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Accessing NOAA Climate Data Online
Fetch data from the National Oceanic and Atmospheric Administration Climate Data Online (NOAA CDO) < https://www.ncdc.noaa.gov/cdo-web/webservices/v2> API including daily, monthly, and yearly climate summaries, radar data, climatological averages, precipitation data, annual summaries, storm events, and agricultural meteorology.
Ordered Homogeneity Pursuit Lasso for Group Variable Selection
Ordered homogeneity pursuit lasso (OHPL)
algorithm for group variable selection proposed in Lin et al. (2017)
Climate Indices
Computes 138 standard climate indices at monthly, seasonal and annual resolution. These indices were selected, based on their direct and significant impacts on target sectors, after a thorough review of the literature in the field of extreme weather events and natural hazards. Overall, the selected indices characterize different aspects of the frequency, intensity and duration of extreme events, and are derived from a broad set of climatic variables, including surface air temperature, precipitation, relative humidity, wind speed, cloudiness, solar radiation, and snow cover. The 138 indices have been classified as follow: Temperature based indices (42), Precipitation based indices (22), Bioclimatic indices (21), Wind-based indices (5), Aridity/ continentality indices (10), Snow-based indices (13), Cloud/radiation based indices (6), Drought indices (8), Fire indices (5), Tourism indices (5).
Test the Homogeneity of Kappa Statistics
Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Muammer Albayrak, Kemal Turhan, Yasemin Yavuz, Zeliha Aydin Kasap (2019)
Analyze biotic homogenization of landscapes
Tools for assessing exotic species' contributions to landscape homogeneity using average pairwise Jaccard similarity and an analytical approximation derived in Harris et al. (2011, "Occupancy is nine-tenths of the law," The American Naturalist). Also includes a randomization method for assessing sources of model error.
PaleoPhyloGeographic Modeling of Climate Niches and Species Distributions
Reconstruction of paleoclimate niches using phylogenetic comparative
methods and projection reconstructed niches onto paleoclimate maps.
The user can specify various models of trait evolution or estimate the best fit
model, include fossils, use one or multiple phylogenies for inference, and make
animations of shifting suitable habitat through time. This model was first used
in Lawing and Polly (2011), and further implemented in Lawing et al (2016) and
Rivera et al (2020).
Lawing and Polly (2011)
Integrating Phylogenetics and Climatic Niche Modeling
Implements some methods in phyloclimatic modeling: estimation of ancestral climatic niches, age-range-correlation, niche equivalency test and background-similarity test.
Climate Crop Zoning Based in Air Temperature for Brazil
Climate crop zoning based in minimum and maximum air temperature. The data used in the package are from 'TerraClimate' dataset (< https://www.climatologylab.org/terraclimate.html>), but, it have been calibrated with automatic weather stations of National Meteorological Institute of Brazil. The climate crop zoning of this package can be run for all the Brazilian territory.
Non-Homogeneous Markov Switching Autoregressive Models
Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations. Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.
Hydrology and Climate Forecasting
Focuses on data processing and visualization in hydrology and climate forecasting. Main function includes data extraction, data downscaling, data resampling, gap filler of precipitation, bias correction of forecasting data, flexible time series plot, and spatial map generation. It is a good pre- processing and post-processing tool for hydrological and hydraulic modellers.