Detection of Structural Changes in Climate and Environment Time Series

Tools for automatic model selection and diagnostics for Climate and Environmental data. In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models. The envcpt() function should be your first port of call.

Climate Services' Indicators Based on Sub-Seasonal to Decadal Predictions

Set of generalised tools for the flexible computation of climate related indicators defined by the user. Each method represents a specific mathematical approach which is combined with the possibility to select an arbitrary time period to define the indicator. This enables a wide range of possibilities to tailor the most suitable indicator for each particular climate service application (agriculture, food security, energy, water management, ...). This package is intended for sub-seasonal, seasonal and decadal climate predictions, but its methods are also applicable to other time-scales, provided the dimensional structure of the input is maintained. Additionally, the outputs of the functions in this package are compatible with 'CSTools'. This package is described in 'Pérez-Zanón et al. (2023) ' and it was developed in the context of 'H2020 MED-GOLD' (776467) and 'S2S4E' (776787) projects. See 'Lledó et al. (2019) ' and 'Chou et al., 2023 ' for details.

Compiles and Visualizes Wildfire, Climate, and Air Quality Data

Fetches data from three disparate data sources and allows user to perform analyses on them. It offers two core components: 1. A robust data retrieval and preparation infrastructure for wildfire, climate, and air quality index data and 2. A simple, informative, and interactive visualizations of the aforementioned datasets for California counties from 2011 through 2015. The sources of data are: wildfire data from Kaggle < https://www.kaggle.com/rtatman/188-million-us-wildfires>, climate data from the National Oceanic and Atmospheric Administration < https://www.ncdc.noaa.gov/cdo-web/token>, and air quality data from the Environmental Protection Agency < https://aqs.epa.gov/aqsweb/documents/data_api.html>.

Tools for Analyzing Climate Extremes

Functions for fitting GEV and POT (via point process fitting) models for extremes in climate data, providing return values, return probabilities, and return periods for stationary and nonstationary models. Also provides differences in return values and differences in log return probabilities for contrasts of covariate values. Functions for estimating risk ratios for event attribution analyses, including uncertainty. Under the hood, many of the functions use functions from 'extRemes', including for fitting the statistical models. Details are given in Paciorek, Stone, and Wehner (2018) .

Access Data from the Oregon State Prism Climate Project

Allows users to access the Oregon State Prism climate data (< https://prism.nacse.org/>). Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.

DMR Detection by Non-Homogeneous Hidden Markov Model from Methylation Array Data

Perform differential analysis for methylation array data. Detect differentially methylated regions (DMRs) from array M-values. The core is a Non-homogeneous Hidden Markov Model for estimating spatial correlation and a novel Constrained Gaussian Mixture Model for modeling the M-value pairs of each individual locus.

Climate Water Balance for Irrigation Purposes

Calculates daily climate water balance for irrigation purposes and also calculates the reference evapotranspiration (ET) using three methods, Penman and Monteith (Allen et al. 1998, ISBN:92-5-104219-5); Priestley and Taylor (1972) ; or Hargreaves and Samani (1985) . Users may specify a management allowed depletion (MAD), which is used to suggest when to irrigate. The functionality allows for the use of crop and water stress coefficients as well.

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: .