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, time series analysis, and growth metrics alongside classical stats and ML — all built from scratch with no external dependencies.

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

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}')

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)

• docs/index.html — full API reference, concepts, examples
• docs/getting-started.html — quick-start for Python/R users
• examples/ — runnable scenario files covering ML, experiment analysis, and time series

To regenerate the HTML docs after editing docs/src/:

make docs

make test              # run all tests
make fulltest          # run with verbose stats
make docs              # regenerate HTML docs from docs/src/
v test tests/          # same as make test
v test tests/stats_test.v   # single test file

experiment module improvements

• Non-parametric tests (nonparametric.v): mann_whitney_ab_test (two-sample) and wilcoxon_paired_test (paired) return rank-biserial correlation effect size and percentile bootstrap CI; both support alternative: .greater / .less
• Winsorized/trimmed means: utils.winsorize and utils.trim clip/remove outliers by percentile; experiment.winsorized_abtest and experiment.trimmed_abtest apply them before Welch's t-test for outlier-robust comparisons
• Bootstrap CI (stats/inference.v): stats.bootstrap_ci(x, stat_fn, n_boot, alpha) returns a percentile CI for any scalar statistic — mean, median, 90th percentile, or custom lambda; BootstrapCIResult carries the point estimate alongside bounds

experiment module improvements

• One-sided hypothesis tests: abtest, ancova, and proportion_test now accept alternative: .greater or alternative: .less (via TestAlternative enum) for directional p-values; CIs remain two-sided; default .two_sided is fully backward-compatible
• mSPRT for continuous metrics (sequential.v): msprt_test applies the mixture Sequential Probability Ratio Test to continuous outcome data — stateless, always-valid inference, normal-mixture prior with tunable tau_sigma_ratio; σ estimated from data or provided by caller
• Novelty/primacy detection (readout.v): novelty_primacy_check takes a per-period effect time series and returns early/late split means, OLS slope with t-test significance, and novelty_suspected / primacy_suspected flags
• CUPED for proportions: documented in cuped_test that binary 0/1 outcomes work directly — pass conversion events as 0.0/1.0 []f64 with any continuous pre-covariate

experiment module improvements

• A/B design optimizer (design_optimizer.v): find_optimal_runtime replaces the EU-based objective with a detection-rate objective — E[I(effect ≥ mde_tolerance) × power(effect, T)] × 30/T — eliminating the need for unknown annual_revenue / day_cost. Given a MixturePrior over possible true effects, the optimizer finds the runtime that maximises expected meaningful detections per month. Answers: "can we catch a ≥ N pp effect within a month, given our traffic?"
• t-distribution corrections: all p-values and CIs in abtest.v (Welch's t-test, ANCOVA) and did.v (DiD regression) now use the exact t-distribution instead of the normal approximation

New module: timeseries

• analysis: diff, seasonal_diff, undiff (differencing and inversion); acf, pacf, acf_confidence_bound (Levinson-Durbin); adf_test (MacKinnon critical values), kpss_test (Bartlett kernel); aic, bic, aicc
• decomposition: Classical additive/multiplicative decomposition (decompose); STL decomposition with iterative LOESS and robustness weights (stl)
• smoothing: Simple Exponential Smoothing (ses), Holt's double smoothing (holt), Holt-Winters triple smoothing (holt_winters); auto-optimization via Nelder-Mead (auto_ses, auto_holt, auto_holt_winters)
• arima: CSS fitting (arima_fit), forecasting with CIs (arima_forecast), model summary (arima_summary), SARIMA (sarima_fit), grid-search order selection (auto_arima)
• var: VAR(p) OLS fitting (var_fit), recursive forecasting (var_forecast), AIC/BIC/HQC lag selection (var_select_lag), Granger causality F-test (granger_causality), Cholesky-orthogonalized impulse response functions (irf)

New examples

• examples/timeseries-arima-forecast/ — unit root tests, ACF/PACF, decomposition, ARIMA fit and 8-step forecast, auto_arima
• examples/timeseries-var-granger/ — VAR lag selection, coefficient recovery, Granger causality, IRF visualization

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

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

• 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!

• V Language
• Data Science from Scratch by Joel Grus