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FFA Framework Wiki

The FFA Framework is an open-source tool for flood frequency analysis (FFA) designed to support systematic and repeatable workflows for stationary and nonstationary analysis. Development is ongoing at the University of Calgary and the University of Saskatchewan in Canada.

The most recent version of the framework is freely available as an R package. The original version was released as a standalone GUI with MATLAB source code and published in Vidrio-Sahagún et al. (2024). For a list of changes from the MATLAB version, see here.

Installation Instructions

From CRAN (Recommended)

Ensure that R (v4.4.0 or later) is installed on your machine.
Open an R session by executing the R command in a terminal.
Run install.packages("ffaframework") from the R session.
Exit the R session with q(). The 'ffaframework' package is now installed and ready to use.

Development Version

Warning: The development version is updated frequently and may contain issues.

Ensure that Git and R (v4.4.0 or later) are installed on your machine.
Open a terminal and navigate to the directory where you want to install the package.
Clone the Github repository by executing the following command:
```
 git clone https://github.com/rileywheadon/ffa-framework.git
```
Open an R session. Then install and load the devtools package (if not already installed):
```
 install.packages("devtools")
 library(devtools)
```
Install the 'ffaframework' package:
```
 devtools::install("~/path/to/ffa-package")
```
Replace ~/path/to/ffa-package with the path to the cloned directory from step 2.
Exit the R session with q(). The 'ffaframework' package is now installed and ready to use.

Troubleshooting FAQ

How do I know if R is installed?

You can check your R version by executing the R --version command in a terminal.

I just installed R on Windows but the R command doesn't work.

Windows users may need to add R to their PATH environment variable. The default location for R is C:\Program Files\R\R-4.5.0\bin (replace "4.5.0" with the version of R you have installed). To update the PATH, edit the system environment variables from the settings menu.

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Version

Install

install.packages('ffaframework')

Monthly Downloads

135

Version

0.1.2

License

AGPL (>= 3)

Maintainer

Riley Wheadon

Last Published

October 27th, 2025

Functions in ffaframework (0.1.2)

eda_pettitt_test

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

Mann–Kendall–Sneyers Test for Change Point Detection

Mann–Kendall Trend Test

Orchestrate Flood Frequency Analysis

Phillips–Perron Unit Root Test

Orchestrate the Full FFA Framework

param-tolerance

Parameter 'tolerance'

Parameter 'method'

Parameter 'samples'

Parameter 'ns_slice'

Maximum Likelihood Parameter Estimation

Orchestrate Exploratory Data Analysis

L-Moments Parameter Estimation

fit_lmoments_kappa

L-Moments Parameter Estimation for the Kappa Distribution

Sen's Trend Estimator

param-distribution

Parameter 'distribution'

ffaframework-package

Flood Frequency Analysis Framework

param-ns-slices

Parameter 'ns_slices'

Generalized Maximum Likelihood Parameter Estimation

plot_nsffa_assessment

Plot Model Assessment for NS-FFA

Plot Mann–Kendall–Sneyers (MKS) Test Results

param-ns-splits

Parameter 'ns_splits'

Plot Block‐Bootstrap Mann–Kendall Test Results

plot_lmom_diagram

Plot L-Moment Ratio Diagram

param-generate-report

Parameter 'generate_report'

param-report-path

Parameter 'report_path'

param-report-formats

Parameter 'report_formats'

Parameter 'years'

Compute Location and Scale of Kappa Distribution

param-ns-structures

Parameter 'ns_structures'

plot_nsffa_estimates

Plot Estimated Return Levels for NS-FFA

Parameter 'periods'

Parameter 'params'

param-ns-structure

Parameter 'ns_structure'

uncertainty_rfgpl

Regula-Falsi Generalized Profile Likelihood Uncertainty Quantification

uncertainty_bootstrap

Parametric Bootstrap Uncertainty Quantification

Plot Fitted Probability Distributions for NS-FFA

Parameter 'data'

Parameter 'alpha'

plot_pettitt_test

Plot Results from the Pettitt Change‐Point Test

Parameter 'ns_years'

select_lkurtosis

L-Kurtosis Method for Distribution Selection

select_ldistance

L-Distance Method for Distribution Selection

uncertainty_rfpl

Regula-Falsi Profile Likelihood Uncertainty Quantification

Parameter 'prior'

Plot Runs Test Results

plot_sffa_assessment

Plot Model Assessment for S-FFA

Plot Annual Maximum Series Data

Plot Fitted Probability Distribution for S-FFA

plot_spearman_test

Plot Spearman’s Rho Autocorrelation

utils_theoretical_lmoments

Theoretical L-moments of Probability Distributions

Cumulative Distribution Functions for Probability Models

select_zstatistic

Z-Statistic Method for Distribution Selection

utils_quantiles

Quantile Functions for Probability Models

sumquad_tau3tau4

Compute L-moment Distance for Kappa Distribution

utils_sample_lmoments

Sample L-moments

plot_sffa_estimates

Plot Estimated Return Levels for S-FFA

utils_log_likelihood

Log-Likelihood Functions for Probability Models

utils_generalized_likelihood

Generalized Log-Likelihood Functions for GEV Models

Fetch Local Package Data

data_decomposition

Decompose Annual Maximum Series

Fetch Data from MSC GeoMet API

data_mw_variability

Estimate Variance for Annual Maximum Series Data

Perform Data Screening

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

Wald–Wolfowitz Runs Test for Randomness

eda_spearman_test

Spearman Test for Autocorrelation

White Test for Heteroskedasticity

Block-Bootstrap Mann-Kendall Test for Trend Detection

model_assessment

Model Assessment