Package: ffaframework 0.1.2

ffaframework: Flood Frequency Analysis Framework

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.

Authors:Riley Wheadon [aut, cre], Cuauhtémoc Vidrio-Sahagún [aut], Alain Pietroniro [aut, fnd], Jianxun He [aut], Environment and Climate Change Canada [fnd]

ffaframework_0.1.2.tar.gz
ffaframework_0.1.2.zip(r-4.7-any)ffaframework_0.1.2.zip(r-4.6-any)ffaframework_0.1.2.zip(r-4.5-any)
ffaframework_0.1.2.tgz(r-4.6-any)ffaframework_0.1.2.tgz(r-4.5-any)
ffaframework_0.1.2.tar.gz(r-4.7-any)ffaframework_0.1.2.tar.gz(r-4.6-any)
ffaframework_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ffaframework/json (API)

# Install 'ffaframework' in R:
install.packages('ffaframework', repos = c('https://rileywheadon.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rileywheadon/ffa-framework/issues

Pkgdown/docs site:https://rileywheadon.github.io

Datasets:

On CRAN:

Conda:

4.48 score 1 stars 8 scripts 300 downloads 48 exports 26 dependencies

Last updated from:c5d278fed4. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK263
source / vignettesOK211
linux-releaseOK263
macos-releaseOK299
macos-oldrelOK357
windows-develOK204
windows-releaseOK210
windows-oldrelOK283
wasm-releaseOK99

Exports:data_decompositiondata_geometdata_localdata_mw_variabilitydata_screeningeda_bbmk_testeda_kpss_testeda_mk_testeda_mks_testeda_pettitt_testeda_pp_testeda_runs_testeda_sens_trendeda_spearman_testeda_white_testfit_gmlefit_lmomentsfit_lmoments_kappafit_mleframework_edaframework_ffaframework_fullmodel_assessmentplot_ams_dataplot_bbmk_testplot_lmom_diagramplot_mks_testplot_nsffa_assessmentplot_nsffa_estimatesplot_nsffa_fitplot_pettitt_testplot_runs_testplot_sffa_assessmentplot_sffa_estimatesplot_sffa_fitplot_spearman_testselect_ldistanceselect_lkurtosisselect_zstatisticuncertainty_bootstrapuncertainty_rfgpluncertainty_rfplutils_cdfutils_generalized_likelihoodutils_log_likelihoodutils_quantilesutils_sample_lmomentsutils_theoretical_lmoments

Dependencies:askpassbase64encclicpp11curlfarverggplot2gluegtablehttrisobandjsonlitelabelinglifecyclemimeopensslpatchworkR6RColorBrewerrlangS7scalessysvctrsviridisLitewithr

Characterizing Trends in the Mean
List of Tests | Case Study | Assessing Trends in the Mean | Mann-Kendall Test | Spearman Test | Further Tests | Sen's Trend Estimator | Runs Test | Conclusion
Rendered fromtrend-mean.Rmdusingknitr::rmarkdown

Last update: 2025-08-26
Started: 2025-07-17

Characterizing Trends in the Variability
List of Tests | Case Study | Assessing Trends in the Variance | MWMK Test | White Test | Sen's Trend Estimator | Runs Test | Conclusion
Rendered fromtrend-variability.Rmdusingknitr::rmarkdown

Last update: 2025-08-26
Started: 2025-07-17

Stationary FFA
Case Study | Distribution Selection | Parameter Estimation | Uncertainty Quantification | Model Assessment
Rendered fromstationary-ffa.Rmdusingknitr::rmarkdown

Last update: 2025-08-15
Started: 2025-07-09

Using Wrapper Functions for Framework Orchestration
Case Study | Exploratory Data Analysis with framework_eda | Selecting Split Point(s) | Flood Frequency Analysis with framework_full
Rendered fromwrapper-functions.Rmdusingknitr::rmarkdown

Last update: 2025-08-15
Started: 2025-08-13

Nonstationary FFA
Case Study | The ns_structure List | Distribution Selection | Parameter Estimation | Uncertainty Quantification | Model Assessment
Rendered fromnon-stationary-ffa.Rmdusingknitr::rmarkdown

Last update: 2025-08-13
Started: 2025-07-09

Change Point Detection
Case Study | The Pettitt Test | The MKS Test | Interpreting and Selecting Change Points
Rendered fromchange-points.Rmdusingknitr::rmarkdown

Last update: 2025-08-01
Started: 2025-07-09

Readme and manuals

Help Manual

Help pageTopics
Flood Frequency Analysis Frameworkffaframework-package ffaframework
CAN-05BB001CAN_05BB001
CAN-07BE001CAN_07BE001
CAN-08MH016CAN_08MH016
CAN-08NH021CAN_08NH021
CAN-08NM050CAN_08NM050
Decompose Annual Maximum Seriesdata_decomposition
Fetch Data from MSC GeoMet APIdata_geomet
Fetch Local Package Datadata_local
Estimate Variance for Annual Maximum Series Datadata_mw_variability
Perform Data Screeningdata_screening
Block-Bootstrap Mann-Kendall Test for Trend Detectioneda_bbmk_test
Kwiatkowski–Phillips–Schmidt–Shin (KPSS) Unit Root Testeda_kpss_test
Mann–Kendall Trend Testeda_mk_test
Mann–Kendall–Sneyers Test for Change Point Detectioneda_mks_test
Pettitt Test for Abrupt Changes in the Mean of a Time Serieseda_pettitt_test
Phillips–Perron Unit Root Testeda_pp_test
Wald–Wolfowitz Runs Test for Randomnesseda_runs_test
Sen's Trend Estimatoreda_sens_trend
Spearman Test for Autocorrelationeda_spearman_test
White Test for Heteroskedasticityeda_white_test
Generalized Maximum Likelihood Parameter Estimationfit_gmle
L-Moments Parameter Estimationfit_lmoments
L-Moments Parameter Estimation for the Kappa Distributionfit_lmoments_kappa
Maximum Likelihood Parameter Estimationfit_mle
Orchestrate Exploratory Data Analysisframework_eda
Orchestrate Flood Frequency Analysisframework_ffa
Orchestrate the Full FFA Frameworkframework_full
Model Assessmentmodel_assessment
Plot Annual Maximum Series Dataplot_ams_data
Plot Block‐Bootstrap Mann–Kendall Test Resultsplot_bbmk_test
Plot L-Moment Ratio Diagramplot_lmom_diagram
Plot Mann–Kendall–Sneyers (MKS) Test Resultsplot_mks_test
Plot Model Assessment for NS-FFAplot_nsffa_assessment
Plot Estimated Return Levels for NS-FFAplot_nsffa_estimates
Plot Fitted Probability Distributions for NS-FFAplot_nsffa_fit
Plot Results from the Pettitt Change‐Point Testplot_pettitt_test
Plot Runs Test Resultsplot_runs_test
Plot Model Assessment for S-FFAplot_sffa_assessment
Plot Estimated Return Levels for S-FFAplot_sffa_estimates
Plot Fitted Probability Distribution for S-FFAplot_sffa_fit
Plot Spearman’s Rho Autocorrelationplot_spearman_test
L-Distance Method for Distribution Selectionselect_ldistance
L-Kurtosis Method for Distribution Selectionselect_lkurtosis
Z-Statistic Method for Distribution Selectionselect_zstatistic
Parametric Bootstrap Uncertainty Quantificationuncertainty_bootstrap
Regula-Falsi Generalized Profile Likelihood Uncertainty Quantificationuncertainty_rfgpl
Regula-Falsi Profile Likelihood Uncertainty Quantificationuncertainty_rfpl
Cumulative Distribution Functions for Probability Modelsutils_cdf
Generalized Log-Likelihood Functions for GEV Modelsutils_generalized_likelihood
Log-Likelihood Functions for Probability Modelsutils_log_likelihood
Quantile Functions for Probability Modelsutils_quantiles
Sample L-momentsutils_sample_lmoments
Theoretical L-moments of Probability Distributionsutils_theoretical_lmoments