c(person("Jon", "Goetz", email = "jon.goetz@gov.bc.ca", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-4993-1119")),
person("Carl James", "Schwarz", email = "cschwarz.stat.sfu.ca@gmail.com", role = "aut"),
person("Sam", "Albers", email = "sam.albers@gmail.com", role = "ctb",
comment = c(ORCID = "0000-0002-9270-7884")),
person("Robin", "Pike", email = "robin.g.pike@gov.bc.ca", role = "ctb"),
person("Province of British Columbia", role = "cph"))
The Flow Analysis Summary Statistics Tool for R, 'fasstr', provides various functions to tidy and screen daily stream discharge data, calculate and visualize various summary statistics and metrics, and compute annual trending and volume frequency analyses.
It features useful function arguments for filtering of and handling dates, customizing data and metrics, and the ability to pull daily data directly from the Water Survey of Canada hydrometric database (<https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>).

Implementation of the Fourier Amplitute Sensitivity Test (FAST)
The Fourier Amplitute Sensitivity Test (FAST) is a method
to deterimine global sensitivities of a model on parameter
changes with relavtively few model runs. This package
implements this sensitivity analysis method.

The Fourier Amplitute Sensitivity Test (FAST) is a method to deterimine global sensitivities of a model on parameter changes with relavtively few model runs. This package implements this sensitivity analysis method.

Implementation of the Fourier Amplitude Sensitivity Test (FAST)
The Fourier Amplitude Sensitivity Test (FAST) is a method
to determine global sensitivities of a model on parameter
changes with relatively few model runs. This package implements
this sensitivity analysis method.

The Fourier Amplitude Sensitivity Test (FAST) is a method to determine global sensitivities of a model on parameter changes with relatively few model runs. This package implements this sensitivity analysis method.

Implements Adaboost based on C++ backend code.
This is blazingly fast and especially useful for large, in memory data sets.
The package uses decision trees as weak classifiers. Once the classifiers
have been trained, they can be used to predict new data.
Currently, we support only binary classification tasks.
The package implements the Adaboost.M1 algorithm and the real
Adaboost(SAMME.R) algorithm.