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.

Padroniza endereços brasileiros a partir de diferentes critérios. Os métodos de padronização incluem apenas manipulações básicas de strings, não oferecendo suporte a correspondências probabilà sticas entre strings. (Standardizes brazilian addresses using different criteria. Standardization methods include only basic string manipulation, not supporting probabilistic matches between strings.).

This package provides a data package containing a database of epidemiological parameters. It stores the data for the epiparameter R package. Epidemiological parameter estimates are extracted from the literature.

Fast procedures for small set of commonly-used, design-appropriate estimators with robust standard errors and confidence intervals. Includes estimators for linear regression, instrumental variables regression, difference-in-means, Horvitz-Thompson estimation, and regression improving precision of experimental estimates by interacting treatment with centered pre-treatment covariates introduced by Lin (2013) <doi:10.1214/12-AOAS583>.

For multiscale analysis, this package carries out ensemble patch transform, its visualization and multiscale decomposition. The detailed procedure is described in Kim et al. (2020), and Oh and Kim (2020). D. Kim, G. Choi, H.-S. Oh, Ensemble patch transformation: a flexible framework for decomposition and filtering of signal, EURASIP Journal on Advances in Signal Processing 30 (2020) 1-27 <doi:10.1186/s13634-020-00690-7>. H.-S. Oh, D. Kim, Image decomposition by bidimensional ensemble patch transform, Pattern Recognition Letters 135 (2020) 173-179 <doi:10.1016/j.patrec.2020.03.029>.

This package contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.

Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community ecological data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.

Interactive labelling of scatter plots, volcano plots and Manhattan plots using a shiny and plotly interface. Users can hover over points to see where specific points are located and click points on/off to easily label them. Labels can be dragged around the plot to place them optimally. Plots can be exported directly to PDF for publication. For plots with large numbers of points, points can optionally be rasterized as a bitmap, while all other elements (axes, text, labels & lines) are preserved as vector objects. This can dramatically reduce file size for plots with millions of points such as Manhattan plots, and is ideal for publication.

Capture code evaluations and script executions by expressions, outputs, and condition calls for logging.

Package EDISON (Estimation of Directed Interactions from Sequences Of Non-homogeneous gene expression) runs an MCMC simulation to reconstruct networks from time series data, using a non-homogeneous, time-varying dynamic Bayesian network. Networks segments and changepoints are inferred concurrently, and information sharing priors provide a reduction of the inference uncertainty.

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.

Estimates item and person parameters for the Continuous Response Model (CRM; Samejima, 1973, <doi:10.1007/BF02291114>), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.

This package provides instructional datasets and simple wrapper functions for selected analyses used in Essentials of Statistics for the Behavioral Sciences (Gravetter et al., 2026). The package is intended to support textbook examples by distributing data in a form that is easy for students and instructors to access within R. Current functionality includes packaged datasets and convenience wrappers for functions from ez', pwr', and WebPower for analysis of variance and statistical power calculations.

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.

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.

Obtain Bayesian posterior distributions of dominance hierarchy steepness (Neumann and Fischer (2023) <doi:10.1111/2041-210X.14021>). Steepness estimation is based on Bayesian implementations of either Elo-rating or David's scores.

Support ecological analyses such as ordination and clustering. Contains consistent and easy wrapper functions of stat', vegan', and labdsv packages, and visualisation functions of ordination and clustering.

Four ensemble-based methods (SMOTEBoost, RUSBoost, UnderBagging, and SMOTEBagging) for class imbalance problem are implemented for binary classification. Such methods adopt ensemble methods and data re-sampling techniques to improve model performance in presence of class imbalance problem. One special feature offers the possibility to choose multiple supervised learning algorithms to build weak learners within ensemble models. References: Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, and Kevin W. Bowyer (2003) <doi:10.1007/978-3-540-39804-2_12>, Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Amri Napolitano (2010) <doi:10.1109/TSMCA.2009.2029559>, R. Barandela, J. S. Sanchez, R. M. Valdovinos (2003) <doi:10.1007/s10044-003-0192-z>, Shuo Wang and Xin Yao (2009) <doi:10.1109/CIDM.2009.4938667>, Yoav Freund and Robert E. Schapire (1997) <doi:10.1006/jcss.1997.1504>.

For multiscale analysis, this package carries out empirical mode decomposition and Hilbert spectral analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).

This package implements the hybrid framework for event prediction described in Fang & Zheng (2011, <doi:10.1016/j.cct.2011.05.013>). To estimate the survival function the event prediction is based on, a piecewise exponential hazard function is fit to the time-to-event data to infer the potential change points. Prior to the last identified change point, the survival function is estimated using Kaplan-Meier, and the tail after the change point is fit using piecewise exponential.

The Australian Regulatory Guidelines for Prescription Medicines (ARGPM), guidance on "Stability testing for prescription medicines", recommends to predict the shelf life of chemically derived medicines from stability data by taking the worst case situation at batch release into account. Consequently, if a change over time is observed, a release limit needs to be specified. Finding a release limit and the associated shelf life is supported, as well as the standard approach that is recommended by guidance Q1E "Evaluation of stability data" from the International Council for Harmonisation (ICH).

This package provides simple functions to create constraints for small test assembly problems (e.g. van der Linden (2005, ISBN: 978-0-387-29054-6)) using sparse matrices. Currently, GLPK', lpSolve', Symphony', and Gurobi are supported as solvers. The gurobi package is not available from any mainstream repository; see <https://www.gurobi.com/downloads/>.

Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) <doi:10.1007/s11336-017-9579-4>.

Method and tool for generating time series forecasts using an ensemble wavelet-based auto-regressive neural network architecture. This method provides additional support of exogenous variables and also generates confidence interval. This package provides EWNet model for time series forecasting based on the algorithm by Panja, et al. (2022) and Panja, et al. (2023) <arXiv:2206.10696> <doi:10.1016/j.chaos.2023.113124>.