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

This package creates simple or stacked epidemic curves for hourly, daily, weekly or monthly outcome data.

Analysis of temporal changes (i.e. dynamics) of ecological entities, defined as trajectories on a chosen multivariate space, by providing a set of trajectory metrics and visual representations [De Caceres et al. (2019) <doi:10.1002/ecm.1350>; and Sturbois et al. (2021) <doi:10.1016/j.ecolmodel.2020.109400>]. Includes functions to estimate metrics for individual trajectories (length, directionality, angles, ...) as well as metrics to relate pairs of trajectories (dissimilarity and convergence). Functions are also provided to estimate the ecological quality of ecosystem with respect to reference conditions [Sturbois et al. (2023) <doi:10.1002/ecs2.4726>].

An implementation of a variety of escalation with overdose control designs introduced by Babb, Rogatko and Zacks (1998) <doi:10.1002/(SICI)1097-0258(19980530)17:10%3C1103::AID-SIM793%3E3.0.CO;2-9>. It calculates the next dose as a clinical trial proceeds and performs simulations to obtain operating characteristics.

Simulating multi-arm cluster-randomized, multi-site, and simple randomized trials. Includes functions for conducting multilevel analyses using both Bayesian and Frequentist methods. Supports futility and superiority analyses through Bayesian approaches, along with visualization tools to aid interpretation and presentation of results.

Set of wrappers for the ncdf4 package to simplify and extend its reading/writing capabilities into/from multidimensional R arrays.

Biotracers and stomach content analyses are combined in a Bayesian hierarchical model to estimate a probabilistic topology matrix (all trophic link probabilities) and a diet matrix (all diet proportions). The package relies on the JAGS software and the jagsUI package to run a Markov chain Monte Carlo approximation of the different variables.

Fitting and testing multi-attribute probabilistic choice models, especially the Bradley-Terry-Luce (BTL) model (Bradley & Terry, 1952 <doi:10.1093/biomet/39.3-4.324>; Luce, 1959), elimination-by-aspects (EBA) models (Tversky, 1972 <doi:10.1037/h0032955>), and preference tree (Pretree) models (Tversky & Sattath, 1979 <doi:10.1037/0033-295X.86.6.542>).

Conducts sensitivity analyses for unmeasured confounding, selection bias, and measurement error (individually or in combination; VanderWeele & Ding (2017) <doi:10.7326/M16-2607>; Smith & VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>; VanderWeele & Li (2019) <doi:10.1093/aje/kwz133>; Smith, Mathur, & VanderWeele (2021) <doi:10.1097/EDE.0000000000001380>). Also conducts sensitivity analyses for unmeasured confounding in meta-analyses (Mathur & VanderWeele (2020a) <doi:10.1080/01621459.2018.1529598>; Mathur & VanderWeele (2020b) <doi:10.1097/EDE.0000000000001180>) and for additive measures of effect modification (Mathur et al., <doi:10.1093/ije/dyac073>).

The core of this package is a function eDT() which enhances DT::datatable() such that it can be used to interactively modify data in shiny'. By the use of generic dplyr methods it supports many types of data storage, with relational databases ('dbplyr') being the main use case.

Randomized multiple-select and single-select question generation for the MyLearn teaching and learning platform. Question templates in the form of the R/exams package (see <http://www.r-exams.org/>) are transformed into XML format required by MyLearn'.

This package provides easy access to tidy education finance data using Bellwether's methodology to combine NCES F-33 Survey, Census Bureau Small Area Income Poverty Estimates (SAIPE), and community data from the ACS 5-Year Estimates. The package simplifies downloading, caching, and filtering education finance data by year and state, enabling researchers and analysts to explore K-12 education funding patterns, revenue sources, expenditure categories, and demographic factors across U.S. school districts.

Data published by the United States Federal Energy Regulatory Commission including electric company financial data, natural gas company financial data, hydropower plant data, liquified natural gas plant data, oil company financial data natural gas company financial data, and natural gas storage field data.

This package implements a segmentation algorithm for multiple change-point detection in univariate time series using the Ensemble Binary Segmentation of Korkas (2022) <Journal of the Korean Statistical Society, 51(1), pp.65-86.>.

Bayesian (and some likelihoodist) functions as alternatives to hypothesis-testing functions in R base using a user interface patterned after those of R's hypothesis testing functions. See McElreath (2016, ISBN: 978-1-4822-5344-3), Gelman and Hill (2007, ISBN: 0-521-68689-X) (new edition in preparation) and Albert (2009, ISBN: 978-0-387-71384-7) for good introductions to Bayesian analysis and Pawitan (2002, ISBN: 0-19-850765-8) for the Likelihood approach. The functions in the package also make extensive use of graphical displays for data exploration and model comparison.

This package provides a simple interface to search and retrieve scientific articles from the SciELO (Scientific Electronic Library Online) database <https://scielo.org>. It allows querying, filtering, and visualizing results in an interactive table.

Computes maximum mean discrepancy two-sample test for univariate data using the Laplacian kernel, as described in Bodenham and Kawahara (2023) <doi:10.1007/s11222-023-10271-x>. The p-value is computed using permutations. Also includes implementation for computing the robust median difference statistic Q_n from Croux and Rousseeuw (1992) <doi:10.1007/978-3-662-26811-7_58> based on Johnson and Mizoguchi (1978) <doi:10.1137/0207013>.

Extreme value theory, nonparametric kernel estimation, tail conditional probabilities, extreme conditional quantile, adaptive estimation, quantile regression, survival probabilities.

This package provides functions for estimating plant pathogen parameters from access period (AP) experiments. Separate functions are implemented for semi-persistently transmitted (SPT) and persistently transmitted (PT) pathogens. The common AP experiment exposes insect cohorts to infected source plants, healthy test plants, and intermediate plants (for PT pathogens). The package allows estimation of acquisition and inoculation rates during feeding, recovery rates, and latent progression rates (for PT pathogens). Additional functions support inference of epidemic risk from pathogen and local parameters, and also simulate AP experiment data. The functions implement probability models for epidemiological analysis, as derived in Donnelly et al. (2025), <doi:10.32942/X29K9P>. These models were originally implemented in the EpiPv GitHub package.

This package provides functions for analysis of rate changes in sequential events.

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.

This package provides functions for signal detection and identification designed for Event-Related Potentials (ERP) data in a linear model framework. The functional F-test proposed in Causeur, Sheu, Perthame, Rufini (2018, submitted) for analysis of variance issues in ERP designs is implemented for signal detection (tests for mean difference among groups of curves in One-way ANOVA designs for example). Once an experimental effect is declared significant, identification of significant intervals is achieved by the multiple testing procedures reviewed and compared in Sheu, Perthame, Lee and Causeur (2016, <DOI:10.1214/15-AOAS888>). Some of the methods gathered in the package are the classical FDR- and FWER-controlling procedures, also available using function p.adjust. The package also implements the Guthrie-Buchwald procedure (Guthrie and Buchwald, 1991 <DOI:10.1111/j.1469-8986.1991.tb00417.x>), which accounts for the auto-correlation among t-tests to control erroneous detection of short intervals. The Adaptive Factor-Adjustment method is an extension of the method described in Causeur, Chu, Hsieh and Sheu (2012, <DOI:10.3758/s13428-012-0230-0>). It assumes a factor model for the correlation among tests and combines adaptively the estimation of the signal and the updating of the dependence modelling (see Sheu et al., 2016, <DOI:10.1214/15-AOAS888> for further details).

Errors in data can be located and removed using validation rules from package validate'. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, chapter 7.

This package provides functions of five estimation method for ED50 (50 percent effective dose) are provided, and they are respectively Dixon-Mood method (1948) <doi:10.2307/2280071>, Choi's original turning point method (1990) <doi:10.2307/2531453> and it's modified version given by us, as well as logistic regression and isotonic regression. Besides, the package also supports comparison between two estimation results.