Estimates power by simulation for multivariate abundance data to be used for sample size estimates. Multivariate equivalence testing by simulation from a Gaussian copula model. The package also provides functions for parameterising multivariate effect sizes and simulating multivariate abundance data jointly. The discrete Gaussian copula approach is described in Popovic et al. (2018) <doi:10.1016/j.jmva.2017.12.002>.

Three functional modules, including genetic features, differential expression analysis and non-additive expression analysis were integrated into the package. And the package is suitable for RNA-seq and small RNA sequencing data. Besides, two methods of non-additive expression analysis were provided. One is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the gene in hybrid offspring with the expression level in parents. For non-additive expression analysis of RNA-seq data, it is only applicable to hybrid offspring (including two sub-genomes) species for the time being.

The Explainable Ensemble Trees e2tree approach has been proposed by Aria et al. (2024) <doi:10.1007/s00180-022-01312-6>. It aims to explain and interpret decision tree ensemble models using a single tree-like structure. e2tree is a new way of explaining an ensemble tree trained through randomForest or xgboost packages.

Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses. Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching.

This package provides functions supporting the reading and parsing of internal e-book content from EPUB files. The epubr package provides functions supporting the reading and parsing of internal e-book content from EPUB files. E-book metadata and text content are parsed separately and joined together in a tidy, nested tibble data frame. E-book formatting is not completely standardized across all literature. It can be challenging to curate parsed e-book content across an arbitrary collection of e-books perfectly and in completely general form, to yield a singular, consistently formatted output. Many EPUB files do not even contain all the same pieces of information in their respective metadata. EPUB file parsing functionality in this package is intended for relatively general application to arbitrary EPUB e-books. However, poorly formatted e-books or e-books with highly uncommon formatting may not work with this package. There may even be cases where an EPUB file has DRM or some other property that makes it impossible to read with epubr'. Text is read as is for the most part. The only nominal changes are minor substitutions, for example curly quotes changed to straight quotes. Substantive changes are expected to be performed subsequently by the user as part of their text analysis. Additional text cleaning can be performed at the user's discretion, such as with functions from packages like tm or qdap'.

Integrates methods for epidemiological analysis, modeling, and visualization, including functions for summary statistics, SIR (Susceptible-Infectious-Recovered) modeling, DALY (Disability-Adjusted Life Years) estimation, age standardization, diagnostic test evaluation, NLP (Natural Language Processing) keyword extraction, clinical trial power analysis, survival analysis, SNP (Single Nucleotide Polymorphism) association, and machine learning methods such as logistic regression, k-means clustering, Random Forest, and Support Vector Machine (SVM). Includes datasets for prevalence estimation, SIR modeling, genomic analysis, clinical trials, DALY, diagnostic tests, and survival analysis. Methods are based on Gelman et al. (2013) <doi:10.1201/b16018> and Wickham et al. (2019, ISBN:9781492052040>.

EPE's (Empresa de Pesquisa Energética) 4MD (Modelo de Mercado da Micro e Minigeração Distribuà da - Micro and Mini Distributed Generation Market Model) model to forecast the adoption of Distributed Generation. Given the user's assumptions, it is possible to estimate how many consumer units will have distributed generation in Brazil over the next 10 years, for example. In addition, it is possible to estimate the installed capacity, the amount of investments that will be made in the country and the monthly energy contribution of this type of generation. <https://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-689/topico-639/NT_Metodologia_4MD_PDE_2032_VF.pdf>.

Routines for Bayesian estimation and analysis of dynamic quantile linear models utilizing the extended asymmetric Laplace error distribution, also known as extended dynamic quantile linear models (exDQLM) described in Barata et al (2020) <doi:10.1214/21-AOAS1497>.

This package provides functions to create simulated time series of environmental exposures (e.g., temperature, air pollution) and health outcomes for use in power analysis and simulation studies in environmental epidemiology. This package also provides functions to evaluate the results of simulation studies based on these simulated time series. This work was supported by a grant from the National Institute of Environmental Health Sciences (R00ES022631) and a fellowship from the Colorado State University Programs for Research and Scholarly Excellence.

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.

This package provides tools for importing, analyzing and visualizing ego-centered network data. Supports several data formats, including the export formats of EgoNet', EgoWeb 2.0 and openeddi'. An interactive (shiny) app for the intuitive visualization of ego-centered networks is provided. Also included are procedures for creating and visualizing Clustered Graphs (Lerner 2008 <DOI:10.1109/PACIFICVIS.2008.4475458>).

Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>.

This package contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). Appropriate print and summary statements are also written to facilitate interpretation wherever possible. Source code is commented throughout to facilitate modification. The target audience includes advanced undergraduate and graduate students in epidemiology or biostatistics courses, and clinical researchers.

Implementation of method for estimating excess mortality and other health related outcomes from weekly or daily count data described in Acosta and Irizarry (2021) "A Flexible Statistical Framework for Estimating Excess Mortality".

This package provides a tool which allows users to create and evaluate ensembles of species distribution model (SDM) predictions. Functionality is offered through R functions or a GUI (R Shiny app). This tool can assist users in identifying spatial uncertainties and making informed conservation and management decisions. The package is further described in Woodman et al (2019) <doi:10.1111/2041-210X.13283>.

This package provides tools to analyse human and mosquito behavioral interactions and to compute exposure to mosquito bites estimates. Using behavioral data for human individuals and biting patterns for mosquitoes, you will be able to compute hourly exposure for bed net users and non-users, and summarize (e.g. proportion indoors and outdoors, proportion per time periods, and proportion prevented by bed nets) or visualize these dynamics across a 24-hour cycle.

Evolutionary relatedness dependent diversification simulation powered by the Rcpp back-end SimTable'.

This package implements a Markov Chain Monte Carlo algorithm to approximate exact conditional inference for logistic regression models. Exact conditional inference is based on the distribution of the sufficient statistics for the parameters of interest given the sufficient statistics for the remaining nuisance parameters. Using model formula notation, users specify a logistic model and model terms of interest for exact inference. See Zamar et al. (2007) <doi:10.18637/jss.v021.i03> for more details.

Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the ergm package, ergmito circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms.

Interactive data exploration with one line of code, automated reporting or use an easy to remember set of tidy functions for low code exploratory data analysis.

This package contains all the datasets that were used in Social Science Experiments: A Hands-On Introduction and in its R Companion. Relevant materials can be found at <https://osf.io/b78je>.

This package provides a set of extensions for the ergm package to fit weighted networks whose edge weights are counts. See Krivitsky (2012) <doi:10.1214/12-EJS696> and Krivitsky, Hunter, Morris, and Klumb (2023) <doi:10.18637/jss.v105.i06>.

Fast and easy computation of Euclidean Minimum Spanning Trees (EMST) from data, relying on the R API for mlpack - the C++ Machine Learning Library (Curtin et. al., 2013). emstreeR uses the Dual-Tree Boruvka (March, Ram, Gray, 2010, <doi:10.1145/1835804.1835882>), which is theoretically and empirically the fastest algorithm for computing an EMST. This package also provides functions and an S3 method for readily visualizing Minimum Spanning Trees (MST) using either the style of the base', scatterplot3d', or ggplot2 libraries; and functions to export the MST output to shapefiles.

Unofficial API wrapper for Euroleague and Eurocup basketball API (<https://www.euroleaguebasketball.net/en/euroleague/>), it allows to retrieve real-time and historical standard and advanced statistics about competitions, teams, players and games.