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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)
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)
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)
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)
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>.