Orchestrate the Full FFA Framework

Runs the entire flood frequency analysis framework using the results of exploratory data analysis (EDA) to guide approach selection (stationary or nonstationary) and perform flood frequency analysis. Returns a comprehensive and reproducible summary of the results.

Usage

framework_full(
  data,
  years,
  ns_splits = NULL,
  ns_structures = NULL,
  generate_report = TRUE,
  report_path = NULL,
  report_formats = "html",
  ...
)

Arguments

data: Numeric vector of observed annual maximum series values. Must be strictly positive, finite, and not missing.
years: Numeric vector of observation years corresponding to data. Must be the same length as data and strictly increasing.
ns_splits: An integer vector of years used to split the data into homogeneous subperiods. For S-FFA, set to NULL (default). For NS-FFA, specify an integer vector of years with physical justification for change points, or NULL if no such years exist. In R, integers have the suffix L, so 1950L is a valid input to ns_splits, but 1950 is not (since R may interpret it as a floating point number).
ns_structures: For S-FFA, set to NULL (default) to use a stationary model for all homogeneous subperiods. For NS-FFA, provide a list of length(ns_splits) + 1 sublists specifying the nonstationary model structure for each homogeneous subperiod. Each sublist must contain logical elements location and scale, indicating monotonic trends in the mean and variability, respectively.
generate_report: If TRUE (default), generate a report.
report_path: A character scalar, the file path for the generated report. If NULL (default), the report will be saved to a new temporary directory.
report_formats: A character vector specifying the output format for the report. Supported values are "md", "pdf", "html", and "json".
...: Additional arguments to be passed to the statistical tests and frequency analysis functions. See the details of framework_eda() and framework_ffa() for a complete list.

Value

eda_recommendations: See framework_eda().

modelling_assumptions: See framework_ffa().

submodule_results: A list of lists of results. Each list contains:

name: Either "Change Point Detection", "Trend Detection", "Distribution Selection", "Parameter Estimation", "Uncertainty Quantification", or "Model Assessment".
start: The first year of the homogeneous subperiod.
end: The last year of the homogeneous subperiod.
Additional items specific to the the submodule.

Usage

Arguments

Value

See also