Climate Futures Toolbox

Developed as a collaboration between Earth lab and the North Central Climate Adaptation Science Center to help users gain insights from available climate data. Includes tools and instructions for downloading climate data via a 'USGS' API and then organizing those data for visualization and analysis that drive insight. Web interface for 'USGS' API can be found at < http://thredds.northwestknowledge.net:8080/thredds/reacch_climate_CMIP5_aggregated_macav2_catalog.html>.

Estimating Climate Representativeness

Offers tools to estimate the climate representativeness of reference polygons and quantifies its transformation under future climate change scenarios. Approaches described in Mingarro and Lobo (2018) and Mingarro and Lobo (2022) .

Community Climate Statistics

Computes community climate statistics for volume and mismatch using species' climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.

Wegener Center Climate Uncertainty Explorer

Methods to calculate and interpret climate change signals and time series from climate multi-model ensembles. Climate model output in binary 'NetCDF' format is read in and aggregated over a specified region to a data.frame for statistical analysis. Global Circulation Models, as the 'CMIP5' simulations, can be read in the same way as Regional Climate Models, as e.g. the 'CORDEX' or 'ENSEMBLES' simulations. The package has been developed at the 'Wegener Center for Climate and Global Change' at the University of Graz, Austria.

Climate Window Analysis

Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response. Please see van de Pol et al. (2016) and Bailey and van de Pol (2016) for details.

General Purpose Client for 'ERDDAP™' Servers

General purpose R client for 'ERDDAP™' servers. Includes functions to search for 'datasets', get summary information on 'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format. 'ERDDAP™' information: < https://upwell.pfeg.noaa.gov/erddap/information.html>.

Koeppen-Geiger Climatic Zones

Aids in identifying the Koeppen-Geiger (KG) climatic zone for a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system to classify regions of the earth by their relative heat and humidity through the year, for the benefit of human health, plant and agriculture and other human activity [1]. This climate zone classification system, applicable to all of the earths surface, has continued to be developed by scientists up to the present day. Recently one of use (FZ) has published updated, higher accuracy KG climate zone definitions [2]. In this package we use these updated high-resolution maps as the data source [3]. We provide functions that return the KG climate zone for a given longitude and lattitude, or for a given United States zip code. In addition the CZUncertainty() function will check climate zones nearby to check if the given location is near a climate zone boundary. In addition an interactive shiny app is provided to define the KG climate zone for a given longitude and lattitude, or United States zip code. Digital data, as well as animated maps, showing the shift of the climate zones are provided on the following website < http://koeppen-geiger.vu-wien.ac.at>. This work was supported by the DOE-EERE SunShot award DE-EE-0007140. [1] W. Koeppen, (2011) . [2] F. Rubel and M. Kottek, (2010) . [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) .

Maximum Homogeneity Clustering for Univariate Data

Maximum homogeneity clustering algorithm for one-dimensional data described in W. D. Fisher (1958) via dynamic programming.

Modelling and Validation of Non Homogeneous Poisson Processes

Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time.

Visualizing Hypothesis Tests in Multivariate Linear Models

Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). It also provides other tools for analysis and graphical display of the models such as robust methods and homogeneity of variance covariance matrices. The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.