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.

Post-Processing of Climate Data for Health Applications

Obtain, transform and export climate data including reanalyses, (seasonal) forecasts and hindcasts, and weather stations for their use in epidemiological analyses. It is organised in three sequential blocks, input (download and load data), transform (downscaling, verification, spatiotemporal aggregation and threshold-based indicators) and output (visualising and exporting data). Downscaling methods include those described in Duzenli et al. (2026) and verification methods are based on those in Manubens et al. (2018) .

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) .

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.

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.

Easily Download and Visualise Climate Data from CliFlo

CliFlo is a web portal to the New Zealand National Climate Database and provides public access (via subscription) to around 6,500 various climate stations (see < https://cliflo.niwa.co.nz/> for more information). Collating and manipulating data from CliFlo (hence clifro) and importing into R for further analysis, exploration and visualisation is now straightforward and coherent. The user is required to have an internet connection, and a current CliFlo subscription (free) if data from stations, other than the public Reefton electronic weather station, is sought.

Climate and Ecological Niche Factor Analysis

Tools for climate- and ecological-niche factor analysis of spatial data, including methods for visualization of spatial variability of species sensitivity, exposure, and vulnerability to climate change. Processing of large files and parallel methods are supported. Climate-niche factor analysis is described in Rinnan and Lawler (2019) .

Estimating Climate Analogue Areas

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

A Tidy Toolbox for Climate Extreme Indices

Calculate Expert Team on Climate Change Detection and Indices (ETCCDI) <-- (acronym) climate indices from daily or hourly temperature and precipitation data. Provides flexible data handling.