Title: Foraminifera and Community Ecology Analyses
Description: SHE, FORAM Index and ABC Method analyses and custom plot

Title: Tools for Working with Categorical Variables (Factors)
Description: Helpers for reordering factor levels (including moving
specified levels to front, ordering by first appearance, reversing, and
randomly shuffling), and tools for modifying factor levels (including
collapsing rare levels into other, anonymising, and manually recoding).

collapsing rare levels into other, 'anonymising', and manually 'recoding').

Description: Helpers for reordering factor levels (including
moving specified levels to front, ordering by first appearance,
reversing, and randomly shuffling), and tools for modifying factor
levels (including collapsing rare levels into other, 'anonymising',
Imports: ellipsis, magrittr, rlang, tibble
Suggests: covr, ggplot2, testthat, readr, knitr, rmarkdown, dplyr

Suggests: covr, dplyr, ggplot2, knitr, readr, rmarkdown, testthat

specified levels to front, ordering by first appearance, reversing,
and randomly shuffling), and tools for modifying factor levels
(including collapsing rare levels into other, 'anonymising', and
Imports: cli, ellipsis, glue, lifecycle, magrittr, rlang (>= 1.0.0),
Suggests: covr, dplyr, ggplot2, knitr, readr, rmarkdown, testthat (>=
Packaged: 2022-08-19 19:05:11 UTC; hadleywickham

Description: For cleaning and analyses of animal closing force measurements.
'forceR' was initially written and optimized to deal with insect bite force
measurements, but can be used for any time series. Includes a full workflow
to load, plot and crop data, correct amplifier and baseline drifts,
identify individual peak shapes (bites), rescale (normalize) peak curves,
and find best polynomial fits to describe and analyze force curve shapes.
Imports: dplyr, graphics, grDevices, filesstrings, magrittr, purrr,
Suggests: ggplot2, knitr, rmarkdown, testthat (>= 3.0.0)
Author: Peter T. Rühr <peter.ruehr@gmail.com>
Maintainer: Peter T. Rühr <peter.ruehr@gmail.com>

Imports: doParallel, ggplot2 (>= 2.1.0), directlabels, cowplot, stats,
Title: Systems Insights from Generation of Hydroclimatic Timeseries
Authors@R: c(person("Bree", "Bennett", role=c("aut","cre"),email="bree.bennett@adelaide.edu.au", comment = c(ORCID = "0000-0002-2131-088X")),
person("Sam", "Culley", role=c("aut"),email="sam.culley@adelaide.edu.au", comment = c(ORCID = "0000-0003-4798-8522")),
person("Seth", "Westra", role=("aut"), email="seth.westra@adelaide.edu.au", comment = c(ORCID = "0000-0003-4023-6061")),
person("Danlu", "Guo", role=("ctb"), email="danlu.guo@adelaide.edu.au", comment = c(ORCID = "0000-0003-1083-1214")),
person("Holger", "Maier", role=("ths"), email="holger.maier@adelaide.edu.au", comment = c(ORCID = "0000-0002-0277-6887")))
Author: Bree Bennett [aut, cre] (<https://orcid.org/0000-0002-2131-088X>),
Description: A tool to create hydroclimate scenarios, stress test systems and visualize system performance in scenario-neutral climate change impact assessments. Scenario-neutral approaches 'stress-test' the performance of a modelled system by applying a wide range of plausible hydroclimate conditions (see Brown & Wilby (2012) <doi:10.1029/2012EO410001> and Prudhomme et al. (2010) <doi:10.1016/j.jhydrol.2010.06.043>). These approaches allow the identification of hydroclimatic variables that affect the vulnerability of a system to hydroclimate variation and change. This tool enables the generation of perturbed time series using a range of approaches including simple scaling of observed time series (e.g. Culley et al. (2016) <doi:10.1002/2015WR018253>) and stochastic simulation of perturbed time series via an inverse approach (see Guo et al. (2018) <doi:10.1016/j.jhydrol.2016.03.025>). It incorporates a number of stochastic weather models to generate hydroclimate variables on a daily basis (e.g. precipitation, temperature, potential evapotranspiration) and allows a variety of different hydroclimate variable properties, herein called attributes, to be perturbed. Options are included for the easy integration of existing system models both internally in R and externally for seamless 'stress-testing'. A suite of visualization options for the results of a scenario-neutral analysis (e.g. plotting performance spaces and overlaying climate projection information) are also included. As further developments in scenario-neutral approaches occur the tool will be updated to incorporate these advances.