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>.

This package implements various quality evaluation statistics to assess the value of plant germplasm core collections using qualitative and quantitative phenotypic trait data according to Odong et al. (2015) <doi:10.1007/s00122-012-1971-y>.

Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the elmNN package using RcppArmadillo after the elmNN package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.

Evidence of Absence software (EoA) is a user-friendly application for estimating bird and bat fatalities at wind farms and designing search protocols. The software is particularly useful in addressing whether the number of fatalities has exceeded a given threshold and what search parameters are needed to give assurance that thresholds were not exceeded. The models are applicable even when zero carcasses have been found in searches, following Huso et al. (2015) <doi:10.1890/14-0764.1>, Dalthorp et al. (2017) <doi:10.3133/ds1055>, and Dalthorp and Huso (2015) <doi:10.3133/ofr20151227>.

Serves as a platform for published fluorometric enzyme assay protocols. ezmmek calibrates, calculates, and plots enzyme activities as they relate to the transformation of synthetic substrates. At present, ezmmek implements two common protocols found in the literature, and is modular to accommodate additional protocols. Here, these protocols are referred to as the In-Sample Calibration (Hoppe, 1983; <doi:10.3354/meps011299>) and In-Buffer Calibration (German et al., 2011; <doi:10.1016/j.soilbio.2011.03.017>). protocols. By containing multiple protocols, ezmmek aims to stimulate discussion about how to best optimize fluorometric enzyme assays. A standardized approach would make studies more comparable and reproducible.

Fast and very memory-efficient calculation of isotope patterns, subsequent convolution to theoretical envelopes (profiles) plus valley detection and centroidization or intensoid calculation. Batch processing, resolution interpolation, wrapper, adduct calculations and molecular formula parsing. Loos, M., Gerber, C., Corona, F., Hollender, J., Singer, H. (2015) <doi:10.1021/acs.analchem.5b00941>.

Query NCBI Entrez and retrieve PubMed records in XML or text format. Process PubMed records by extracting and aggregating data from selected fields. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API.

This is a (somewhat bizarre) collection of functions written to do various sorts of statistical election audits. There are also functions to generate simulated voting data, including methods to simulation different types of voting errors which allow for simulations for checking the characteristics of these methods.

This package contains a set of clustering methods and evaluation metrics to select the best number of the clusters based on clustering stability. Two references describe the methodology: Fahimeh Nezhadmoghadam, and Jose Tamez-Pena (2021)<doi:10.1016/j.compbiomed.2021.104753>, and Fahimeh Nezhadmoghadam, et al.(2021)<doi:10.2174/1567205018666210831145825>.

Analyses districted electoral systems of any magnitude by computing district-party conversion ratios and seats-to-votes deviations, decomposing the sources of deviation. Traditional indexes are also computed. References: Kedar, O., Harsgor, L. and Sheinerman, R.A. (2016). <doi:10.1111/ajps.12225>. Penades, A and Pavia, J.M. (2025) The decomposition of seats-to-votes distortion in elections: mean, variance, malapportionment and participation''. Acknowledgements: The authors wish to thank Consellerà a de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACO/2023/031) for supporting this research.

The EUNIS habitat classification is a comprehensive pan-European system for habitat identification <https://www.eea.europa.eu/data-and-maps/data/eunis-habitat-classification-1>. This is an R data package providing the EUNIS classification system. The classification is hierarchical and covers all types of habitats from natural to artificial, from terrestrial to freshwater and marine. The habitat types are identified by specific codes, names and descriptions and come with schema crosswalks to other habitat typologies.

Small toolbox for data analyses in environmental chemistry and ecotoxicology. Provides, for example, calibration() to calculate calibration curves and corresponding limits of detection (LODs) and limits of quantification (LOQs) according to German DIN 32645 (2008). texture() makes it easy to estimate soil particle size distributions from hydrometer measurements (ASTM D422-63, 2007).

Total Time on Test plot and routines for parameter estimation of any lifetime distribution implemented in R via maximum likelihood (ML) given a data set. It is implemented thinking on parametric survival analysis, but it feasible to use in parameter estimation of probability density or mass functions in any field. The main routines maxlogL and maxlogLreg are wrapper functions specifically developed for ML estimation. There are included optimization procedures such as nlminb and optim from base package, and DEoptim Mullen (2011) <doi:10.18637/jss.v040.i06>. Standard errors are estimated with numDeriv Gilbert (2011) <https://CRAN.R-project.org/package=numDeriv> or the option Hessian = TRUE of optim function.

Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon partykit to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) <arXiv:2207.04430>.

This package provides a robust and efficient solution for working with Ethiopian dates. It can seamlessly convert to and from Gregorian dates. It is designed to be compatible with the tidyverse data workflow, including plotting with ggplot2'. It ensures lightning-fast computations by integrating high-performance C++ code through Rcpp package.

This package performs likelihood-based extreme value inferences with adjustment for the presence of missing values based on Simpson and Northrop (2026) <doi:10.1002/env.70075>. A Generalised Extreme Value distribution is fitted to block maxima using maximum likelihood estimation, with the location and scale parameters reflecting the numbers of non-missing raw values in each block. A Bayesian version is also provided. For the purposes of comparison, there are options to make no adjustment for missing values or to discard any block maximum for which greater than a percentage of the underlying raw values are missing. Example datasets containing missing values are provided.

Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) model. Empirical likelihood tests within a Cox model, for parameters defined via both baseline hazard function and regression parameters.

This package provides a lightweight implementation of functions and methods for fast and fully automatic time series modeling and forecasting using Echo State Networks (ESNs).

Conduct numerous exploratory analyses in an instant with a point-and-click interface. With one simple command, this tool launches a Shiny App on the local machine. Drag and drop variables in a data set to categorize them as possible independent, dependent, moderating, or mediating variables. Then run dozens (or hundreds) of analyses instantly to uncover any statistically significant relationships among variables. Any relationship thus uncovered should be tested in follow-up studies. This tool is designed only to facilitate exploratory analyses and should NEVER be used for p-hacking. Many of the functions used in this package are previous versions of functions in the R Packages kim and ezr'. Selected References: Chang et al. (2021) <https://CRAN.R-project.org/package=shiny>. Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. Kim (2023) <https://jinkim.science/docs/kim.pdf>. Kim (2021) <doi:10.5281/zenodo.4619237>. Kim (2020) <https://CRAN.R-project.org/package=ezr>. Simmons et al. (2011) <doi:10.1177/0956797611417632> Tingley et al. (2019) <https://CRAN.R-project.org/package=mediation>. Wickham et al. (2020) <https://CRAN.R-project.org/package=ggplot2>.

This package provides methods for working with dose-finding clinical trials. We provide implementations of many dose-finding clinical trial designs, including the continual reassessment method (CRM) by O'Quigley et al. (1990) <doi:10.2307/2531628>, the toxicity probability interval (TPI) design by Ji et al. (2007) <doi:10.1177/1740774507079442>, the modified TPI (mTPI) design by Ji et al. (2010) <doi:10.1177/1740774510382799>, the Bayesian optimal interval design (BOIN) by Liu & Yuan (2015) <doi:10.1111/rssc.12089>, EffTox by Thall & Cook (2004) <doi:10.1111/j.0006-341X.2004.00218.x>; the design of Wages & Tait (2015) <doi:10.1080/10543406.2014.920873>, and the 3+3 described by Korn et al. (1994) <doi:10.1002/sim.4780131802>. All designs are implemented with a common interface. We also offer optional additional classes to tailor the behaviour of all designs, including avoiding skipping doses, stopping after n patients have been treated at the recommended dose, stopping when a toxicity condition is met, or demanding that n patients are treated before stopping is allowed. By daisy-chaining together these classes using the pipe operator from magrittr', it is simple to tailor the behaviour of a dose-finding design so it behaves how the trialist wants. Having provided a flexible interface for specifying designs, we then provide functions to run simulations and calculate dose-paths for future cohorts of patients.

Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). <doi:10.1093/jrsssb/qkae108>.

We quantitatively evaluated the assertion that says if one suit is found to be evenly distributed among the 4 players, the rest of the suits are more likely to be evenly distributed. Our mathematical analyses show that, if one suit is found to be evenly distributed, then a second suit has a slightly elevated probability (ranging between 10% to 15%) of being evenly distributed. If two suits are found to be evenly distributed, then a third suit has a substantially elevated probability (ranging between 30% to 50%) of being evenly distributed.This package refers to methods and authentic data from Ely Culbertson <https://www.bridgebum.com/law_of_symmetry.php>, Gregory Stoll <https://gregstoll.com/~gregstoll/bridge/math.html>, and details of performing the probability calculations from Jeremy L. Martin <https://jlmartin.ku.edu/~jlmartin/bridge/basics.pdf>, Emile Borel and Andre Cheron (1954) "The Mathematical Theory of Bridge",Antonio Vivaldi and Gianni Barracho (2001, ISBN:0 7134 8663 5) "Probabilities and Alternatives in Bridge", Ken Monzingo (2005) "Hand and Suit Patterns" <http://web2.acbl.org/documentlibrary/teachers/celebritylessons/handpatternsrevised.pdf>Ken Monzingo (2005) "Hand and Suit Patterns" <http://web2.acbl.org/documentlibrary/teachers/celebritylessons/handpatternsrevised.pdf>.

This package provides functions for the computation of functional elastic shape means over sets of open planar curves. The package is particularly suitable for settings where these curves are only sparsely and irregularly observed. It uses a novel approach for elastic shape mean estimation, where planar curves are treated as complex functions and a full Procrustes mean is estimated from the corresponding smoothed Hermitian covariance surface. This is combined with the methods for elastic mean estimation proposed in Steyer, Stöcker, Greven (2022) <doi:10.1111/biom.13706>. See Stöcker et. al. (2022) <arXiv:2203.10522> for details.

Reads European Data Format files EDF and EDF+, see <http://www.edfplus.info>, BioSemi Data Format files BDF, see <http://www.biosemi.com/faq/file_format.htm>, and BDF+ files, see <http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html>. The files are read in two steps: first the header is read and then the signals (using the header object as a parameter).