Species Sensitivity Distributions

Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) . The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.

Easy Access to High-Resolution Daily Climate Data for Europe

Get high-resolution (1 km) daily climate data (precipitation, minimum and maximum temperatures) for points and polygons within Europe.

Non-Homogeneous Markov and Hidden Markov Multistate Models

Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) . Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities.

Tests of Homogeneity of Variances

Most common exact, asymptotic and resample based tests are provided for testing the homogeneity of variances of k normal distributions under normality. These tests are Barlett, Bhandary & Dai, Brown & Forsythe, Chang et al., Gokpinar & Gokpinar, Levene, Liu and Xu, Gokpinar. Also, a data generation function from multiple normal distribution is provided using any multiple normal parameters. Bartlett, M. S. (1937) Bhandary, M., & Dai, H. (2008) Brown, M. B., & Forsythe, A. B. (1974). Chang, C. H., Pal, N., & Lin, J. J. (2017) Gokpinar E. & Gokpinar F. (2017) Liu, X., & Xu, X. (2010) Levene, H. (1960) < https://cir.nii.ac.jp/crid/1573950400526848896> Gökpınar, E. (2020) .

Interface to Climatic Research Unit Time-Series Version 3.21 Data

Functions for reading in and manipulating CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 data.

Monthly Climate Data for Germany, Usable for Heating and Cooling Calculations

This data package contains monthly climate data in Germany, it can be used for heating and cooling calculations (external temperature, heating / cooling days, solar radiation).

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>.

Numerical Weather Predictions

Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS < https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast>, MeteoGalicia < https://www.meteogalicia.gal/web/modelos/threddsIndex.action>, NAM < https://www.ncei.noaa.gov/products/weather-climate-models/north-american-mesoscale>, and RAP < https://www.ncei.noaa.gov/products/weather-climate-models/rapid-refresh-update>.