Learn R Programming

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.

Copy Link

Version

Install

install.packages('ffaframework')

Version

0.1.1

License

AGPL (>= 3)

Maintainer

Riley Wheadon

Last Published

August 29th, 2025

Functions in ffaframework (0.1.1)

eda_pettitt_test

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

White Test for Heteroskedasticity

Sen's Trend Estimator

Wald–Wolfowitz Runs Test for Randomness

eda_spearman_test

Spearman Test for Autocorrelation

Block-Bootstrap Mann-Kendall Test for Trend Detection

Mann–Kendall Trend Test

Phillips–Perron Unit Root Test

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

Mann–Kendall–Sneyers Test for Change Point Detection

Orchestrate Flood Frequency Analysis

Maximum Likelihood Parameter Estimation

model_assessment

Model Assessment

Compute Location and Scale of Kappa Distribution

Orchestrate the Full FFA Framework

ffaframework-package

Flood Frequency Analysis Framework

L-Moments Parameter Estimation

fit_lmoments_kappa

L-Moments Parameter Estimation for the Kappa Distribution

Generalized Maximum Likelihood Parameter Estimation

param-generate-report

Parameter 'generate_report'

param-ns-structure

Parameter 'ns_structure'

param-distribution

Parameter 'distribution'

Parameter 'method'

Orchestrate Exploratory Data Analysis

Parameter 'periods'

param-ns-slices

Parameter 'ns_slices'

param-report-path

Parameter 'report_path'

param-ns-splits

Parameter 'ns_splits'

param-report-formats

Parameter 'report_formats'

Parameter 'params'

Plot Mann–Kendall–Sneyers (MKS) Test Results

plot_nsffa_estimates

Plot Estimated Return Levels for NS-FFA

Parameter 'ns_slice'

Plot Annual Maximum Series Data

Plot Block‐Bootstrap Mann–Kendall Test Results

Parameter 'ns_years'

plot_pettitt_test

Plot Results from the Pettitt Change‐Point Test

Plot Runs Test Results

param-tolerance

Parameter 'tolerance'

param-ns-structures

Parameter 'ns_structures'

Parameter 'years'

Parameter 'samples'

Plot Fitted Probability Distribution for S-FFA

Plot Fitted Probability Distributions for NS-FFA

plot_lmom_diagram

Plot L-Moment Ratio Diagram

uncertainty_bootstrap

Parametric Bootstrap Uncertainty Quantification

plot_nsffa_assessment

Plot Model Assessment for NS-FFA

select_ldistance

L-Distance Method for Distribution Selection

select_zstatistic

Z-Statistic Method for Distribution Selection

uncertainty_rfgpl

Regula-Falsi Generalized Profile Likelihood Uncertainty Quantification

Parameter 'alpha'

plot_spearman_test

Plot Spearman’s Rho Autocorrelation

utils_sample_lmoments

Sample L-moments

utils_quantiles

Quantile Functions for Probability Models

sumquad_tau3tau4

Compute L-moment Distance for Kappa Distribution

Parameter 'data'

plot_sffa_assessment

Plot Model Assessment for S-FFA

plot_sffa_estimates

Plot Estimated Return Levels for S-FFA

Parameter 'prior'

utils_generalized_likelihood

Generalized Log-Likelihood Functions for GEV Models

utils_theoretical_lmoments

Theoretical L-moments of Probability Distributions

select_lkurtosis

L-Kurtosis Method for Distribution Selection

utils_log_likelihood

Log-Likelihood Functions for Probability Models

uncertainty_rfpl

Regula-Falsi Profile Likelihood Uncertainty Quantification

Cumulative Distribution Functions for Probability Models

data_decomposition

Decompose Annual Maximum Series

Fetch Local Package Data

Fetch Data from MSC GeoMet API

Perform Data Screening

data_mw_variability

Estimate Variance for Annual Maximum Series Data