Python API reference
Distributions
The general univariate distribution interface: 38 families with density, quantile, seeded random generation, and fitting.
| Distribution | A univariate distribution from the ported Numerics library. |
| distribution_names | List the supported distribution families. |
GEV convenience functions
Direct density, distribution, quantile, moments, and fitting functions for the Generalized Extreme Value distribution.
| dgev | GEV probability density at x. |
| pgev | GEV cumulative distribution at q. |
| qgev | GEV quantile (inverse CDF) at probability p. |
| gev_moments | GEV distribution moments and support. |
| gev_fit | Fit a GEV distribution to sample x. |
Frequency analysis
The main flood-frequency analysis entry points.
| univariate_analysis | Bayesian univariate frequency analysis. |
| fit_distributions | Fit and rank the 14 candidate distributions by maximum likelihood. |
| bulletin17c_analysis | Bulletin 17C (log-Pearson Type III) flood-frequency analysis. |
| composite_analysis | Composite frequency analysis over one child analysis per families entry. |
Model-family analyses
| mixture_analysis | Bayesian mixture-model frequency analysis. |
| competing_risk_analysis | Bayesian competing-risk frequency analysis. |
| point_process_analysis | Bayesian point-process (peaks-over-threshold) frequency analysis. |
| spatial_gev_analysis | Hierarchical spatial-GEV frequency analysis over gauged sites. |
| bivariate_analysis | Bivariate (copula) joint-exceedance frequency analysis over two fixed marginals. |
| coincident_frequency_analysis | Coincident-frequency analysis: a fitted bivariate copula + an M x N response surface. |
| rating_curve_analysis | Stage-discharge rating-curve frequency analysis. |
Time series
| ar_analysis | Bayesian autoregressive AR(p) time-series analysis. |
| ma_analysis | Bayesian moving-average MA(q) time-series analysis. |
| arima_analysis | Bayesian ARIMA(p,d,q) time-series analysis. |
| arimax_analysis | Bayesian ARIMAX(p,d,q) time-series analysis with a deterministic trend. |
MCMC
| mcmc_sample | Sample the posterior of a distribution’s parameters by MCMC. |
Uncertainty and diagnostics
| bootstrap_analysis | Parametric bootstrap confidence bands for a fitted distribution. |
| estimation_diagnostics | Bayesian estimation diagnostics (leverage / influence / prior influence). |
| prior_predictive_check | Prior predictive check: sample from the model priors, simulate, summarize. |
| posterior_predictive_check | Posterior predictive check: fit an MCMC, draw replicates, compute common p-values. |
Statistics utilities
| mgbt_test | Multiple Grubbs-Beck low-outlier test. |
| box_cox_lambda | Fit the Box-Cox transformation exponent by maximum likelihood. |
| box_cox | Box-Cox power transformation of x with exponent lambda_. |
| box_cox_inverse | Inverse Box-Cox transformation of x with exponent lambda_. |
| yeo_johnson_lambda | Fit the Yeo-Johnson transformation exponent by maximum likelihood. |
| yeo_johnson | Yeo-Johnson power transformation of x with exponent lambda_. |
| yeo_johnson_inverse | Inverse Yeo-Johnson transformation of x with exponent lambda_. |
| plotting_positions | Empirical plotting positions for a sample of size n. |
| latin_hypercube | Latin hypercube sample of uniform [0, 1] probabilities. |