Download, Extract and Visualise Climate and Elevation Data

Grants access to three widely recognised modelled data sets, namely Global Climate Data (WorldClim 2), Climatologies at high resolution for the earth's land surface areas (CHELSA), and National Aeronautics and Space Administration's (NASA) Shuttle Radar Topography Mission (SRTM). It handles both multi and single geospatial polygon and point data, extracts outputs that can serve as covariates in various ecological studies. Provides two common graphic options – the Walter-Lieth (1960) < https://donum.uliege.be/bitstream/2268.1/7079/1/Walter-Lieth_Klimadiagramm-Weltatlas.pdf> climate diagram and the Holdridge (1967) < https://reddcr.go.cr/sites/default/files/centro-de-documentacion/holdridge_1966_-_life_zone_ecology.pdf> life zone classification scheme. Provides one new graphic scheme of our own design which incorporates aspects of both Walter-Leigh and Holdridge. Provides user-friendly access and extraction of globally recognisable data sets to enhance their usability across a broad spectrum of applications.

Detection and Attribution Analysis of Climate Change

Detection and attribution of climate change using methods including optimal fingerprinting via generalized total least squares or an estimating equation approach (Li et al., 2025, ; Ma et al., 2023, ). Provides shrinkage estimators for the covariance matrix following Ledoit and Wolf (2004, ) and Ledoit and Wolf (2017, ).

Improved q-Values for Discrete Uniform and Homogeneous Tests

We consider a multiple testing procedure used in many modern applications which is the q-value method proposed by Storey and Tibshirani (2003), . The q-value method is based on the false discovery rate (FDR), hence versions of the q-value method can be defined depending on which estimator of the proportion of true null hypotheses, p0, is plugged in the FDR estimator. We implement the q-value method based on two classical pi0 estimators, and furthermore, we propose and implement three versions of the q-value method for homogeneous discrete uniform P-values based on pi0 estimators which take into account the discrete distribution of the P-values.

'NOAA' Weather Data from R

Client for many 'NOAA' data sources including the 'NCDC' climate 'API' at < https://www.ncdc.noaa.gov/cdo-web/webservices/v2>, with functions for each of the 'API' 'endpoints': data, data categories, data sets, data types, locations, location categories, and stations. In addition, we have an interface for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS', tornado data via the 'NOAA' storm prediction center, and more.

Ecological Inference by Linear Programming under Homogeneity

Provides a bunch of algorithms based on linear programming for estimating, under the homogeneity hypothesis, RxC ecological contingency tables (or vote transition matrices) using mainly aggregate data (from voting units). References: Pavía and Romero (2024) . Pavía and Romero (2024) . Pavía (2023) . Pavía (2024) . Pavía (2024) . Pavía and Penadés (2024). A bottom-up approach for ecological inference. Romero, Pavía, Martín and Romero (2020) . Acknowledgements: The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257, CIAICO/2023/031) and MICIU/AEI/10.13039/501100011033/FEDER, UE (grant PID2021-128228NB-I00) for supporting this research.

Improves the Interpretation of the Standardized Precipitation Index Under Changing Climate Conditions

Improves the interpretation of the Standardized Precipitation Index under changing climate conditions. The package uses the nonstationary approach proposed in Blain et al. (2022) to detect trends in rainfall quantities and to quantify the effect of such trends on the probability of a drought event occurring.

NUtrient Cycling and COMpetition Model Undisturbed Open Bog Ecosystems in a Temperate to Sub-Boreal Climate

Modelling the vegetation, carbon, nitrogen and water dynamics of undisturbed open bog ecosystems in a temperate to sub-boreal climate. The executable of the model can downloaded from < https://github.com/jeroenpullens/NUCOMBog>.

Interface to the 'Daymet' Web Services

Programmatic interface to the 'Daymet' web services (< http://daymet.ornl.gov>). Allows for easy downloads of 'Daymet' climate data directly to your R workspace or your computer. Routines for both single pixel data downloads and gridded (netCDF) data are provided.

A Probabilistic Approach to Reconstruct Past Climates Using Palaeoecological Datasets

Applies the CREST climate reconstruction method. It can be used using the calibration data that can be obtained through the package or by importing private data. An ensemble of graphical outputs were designed to facilitate the use of the package and the interpretation of the results. More information can be obtained from Chevalier (2022) .

A Novel SAFE Model for Predicting Climate-Related Extreme Losses

The goal of 'SAFEPG' is to predict climate-related extreme losses by fitting a frequency-severity model. It improves predictive performance by introducing a sign-aligned regularization term, which ensures consistent signs for the coefficients across the frequency and severity components. This enhancement not only increases model accuracy but also enhances its interpretability, making it more suitable for practical applications in risk assessment.