ffaframework0.1.2 package

Flood Frequency Analysis Framework

CAN_05BB001

CAN-05BB001

CAN_07BE001

CAN-07BE001

CAN_08MH016

CAN-08MH016

CAN_08NH021

CAN-08NH021

CAN_08NM050

CAN-08NM050

data_decomposition

Decompose Annual Maximum Series

data_geomet

Fetch Data from MSC GeoMet API

data_local

Fetch Local Package Data

data_mw_variability

Estimate Variance for Annual Maximum Series Data

data_screening

Perform Data Screening

eda_bbmk_test

Block-Bootstrap Mann-Kendall Test for Trend Detection

eda_kpss_test

Kwiatkowski–Phillips–Schmidt–Shin (KPSS) Unit Root Test

eda_mk_test

Mann–Kendall Trend Test

eda_mks_test

Mann–Kendall–Sneyers Test for Change Point Detection

eda_pettitt_test

Pettitt Test for Abrupt Changes in the Mean of a Time Series

eda_pp_test

Phillips–Perron Unit Root Test

eda_runs_test

Wald–Wolfowitz Runs Test for Randomness

eda_sens_trend

Sen's Trend Estimator

eda_spearman_test

Spearman Test for Autocorrelation

eda_white_test

White Test for Heteroskedasticity

ffaframework-package

Flood Frequency Analysis Framework

fit_gmle

Generalized Maximum Likelihood Parameter Estimation

fit_lmoments_kappa

L-Moments Parameter Estimation for the Kappa Distribution

fit_lmoments

L-Moments Parameter Estimation

fit_mle

Maximum Likelihood Parameter Estimation

framework_eda

Orchestrate Exploratory Data Analysis

framework_ffa

Orchestrate Flood Frequency Analysis

framework_full

Orchestrate the Full FFA Framework

model_assessment

Model Assessment

mu_sigma

Compute Location and Scale of Kappa Distribution

param-alpha

Parameter 'alpha'

param-data

Parameter 'data'

param-distribution

Parameter 'distribution'

param-generate-report

Parameter 'generate_report'

param-method

Parameter 'method'

param-ns-slice

Parameter 'ns_slice'

param-ns-slices

Parameter 'ns_slices'

param-ns-splits

Parameter 'ns_splits'

param-ns-structure

Parameter 'ns_structure'

param-ns-structures

Parameter 'ns_structures'

param-ns-years

Parameter 'ns_years'

param-p

Parameter 'p'

param-params

Parameter 'params'

param-periods

Parameter 'periods'

param-prior

Parameter 'prior'

param-q

Parameter 'q'

param-report-formats

Parameter 'report_formats'

param-report-path

Parameter 'report_path'

param-samples

Parameter 'samples'

param-tolerance

Parameter 'tolerance'

param-years

Parameter 'years'

plot_ams_data

Plot Annual Maximum Series Data

plot_bbmk_test

Plot Block‐Bootstrap Mann–Kendall Test Results

plot_lmom_diagram

Plot L-Moment Ratio Diagram

plot_mks_test

Plot Mann–Kendall–Sneyers (MKS) Test Results

plot_nsffa_assessment

Plot Model Assessment for NS-FFA

plot_nsffa_estimates

Plot Estimated Return Levels for NS-FFA

plot_nsffa_fit

Plot Fitted Probability Distributions for NS-FFA

plot_pettitt_test

Plot Results from the Pettitt Change‐Point Test

plot_runs_test

Plot Runs Test Results

plot_sffa_assessment

Plot Model Assessment for S-FFA

plot_sffa_estimates

Plot Estimated Return Levels for S-FFA

plot_sffa_fit

Plot Fitted Probability Distribution for S-FFA

plot_spearman_test

Plot Spearman’s Rho Autocorrelation

select_ldistance

L-Distance Method for Distribution Selection

select_lkurtosis

L-Kurtosis Method for Distribution Selection

select_zstatistic

Z-Statistic Method for Distribution Selection

sumquad_tau3tau4

Compute L-moment Distance for Kappa Distribution

uncertainty_bootstrap

Parametric Bootstrap Uncertainty Quantification

uncertainty_rfgpl

Regula-Falsi Generalized Profile Likelihood Uncertainty Quantification

uncertainty_rfpl

Regula-Falsi Profile Likelihood Uncertainty Quantification

utils_cdf

Cumulative Distribution Functions for Probability Models

utils_generalized_likelihood

Generalized Log-Likelihood Functions for GEV Models

utils_log_likelihood

Log-Likelihood Functions for Probability Models

utils_quantiles

Quantile Functions for Probability Models

utils_sample_lmoments

Sample L-moments

utils_theoretical_lmoments

Theoretical L-moments of Probability Distributions

Tools to support systematic and reproducible workflows for both stationary and nonstationary flood frequency analysis, with applications extending to other hydroclimate extremes, such as precipitation frequency analysis. This package implements the FFA framework proposed by Vidrio- Sahagún et al. (2024) <doi:10.1016/j.envsoft.2024.105940>, originally developed in 'MATLAB', now adapted for the 'R' environment. This work was funded by the Flood Hazard Identification and Mapping Program of Environment and Climate Change Canada, as well as the Canada Research Chair (Tier 1) awarded to Dr. Pietroniro.

  • Maintainer: Riley Wheadon
  • License: AGPL (>= 3)
  • Last published: 2025-10-27