| Descriptive statistics | stats_descriptive | Summaries of every column, weighted statistics, correlation matrices, robust location and scale, empirical distributions and Q-Q comparisons. | 5 |
| Regression | stats_regression | Linear models: ordinary, weighted and generalized least squares, AR errors, quantile, robust, recursive, rolling and beta regression, with robust covariances and predictions. | 9 |
| Generalized linear and mixed models | stats_glm | Generalized linear models for every family and link (logistic, Poisson, gamma, inverse Gaussian, negative binomial, Tweedie), GEE for clustered and panel data (including ordinal and nominal outcomes), linear mixed models with random intercepts, slopes and variance components, Bayesian mixed GLMs and generalized additive models with spline smooths. | 6 |
| Discrete choice and count models | stats_discrete | Binary logit and probit, multinomial and conditional (fixed-effects) logit, ordered logit and probit, Poisson, negative binomial and generalized Poisson counts, zero-inflated, hurdle and truncated counts, with marginal effects, predicted probabilities and classification tables. | 7 |
| Time-series models | stats_tsa_models | Forecasting models for one series: ARIMA/SARIMAX with seasonality and regressors, autoregression and ARDL/error correction, exponential smoothing (Holt-Winters, ETS), Theta, unobserved components, Markov regime switching, STL forecasting, ARMA order selection and theoretical ARMA processes. | 11 |
| Time-series analysis | stats_tsa_analysis | Autocorrelation, partial autocorrelation and cross-correlation with Ljung-Box tests; unit-root and stationarity tests (ADF, KPSS, Zivot-Andrews, range); Engle-Granger cointegration; BDS independence and variance-break tests; classical, STL and MSTL seasonal decomposition; Hodrick-Prescott, Baxter-King and Christiano-Fitzgerald filters, polynomial detrending and the periodogram. | 9 |
| Multivariate time series | stats_tsa_multivariate | Vector autoregression with lag selection, forecasts, impulse responses, variance decompositions, Granger and instantaneous causality, whiteness, normality and stability checks; structural VAR; VARMAX; vector error correction with Johansen cointegration rank; dynamic factor models (including mixed monthly/quarterly) and pairwise Granger causality tests. | 9 |
| Hypothesis tests | stats_tests | t, z and Welch tests, equivalence (TOST), one-way, factorial and repeated-measures ANOVA, Tukey HSD and pairwise comparisons, multiple-testing corrections, proportion, binomial and Poisson-rate tests, contingency tables, runs, normality and goodness-of-fit tests, effect sizes, rater agreement, mediation and Oaxaca-Blinder decomposition. | 21 |
| Model diagnostics | stats_diagnostics | Tests on a fitted linear regression: heteroskedasticity (Breusch-Pagan, White, Goldfeld-Quandt, ARCH), residual autocorrelation (Ljung-Box, Breusch-Godfrey, Durbin-Watson), specification (RESET, rainbow, Harvey-Collier, White), structural breaks (CUSUM, Hansen), non-nested model comparison (Cox, J, encompassing), multicollinearity (VIF), influential observations and outliers, and nearest valid correlation or covariance matrices. | 8 |
| Power and sample size | stats_power | Statistical power and required sample size for t, z, ANOVA, regression F and chi-square tests (solve for effect size, sample size, alpha or power), power curves, proportion sample sizes and power, and the power of equivalence tests. | 4 |
| Multivariate analysis | stats_multivariate | Principal components (yield-curve and return PCA), factor analysis with rotations (factor models of returns, risk factors), MANOVA, canonical correlation, and tests of multivariate means and of the structure of a covariance matrix. | 6 |
| Nonparametric estimation | stats_nonparametric | Kernel density estimates of return distributions (univariate, multivariate and conditional), LOWESS smoothing, and kernel regression (local constant or local linear, optionally censored) with marginal effects. | 4 |
| Survival and duration analysis | stats_survival | Time-to-event analysis with censoring: Cox proportional hazards regression with hazard ratios and baseline hazards, Kaplan-Meier survival curves with confidence bands and median survival, log-rank and weighted tests between groups, and cumulative incidence under competing risks. | 4 |
| Missing-data imputation | stats_imputation | Multiple imputation of missing values: chained equations (MICE) with an analysis model pooled by Rubin’s rules and the fraction of missing information, and Bayesian multivariate-normal imputation of gaps in a panel of series with the posterior mean and covariance. | 2 |
