Search Download and Handle Data from Copernicus Climate Data Service

Subset and download data from EU Copernicus Climate Data Service: < https://cds.climate.copernicus.eu/>. Import information about the Earth's past, present and future climate from Copernicus into R without the need of external software.

Bayesian Emulation of Computer Programs

Allows one to estimate the output of a computer program, as a function of the input parameters, without actually running it. The computer program is assumed to be a Gaussian process, whose parameters are estimated using Bayesian techniques that give a PDF of expected program output. This PDF is conditional on a training set of runs, each consisting of a point in parameter space and the model output at that point. The emphasis is on complex codes that take weeks or months to run, and that have a large number of undetermined input parameters; many climate prediction models fall into this class. The emulator essentially determines Bayesian posterior estimates of the PDF of the output of a model, conditioned on results from previous runs and a user-specified prior linear model. The package includes functionality to evaluate quadratic forms efficiently.

Climate Classification According to Several Indices

Classification of climate according to Koeppen - Geiger, of aridity indices, of continentality indices, of water balance after Thornthwaite, of viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy, Bagnouls-Gaussen.

Interior Point Conic Optimization Solver

A versatile interior point solver that solves linear programs (LPs), quadratic programs (QPs), second-order cone programs (SOCPs), semidefinite programs (SDPs), and problems with exponential and power cone constraints (< https://clarabel.org/stable/>). For quadratic objectives, unlike interior point solvers based on the standard homogeneous self-dual embedding (HSDE) model, Clarabel handles quadratic objective without requiring any epigraphical reformulation of its objective function. It can therefore be significantly faster than other HSDE-based solvers for problems with quadratic objective functions. Infeasible problems are detected using using a homogeneous embedding technique.

Zhang + Yue-Pilon Trends Package

An efficient implementation of the slope method described by Sen (1968) plus implementation of prewhitening approaches to determining trends in climate data described by Zhang, Vincent, Hogg, and Niitsoo (2000) and Yue, Pilon, Phinney, and Cavadias (2002) .

Dataset for Climate Analysis with Data from the Nordic Region

The Nordklim dataset 1.0 is a unique and useful achievement for climate analysis. It includes observations of twelve different climate elements from more than 100 stations in the Nordic region, in time span over 100 years. The project contractors were NORDKLIM/NORDMET on behalf of the National meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), Norway (DNMI) and Sweden (SMHI).

Multivariate Bias Correction of Climate Model Outputs

Calibrate and apply multivariate bias correction algorithms for climate model simulations of multiple climate variables. Three methods described by Cannon (2016) and Cannon (2018) are implemented — (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr), and (iii) MBC N-dimensional PDF transform (MBCn) — as is the Rank Resampling for Distributions and Dependences (R2D2) method.

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.

Agro-Climatic Data by County

The functions are designed to calculate the most widely-used county-level variables in agricultural production or agricultural-climatic and weather analyses. To operate some functions in this package needs download of the bulk PRISM raster. See the examples, testing versions and more details from: < https://github.com/ysd2004/acdcR>.

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) .