Mathematical models of infectious diseases in humans and animals. Both, deterministic and stochastic models can be simulated and plotted.

Experiences studies are an integral component of the actuarial control cycle. Regardless of the decrement or policyholder behavior of interest, the analyses conducted is often the same. Ultimately, this package aims to reduce time spent writing the same code used for different experience studies, therefore increasing the time for to uncover new insights inherit within the relevant experience.

The main function, ProtectTable(), performs table suppression according to a frequency rule with a data set as the only required input. Within this function, protectTable(), protect_linked_tables() or runArgusBatchFile() in package sdcTable is called. Lists of level-hierarchy (parameter dimList') and other required input to these functions are created automatically. The suppression method Gauss (default) is implemented independently of sdcTable'. The function, PTgui(), starts a graphical user interface based on the shiny package.

Offers a flexible and user-friendly interface for visualizing conditional effects from a broad range of regression models, including mixed-effects and generalized additive (mixed) models. Compatible model types include lm(), rlm(), glm(), glm.nb(), betareg(), and gam() (from mgcv'); nonlinear models via nls(); generalized least squares via gls(); and survival models via coxph() (from survival'). Mixed-effects models with random intercepts and/or slopes can be fitted using lmer(), glmer(), glmer.nb(), glmmTMB(), or gam() (from mgcv', via smooth terms). Plots are rendered using base R graphics with extensive customization options. Approximate confidence intervals for nls() and betareg() models are computed using the delta method. Robust standard errors for rlm() are computed using the sandwich estimator (Zeileis 2004) <doi:10.18637/jss.v011.i10>. For beta regression using betareg', see Cribari-Neto and Zeileis (2010) <doi:10.18637/jss.v034.i02>. For mixed-effects models with lme4', see Bates et al. (2015) <doi:10.18637/jss.v067.i01>. For models using glmmTMB', see Brooks et al. (2017) <doi:10.32614/RJ-2017-066>. Methods for generalized additive models using mgcv follow Wood (2017) <doi:10.1201/9781315370279>.

This package performs parallel analysis (Timmerman & Lorenzo-Seva, 2011 <doi:10.1037/a0023353>) and hull method (Lorenzo-Seva, Timmerman, & Kiers, 2011 <doi:10.1080/00273171.2011.564527>) for assessing the dimensionality of a set of variables using minimum rank factor analysis (see ten Berge & Kiers, 1991 <doi:10.1007/BF02294464> for more information). The package also includes the option to compute minimum rank factor analysis by itself, as well as the greater lower bound calculation.

Allows calculating global scores for characteristics of visual stimuli as assessed by human raters. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses preference for one stimulus over the other (forced choice). The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest. Clark et al (2018) <doi:10.1371/journal.pone.0190393> provide details.

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).

This package contains several functions for equivalence testing and practical significance testing. First, the tsti() command provides an automatic computation of three-sided testing results for a given estimate, standard error, and region of practical equivalence. For details, see Goeman, Solari, & Stijnen (2010) <doi:10.1002/sim.4002> and Isager & Fitzgerald (2024) <doi:10.31234/osf.io/8y925>. Second, the lddtest() command performs logarithmic density discontinuity equivalence testing for regression discontinuity designs. For reference, see Fitzgerald (2025) <doi:10.31222/osf.io/2dgrp_v1>.

The R package proposes extreme value index estimators for heavy tailed models by mean of order p <DOI:10.1016/j.csda.2012.07.019>, peaks over random threshold <DOI:10.57805/revstat.v4i3.37> and a bias-reduced estimator <DOI:10.1080/00949655.2010.547196>. The package also computes moment, generalised Hill <DOI:10.2307/3318416> and mixed moment estimates for the extreme value index. High quantiles and value at risk estimators based on these estimators are implemented.

This package provides a graphical user interface for open source event detection.

Collection of ancillary functions and utilities for Partial Linear Single Index Models for Environmental mixture analyses, which currently provides functions for scalar outcomes. The outputs of these functions include the single index function, single index coefficients, partial linear coefficients, mixture overall effect, exposure main and interaction effects, and differences of quartile effects. In the future, we will add functions for binary, ordinal, Poisson, survival, and longitudinal outcomes, as well as models for time-dependent exposures. See Wang et al (2020) <doi:10.1186/s12940-020-00644-4> for an overview.

Constructs a shiny app function with interactive displays for summary and analysis of variance regression tables, and parallel coordinate plots of data and residuals.

Computes alpha and beta diversity metrics using concurrent C threads. Metrics include UniFrac', Faith's phylogenetic diversity, Bray-Curtis dissimilarity, Shannon diversity index, and many others. Also parses newick trees into phylo objects and rarefies feature tables.

This package provides a collection of tools for representing epidemiological contact data, composed of case line lists and contacts between cases. Also contains procedures for data handling, interactive graphics, and statistics.

Compute the empirical likelihood ratio, -2LogLikRatio (Wilks) statistics, based on current status data for the hypothesis about the parameters of mean or probability or weighted cumulative hazard.

Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data using the model described in Forcina et al. (2012), as extension of the model proposed in Brown and Payne (1986). Allows incorporation of covariates. References: Brown, P. and Payne, C. (1986). Aggregate data, ecological regression and voting transitions''. Journal of the American Statistical Association, 81, 453â 460. <DOI:10.1080/01621459.1986.10478290>. Forcina, A., Gnaldi, M. and Bracalente, B. (2012). A revised Brown and Payne model of voting behaviour applied to the 2009 elections in Italy''. Statistical Methods & Applications, 21, 109â 119. <DOI:10.1007/s10260-011-0184-x>.

Conduct one- and two-sample goodness-of-fit tests for univariate data. In the one-sample case, normal, uniform, exponential, Bernoulli, binomial, geometric, beta, Poisson, lognormal, Laplace, asymmetric Laplace, inverse Gaussian, half-normal, chi-squared, gamma, F, Weibull, Cauchy, and Pareto distributions are supported. egof.test() can also test goodness-of-fit to any distribution with a continuous distribution function. A subset of the available distributions can be tested for the composite goodness-of-fit hypothesis, that is, one can test for distribution fit with unknown parameters. P-values are calculated via parametric bootstrap.

The remit of the European Clinical Trials Data Base (EudraCT <https://eudract.ema.europa.eu/> ), or ClinicalTrials.gov <https://clinicaltrials.gov/>, is to provide open access to summaries of all registered clinical trial results; thus aiming to prevent non-reporting of negative results and provide open-access to results to inform future research. The amount of information required and the format of the results, however, imposes a large extra workload at the end of studies on clinical trial units. In particular, the adverse-event-reporting component requires entering: each unique combination of treatment group and safety event; for every such event above, a further 4 pieces of information (body system, number of occurrences, number of subjects, number exposed) for non-serious events, plus an extra three pieces of data for serious adverse events (numbers of causally related events, deaths, causally related deaths). This package prepares the required statistics needed by EudraCT and formats them into the precise requirements to directly upload an XML file into the web portal, with no further data entry by hand.

Runs ecological niche models over all combinations of user-defined settings (i.e., tuning), performs cross validation to evaluate models, and returns data tables to aid in selection of optimal model settings that balance goodness-of-fit and model complexity. Also has functions to partition data spatially (or not) for cross validation, to plot multiple visualizations of results, to run null models to estimate significance and effect sizes of performance metrics, and to calculate range overlap between model predictions, among others. The package was originally built for Maxent models (Phillips et al. 2006, Phillips et al. 2017), but the current version allows possible extensions for any modeling algorithm. The extensive vignette, which guides users through most package functionality but unfortunately has a file size too big for CRAN, can be found here on the package's Github Pages website: <https://jamiemkass.github.io/ENMeval/articles/ENMeval-2.0-vignette.html>.

Estimates RxC (R by C) vote transfer matrices (ecological contingency tables) from aggregate data by simultaneously minimizing Euclidean row-standardized unit-to-global distances. Acknowledgements: The authors wish to thank Generalitat Valenciana, Consellerà a de Educación, Cultura, Universidades y Empleo (grant CIAICO/2023/031) for supporting this research.

Allows to calculate the probabilities of occurrences of an event in a great number of repetitions of Bernoulli experiment, through the application of the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. Gives the possibility to show the results graphically and analytically, and to compare the results obtained by the application of the above theorems with those calculated by the direct application of the Binomial formula. Is basically useful for educational purposes.

Easy and rapid quantitative estimation of small terrestrial ectotherm temperature regulation effectiveness in R. ectotemp is built on classical formulas that evaluate temperature regulation by means of various indices, inaugurated by Hertz et al. (1993) <doi: 10.1086/285573>. Options for bootstrapping and permutation testing are included to test hypotheses about divergence between organisms, species or populations.

This package provides a set of user-friendly functions to aid in organizing, plotting and analyzing event-related potential (ERP) data. Provides an easy-to-learn method to explore ERP data. Should be useful to those without a background in computer programming, and to those who are new to ERPs (or new to the more advanced ERP software available). Emphasis has been placed on highly automated processes using functions with as few arguments as possible. Expects processed (cleaned) data.

Researchers often use the bootstrap to understand a sample drawn from a population with unknown distribution. The exact bootstrap method is a practical tool for exploring the distribution of small sample size data. For a sample of size n, the exact bootstrap method generates the entire space of n to the power of n resamples and calculates all realizations of the selected statistic. The exactamente package includes functions for implementing two bootstrap methods, the exact bootstrap and the regular bootstrap. The exact_bootstrap() function applies the exact bootstrap method following methodologies outlined in Kisielinska (2013) <doi:10.1007/s00180-012-0350-0>. The regular_bootstrap() function offers a more traditional bootstrap approach, where users can determine the number of resamples. The e_vs_r() function allows users to directly compare results from these bootstrap methods. To augment user experience, exactamente includes the function exactamente_app() which launches an interactive shiny web application. This application facilitates exploration and comparison of the bootstrap methods, providing options for modifying various parameters and visualizing results.