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.

Prints out information about the R working environment (system, R version,loaded and attached packages and versions) from a single function "env_doc()". Optionally adds information on git repository, tags, commits and remotes (if available).

Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition (Chapman&Hall/CRC).

Fast implementations of functional enrichment analysis methods using C++ via Rcpp'. Currently provides Over-Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). The multilevel GSEA algorithm is derived from the fgsea package. Methods are described in Subramanian et al. (2005) <doi:10.1073/pnas.0506580102> and Korotkevich et al. (2021) <doi:10.1101/060012>.

Create encrypted html files that are fully self contained and do not require any additional software. Using the package you can encrypt arbitrary html files and also directly create encrypted rmarkdown html reports.

Collection of convenience functions to make working with administrative records easier and more consistent. Includes functions to clean strings, and identify cut points. Also includes three example data sets of administrative education records for learning how to process records with errors.

This package provides tools to fit Mixture Cure Rate models via the Expectation-Maximization (EM) algorithm, allowing for flexible link functions in the cure component and various survival distributions in the latency part. The package supports user-specified link functions, includes methods for parameter estimation and model diagnostics, and provides residual analysis tailored for cure models. The classical theory methods used are described in Berkson, J. and Gage, R. P. (1952) <doi:10.2307/2281318>, Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) <https://www.jstor.org/stable/2984875>, Bazán, J., Torres-Avilés, F., Suzuki, A. and Louzada, F. (2017)<doi:10.1002/asmb.2215>.

This package provides tools for modelling electric vehicle charging sessions into generic groups with similar connection patterns called "user profiles", using Gaussian Mixture Models clustering. The clustering and profiling methodology is described in Cañigueral and Meléndez (2021, ISBN:0142-0615) <doi:10.1016/j.ijepes.2021.107195>.

Implementation of a modular framework for ecosystem risk assessments, combining existing risk assessment approaches tailored to semi-quantitative and quantitative analyses.

Produce maximum likelihood estimates of common accuracy statistics for multiple measurement methods when a gold standard is not available. An R implementation of the expectation maximization algorithms described in Zhou et al. (2011) <doi:10.1002/9780470906514> with additional functions for creating simulated data and visualizing results. Supports binary, ordinal, and continuous measurement methods.

We implement (or re-implements in R) a variety of statistical tools. They are focused on non-parametric two-sample (or k-sample) distribution comparisons in the univariate or multivariate case. See the vignette for more info.

Parametric proportional hazards fitting with left truncation and right censoring for common families of distributions, piecewise constant hazards, and discrete models. Parametric accelerated failure time models for left truncated and right censored data. Proportional hazards models for tabular and register data. Sampling of risk sets in Cox regression, selections in the Lexis diagram, bootstrapping. Broström (2022) <doi:10.1201/9780429503764>.

This package provides a flexible framework for Agent-Based Models (ABM), the epiworldR package provides methods for prototyping disease outbreaks and transmission models using a C++ backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents features, providing great complexity for the model dynamics. Furthermore, epiworldR is ideal for simulation studies featuring large populations.

Statistical tools for environmental and ecological surveys. Simulation-based power and precision analysis; detection probabilities from different survey designs; visual fast count estimation.

Evolutionary game theory applies game theory to evolving populations in biology, see e.g. one of the books by Weibull (1994, ISBN:978-0262731218) or by Sandholm (2010, ISBN:978-0262195874) for more details. A comprehensive set of tools to illustrate the core concepts of evolutionary game theory, such as evolutionary stability or various evolutionary dynamics, for teaching and academic research is provided.

Replication methods to compute some basic statistic operations (means, standard deviations, frequency tables, percentiles, mean comparisons using weighted effect coding, generalized linear models, and linear multilevel models) in complex survey designs comprising multiple imputed or nested imputed variables and/or a clustered sampling structure which both deserve special procedures at least in estimating standard errors. See the package documentation for a more detailed description along with references.

This package creates ensemble taxonomic assignments of amplicon sequencing data in R using outputs of multiple taxonomic assignment algorithms and/or reference databases. Includes flexible algorithms for mapping taxonomic nomenclatures onto one another and for computing ensemble taxonomic assignments.

Makes difficult operations easy. Includes these types of functions: shorthand, type conversion, data wrangling, and work flow. Also includes some helpful data objects: NA strings, U.S. state list, color blind charting colors. Built and shared by Oliver Wyman Actuarial Consulting. Accepting proposed contributions through GitHub.

This package provides easy access to ERVISS (European Respiratory Virus Surveillance Summary) data from the EU-ECDC <https://github.com/EU-ECDC/Respiratory_viruses_weekly_data>. Enables retrieval, filtering, and optional visualization of data across European countries. Data is fetched directly from the EU-ECDC Respiratory Viruses Weekly Data repository, with support for both latest data and historical snapshots for reproducible analyses.

Calculates the empirical likelihood ratio and p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with possible censored data.

This package provides a wrapper of different methods from Linear Algebra for the equations introduced in The Atlas of Economic Complexity and related literature. This package provides standard matrix and graph output that can be used seamlessly with other packages. See <doi:10.21105/joss.01866> for a summary of these methods and its evolution in literature.

Evaluates the empirical characteristic function of univariate and multivariate samples. This package uses RcppArmadillo for fast evaluation. It is also possible to export the code to be used in other packages at C++ level.

An implementation of Bayesian hierarchical models for faecal egg count data to assess anthelmintic efficacy. Bayesian inference is done via MCMC sampling using Stan <https://mc-stan.org/>.

This package provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.