Data sets for the chapter "Ensemble Postprocessing with R" of the book Stephane Vannitsem, Daniel S. Wilks, and Jakob W. Messner (2018) "Statistical Postprocessing of Ensemble Forecasts", Elsevier, 362pp. These data sets contain temperature and precipitation ensemble weather forecasts and corresponding observations at Innsbruck/Austria. Additionally, a demo with the full code of the book chapter is provided.

User friendly interface based on the R package gstat to fit exponential parametric models to empirical semi-variograms in order to model the spatial correlation structure of health data. Geo-located health outcomes of survey participants may be used to model spatial effects on health in an ego-centred approach. The package contains a range of functions to help explore the spatial structure of the data as well as visualize the fit of exponential models for various metaparameter combinations with respect to the number of lag intervals and maximal distance. Furthermore, the outcome of interest can be adjusted for covariates by fitting a linear regression in a preliminary step before the semi-variogram fitting process.

This package provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.

Analysis and visualization of plant disease progress curve data. Functions for fitting two-parameter population dynamics models (exponential, monomolecular, logistic and Gompertz) to proportion data for single or multiple epidemics using either linear or no-linear regression. Statistical and visual outputs are provided to aid in model selection. Synthetic curves can be simulated for any of the models given the parameters. See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058> for further information on the methods.

This package provides a built-in Nemaplex database for nematodes, which can be used to search for various nematodes. Also supports various nematode community and functional analyses such as nematode diversity, maturity index, metabolic footprint, and functional guild. The methods are based on <https://shiny.wur.nl/ninja/>, Bongers, T. (1990) <doi:10.1007/BF00324627>, Ferris, H. (2010) <doi:10.1016/j.ejsobi.2010.01.003>, Wan, B. et al. (2022) <doi:10.1016/j.soilbio.2022.108695>, and Van Den Hoogen, J. et al. (2019) <doi:10.1038/s41586-019-1418-6>.

Automatic generation of quizzes or individual questions as (interactive) forms within rmarkdown or quarto documents based on R/exams exercises.

Analytical methods to locate and characterise ecotones, ecosystems and environmental patchiness along ecological gradients. Methods are implemented for isolated sampling or for space/time series. It includes Detrended Correspondence Analysis (Hill & Gauch (1980) <doi:10.1007/BF00048870>), fuzzy clustering (De Cáceres et al. (2010) <doi:10.1080/01621459.1963.10500845>), biodiversity indices (Jost (2006) <doi:10.1111/j.2006.0030-1299.14714.x>), and network analyses (Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>) - as well as tools to explore the number of clusters in the data. Functions to produce synthetic ecological datasets are also provided.

Evaluate diagnostic test performance using data from laboratory or diagnostic research. It supports both binary and continuous test variables. It allows users to compute key performance indicators and visualize Receiver Operating Characteristic (ROC) curves, determine optimal cut-off thresholds, display confusion matrix, and export publication-ready plot. It aims to facilitate the application of statistical methods in diagnostic test evaluation by healthcare professionals. The methodology used to compute the performance indicators follows the overview described by Habibzadeh (2025) <doi:10.11613/BM.2025.010101>. Thanks to shiny package.

An interface for performing climate matching using the Euclidean "Climatch" algorithm. Functions provide a vector of climatch scores (0-10) for each location (i.e., grid cell) within the recipient region, the percent of climatch scores >= a threshold value, and mean climatch score. Tools for parallelization and visualizations are also provided. Note that the floor function that rounds the climatch score down to the nearest integer has been removed in this implementation and the â Climatchâ algorithm, also referred to as the â Climateâ algorithm, is described in: Crombie, J., Brown, L., Lizzio, J., & Hood, G. (2008). â Climatch user manualâ . The method for the percent score is described in: Howeth, J.G., Gantz, C.A., Angermeier, P.L., Frimpong, E.A., Hoff, M.H., Keller, R.P., Mandrak, N.E., Marchetti, M.P., Olden, J.D., Romagosa, C.M., and Lodge, D.M. (2016). <doi:10.1111/ddi.12391>.

An implementation of multiple-locus association mapping on a genome-wide scale. Eagle can handle inbred and outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since Eagle is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. Eagle reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run OpenGUI()'. This opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.

This package creates text, LaTeX', Markdown, or Bootstrap-styled HTML-formatted odds ratio tables with confidence intervals for multiple logistic regression models.

This package provides functions for the Bayesian analysis of extreme value models, using Markov chain Monte Carlo methods. Allows the construction of both uninformative and informed prior distributions for common statistical models applied to extreme event data, including the generalized extreme value distribution.

If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups. The exterior match is used to compare the two control groups.

Interconverts between ordered lists and compact string notation. Useful for capturing code lists, and pair-wise codes and decodes, for text storage. Analogous to factor levels and labels. Generics encode() and decode() perform interconversion, while codes() and decodes() extract components of an encoding. The function encoded() checks whether something is interpretable as an encoding. If a vector has an encoded guide attribute, as_factor() uses it to coerce to factor.

Saturation of ionic substances in urine is calculated based on sodium, potassium, calcium, magnesium, ammonia, chloride, phosphate, sulfate, oxalate, citrate, ph, and urate. This program is intended for research use, only. The code within is translated from EQUIL2 Visual Basic code based on Werness, et al (1985) "EQUIL2: a BASIC computer program for the calculation of urinary saturation" <doi:10.1016/s0022-5347(17)47703-2> to R. The Visual Basic code was kindly provided by Dr. John Lieske of the Mayo Clinic.

Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression, proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/00949655.2013.876024>. The code is based on the gbm package originally developed by Greg Ridgeway.

DNA methylation (6mA) is a major epigenetic process by which alteration in gene expression took place without changing the DNA sequence. Predicting these sites in-vitro is laborious, time consuming as well as costly. This EpiSemble package is an in-silico pipeline for predicting DNA sequences containing the 6mA sites. It uses an ensemble-based machine learning approach by combining Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting approach to predict the sequences with 6mA sites in it. This package has been developed by using the concept of Chen et al. (2019) <doi:10.1093/bioinformatics/btz015>.

This package provides a collection of functions to perform core tasks within Energy Trading and Risk Management (ETRM). Calculation of maximum smoothness forward price curves for electricity and natural gas contracts with flow delivery, as presented in F. E. Benth, S. Koekebakker, and F. Ollmar (2007) <doi:10.3905/jod.2007.694791> and F. E. Benth, J. S. Benth, and S. Koekebakker (2008) <doi:10.1142/6811>. Portfolio insurance trading strategies for price risk management in the forward market, see F. Black (1976) <doi:10.1016/0304-405X(76)90024-6>, T. Bjork (2009) <https://EconPapers.repec.org/RePEc:oxp:obooks:9780199574742>, F. Black and R. W. Jones (1987) <doi:10.3905/jpm.1987.409131> and H. E. Leland (1980) <http://www.jstor.org/stable/2327419>.

Access and interrogate EMODnet (European Marine Observation and Data Network) Web Feature Service data <https://emodnet.ec.europa.eu/en/emodnet-web-service-documentation#data-download-services>. This includes listing existing data sources, and getting data from each of them.

EB-PRS is a novel method that leverages information for effect sizes across all the markers to improve the prediction accuracy. No parameter tuning is needed in the method, and no external information is needed. This R-package provides the calculation of polygenic risk scores from the given training summary statistics and testing data. We can use EB-PRS to extract main information, estimate Empirical Bayes parameters, derive polygenic risk scores for each individual in testing data, and evaluate the PRS according to AUC and predictive r2. See Song et al. (2020) <doi:10.1371/journal.pcbi.1007565> for a detailed presentation of the method.

Work with Ecological Metadata Language ('EML') files. EML is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), <doi:10.1146/annurev.ecolsys.37.091305.110031>.

This package provides a collection of functions and jamovi module for the estimation approach to inferential statistics, the approach which emphasizes effect sizes, interval estimates, and meta-analysis. Nearly all functions are based on statpsych and metafor'. This package is still under active development, and breaking changes are likely, especially with the plot and hypothesis test functions. Data sets are included for all examples from Cumming & Calin-Jageman (2024) <ISBN:9780367531508>.

This package implements likelihood-based evidence ratios for unified reporting in classical statistical testing. The package reports effect estimates, uncertainty intervals, and likelihood ratios on the log 10 scale derived from a single statistical model. It applies to standard normal mean tests, contingency tables, and regression coefficients, and provides a direct evidential measure while retaining classical error guarantees. For the Evidence Ratio Reporting Standard see Lawless (2026) <doi:10.5281/zenodo.18261076>.

We provide functions to fit finite mixtures of multivariate normal or t-distributions to data with various factor analytic structures adopted for the covariance/scale matrices. The factor analytic structures available include mixtures of factor analyzers and mixtures of common factor analyzers. The latter approach is so termed because the matrix of factor loadings is common to components before the component-specific rotation of the component factors to make them white noise. Note that the component-factor loadings are not common after this rotation. Maximum likelihood estimators of model parameters are obtained via the Expectation-Maximization algorithm. See descriptions of the algorithms used in McLachlan GJ, Peel D (2000) <doi:10.1002/0471721182.ch8> McLachlan GJ, Peel D (2000) <ISBN:1-55860-707-2> McLachlan GJ, Peel D, Bean RW (2003) <doi:10.1016/S0167-9473(02)00183-4> McLachlan GJ, Bean RW, Ben-Tovim Jones L (2007) <doi:10.1016/j.csda.2006.09.015> Baek J, McLachlan GJ, Flack LK (2010) <doi:10.1109/TPAMI.2009.149> Baek J, McLachlan GJ (2011) <doi:10.1093/bioinformatics/btr112> McLachlan GJ, Baek J, Rathnayake SI (2011) <doi:10.1002/9781119995678.ch9>.