, Naive Bayes, Random Forest, DBSCAN, hierarchical clustering

Improvements

• Generics migration across linalg and ml (replaced _f64 suffix functions)
• Bug fixes in matrix reshape, inverse normal CDF, chi-squared approximation, silhouette coefficient
• HTML documentation site with API reference, concepts, and examples

v0.1.0

• Initial release with linalg, stats, prob, optim, utils, ml, nn modules

Disclaimer

• Written as an exercise to bring V closer to data analytics and ML workflows
• Inspired by Joel Grus's Data Science from Scratch
• Focus is on correctness and API design, not raw performance
• Contributions welcome!

References

• V Language
• Data Science from Scratch by Joel Grus