Download Colombian Demographic, Climate and Geospatial Data

Downloads wrangled Colombian socioeconomic, geospatial,population and climate data from DANE < https://www.dane.gov.co/> (National Administrative Department of Statistics) and IDEAM (Institute of Hydrology, Meteorology and Environmental Studies). It solves the problem of Colombian data being issued in different web pages and sources by using functions that allow the user to select the desired database and download it without having to do the exhausting acquisition process.

C3S Quality Control Tools for Historical Climate Data

Quality control and formatting tools developed for the Copernicus Data Rescue Service. The package includes functions to handle the Station Exchange Format (SEF), various statistical tests for climate data at daily and sub-daily resolution, as well as functions to plot the data. For more information and documentation see < https://datarescue.climate.copernicus.eu/st_data-quality-control>.

Download and Visualize Essential Climate Change Data

Provides easy access to essential climate change datasets to non-climate experts. Users can download the latest raw data from authoritative sources and view it via pre-defined 'ggplot2' charts. Datasets include atmospheric CO2, methane, emissions, instrumental and proxy temperature records, sea levels, Arctic/Antarctic sea-ice, Hurricanes, and Paleoclimate data. Sources include: NOAA Mauna Loa Laboratory < https://gml.noaa.gov/ccgg/trends/data.html>, Global Carbon Project < https://www.globalcarbonproject.org/carbonbudget/>, NASA GISTEMP < https://data.giss.nasa.gov/gistemp/>, National Snow and Sea Ice Data Center < https://nsidc.org/home>, CSIRO < https://research.csiro.au/slrwavescoast/sea-level/measurements-and-data/sea-level-data/>, NOAA Laboratory for Satellite Altimetry < https://www.star.nesdis.noaa.gov/socd/lsa/SeaLevelRise/> and HURDAT Atlantic Hurricane Database < https://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html>, Vostok Paleo carbon dioxide and temperature data: .

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, ).

'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.

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.

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, Universidades y Empleo, Generalitat Valenciana (grants AICO/2021/257, CIAICO/2023/031) and Ministerio de Economía e Innovación (grant PID2021-128228NB-I00) for supporting this research.

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>.

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.