VStats 0.3.0

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, time series analysis, 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

Module	Purpose	Status
linalg	Vector & matrix operations	Complete
stats	Descriptive & inferential statistics	Complete
prob	Probability distributions (PDF/CDF)	Complete
optim	Numerical optimization	Complete
utils	Metrics, datasets, feature tools	Complete
ml	Regression, classification, clustering	Complete
nn	Neural network layers & training	Complete
hypothesis	Statistical hypothesis tests	Complete
experiment	A/B testing, PSM, DiD, CUPED	Complete
growth	Funnels, cohorts, attribution	Complete
timeseries	ARIMA, VAR, decomposition, smoothing	Complete
symbol	Symbolic computation	WIP

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

• 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

Build & Test

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

Changelog

v0.3.3

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

v0.3.2

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

v0.3.1

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

v0.3.0

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

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