Constructing an epistemic model such that, for every player i and for every choice c(i) which is optimal, there is one type that expresses common belief in rationality.

Processing tools to create emissions for use in numerical air quality models. Emissions can be calculated both using emission factors and activity data (Schuch et al 2018) <doi:10.21105/joss.00662> or using pollutant inventories (Schuch et al., 2018) <doi:10.30564/jasr.v1i1.347>. Functions to process individual point emissions, line emissions and area emissions of pollutants are available as well as methods to incorporate alternative data for Spatial distribution of emissions such as satellite images (Gavidia-Calderon et. al, 2018) <doi:10.1016/j.atmosenv.2018.09.026> or openstreetmap data (Andrade et al, 2015) <doi:10.3389/fenvs.2015.00009>.

This package provides a collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.

Conduct numerous exploratory analyses in an instant with a point-and-click interface. With one simple command, this tool launches a Shiny App on the local machine. Drag and drop variables in a data set to categorize them as possible independent, dependent, moderating, or mediating variables. Then run dozens (or hundreds) of analyses instantly to uncover any statistically significant relationships among variables. Any relationship thus uncovered should be tested in follow-up studies. This tool is designed only to facilitate exploratory analyses and should NEVER be used for p-hacking. Many of the functions used in this package are previous versions of functions in the R Packages kim and ezr'. Selected References: Chang et al. (2021) <https://CRAN.R-project.org/package=shiny>. Dowle et al. (2021) <https://CRAN.R-project.org/package=data.table>. Kim (2023) <https://jinkim.science/docs/kim.pdf>. Kim (2021) <doi:10.5281/zenodo.4619237>. Kim (2020) <https://CRAN.R-project.org/package=ezr>. Simmons et al. (2011) <doi:10.1177/0956797611417632> Tingley et al. (2019) <https://CRAN.R-project.org/package=mediation>. Wickham et al. (2020) <https://CRAN.R-project.org/package=ggplot2>.

Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at <doi:10.48550/arXiv.2409.18227>.

Routines for performing empirical calibration of observational study estimates. By using a set of negative control hypotheses we can estimate the empirical null distribution of a particular observational study setup. This empirical null distribution can be used to compute a calibrated p-value, which reflects the probability of observing an estimated effect size when the null hypothesis is true taking both random and systematic error into account. A similar approach can be used to calibrate confidence intervals, using both negative and positive controls. For more details, see Schuemie et al. (2013) <doi:10.1002/sim.5925> and Schuemie et al. (2018) <doi:10.1073/pnas.1708282114>.

The purpose of this package is to support the setup the R environment. The two main features are autos', to automatically source files and/or directories into your environment, and paths to consistently set path objects across projects for input and output. Both are implemented using a configuration file to allow easy, custom configurations that can be used for multiple or all projects.

This package provides an R interface to the Evolution API <https://evoapicloud.com>, enabling sending and receiving WhatsApp messages directly from R'. Functions include sending text, images, documents, stickers, geographic locations, and interactive messages (lists). Also includes webhook parsing utilities and channel health checks.

Reads, writes, and edits EXIF and other file metadata using ExifTool <https://exiftool.org/>, returning read results as a data frame. ExifTool supports many different metadata formats including EXIF, GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, Lyrics3, as well as the maker notes of many digital cameras by Canon, Casio, DJI, FLIR, FujiFilm, GE, GoPro, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon, Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh, Samsung, Sanyo, Sigma/Foveon and Sony.

Background correction of spectral like data. Handles variations in scaling, polynomial baselines, interferents, constituents and replicate variation. Parameters for corrections are stored for further analysis, and spectra are corrected accordingly.

Analysis of items and persons in data. To identify and remove person misfit in polytomous item-response data using either mokken or a graded response model (GRM, via mirt'). Provides automatic thresholds, visual diagnostics (2D/3D), and export utilities. Methods build on Mokken scaling as in Mokken (1971, ISBN:9789027968821) and on the graded response model of Samejima (1969) <doi:10.1007/BF03372160>.

This package provides a predictable and pipeable framework for performing ETL (extract-transform-load) operations on publicly-accessible medium-sized data set. This package sets up the method structure and implements generic functions. Packages that depend on this package download specific data sets from the Internet, clean them up, and import them into a local or remote relational database management system.

Programmatic interface to the European Centre for Medium-Range Weather Forecasts dataset web services (ECMWF; <https://www.ecmwf.int/>) and Copernicus's Data Stores. Allows for easy downloads of weather forecasts and climate reanalysis data in R. Data stores covered include the Climate Data Store (CDS; <https://cds.climate.copernicus.eu>), Atmosphere Data Store (ADS; <https://ads.atmosphere.copernicus.eu>) and Early Warning Data Store (CEMS; <https://ewds.climate.copernicus.eu>).

Calculates several indices, such as of diversity, fluctuation, etc., and they are used to estimate ecological indicators.

Connect to Elasticsearch and OpenSearch', NoSQL databases built on the Java Virtual Machine and using the Apache Lucene library. Interacts with the Elasticsearch HTTP API (<https://www.elastic.co/elasticsearch/>) and the OpenSearch HTTP API (<https://opensearch.org/>). Includes functions for setting connection details to Elasticsearch and OpenSearch instances, loading bulk data, searching for documents with both HTTP query variables and JSON based body requests. In addition, elastic provides functions for interacting with APIs for indices', documents, nodes, clusters, an interface to the cat API, and more.

If translate English or Chinese sentence, there is a faster way for R user. You can pass in an English or Chinese sentence, ecce package support both English and Chinese translation. It also support browse translation results in website. In addition, also support obtain the pinyin of the Chinese character, you can more easily understand the pronunciation of the Chinese character.

Calculate cutoff values for model fit measures used in structural equation modeling (SEM) by simulating and testing data sets (cf. Hu & Bentler, 1999 <doi:10.1080/10705519909540118>) with the same parameters (population model, number of observations, etc.) as the model under consideration.

This package provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.

High-performance implementation of various effect plots useful for regression and probabilistic classification tasks. The package includes partial dependence plots (Friedman, 2021, <doi:10.1214/aos/1013203451>), accumulated local effect plots and M-plots (both from Apley and Zhu, 2016, <doi:10.1111/rssb.12377>), as well as plots that describe the statistical associations between model response and features. It supports visualizations with either ggplot2 or plotly', and is compatible with most models, including Tidymodels', models wrapped in DALEX explainers, or models with case weights.

Simulates cyclic voltammetry, linear-sweep voltammetry (both with and without stirring of the solution), and single-pulse and double-pulse chronoamperometry and chronocoulometry experiments using the implicit finite difference method outlined in Gosser (1993, ISBN: 9781560810261) and in Brown (2015) <doi:10.1021/acs.jchemed.5b00225>. Additional functions provide ways to display and to examine the results of these simulations. The primary purpose of this package is to provide tools for use in courses in analytical chemistry.

An implementation of extended state-space SIR models developed by Song Lab at UM school of Public Health. There are several functions available by 1) including a time-varying transmission modifier, 2) adding a time-dependent quarantine compartment, 3) adding a time-dependent antibody-immunization compartment. Wang L. (2020) <doi:10.6339/JDS.202007_18(3).0003>.

Fits engression models for nonlinear distributional regression. Predictors and targets can be univariate or multivariate. Functionality includes estimation of conditional mean, estimation of conditional quantiles, or sampling from the fitted distribution. Training is done full-batch on CPU (the python version offers GPU-accelerated stochastic gradient descent). Based on "Engression: Extrapolation through the lens of distributional regression" by Xinwei Shen and Nicolai Meinshausen (2024) in JRSSB. Also supports classification (experimental). <doi:10.1093/jrsssb/qkae108>.

This package provides a toolbox for implementing the Ecological Dynamic Regime framework (Sánchez-Pinillos et al., 2023 <doi:10.1002/ecm.1589>) to characterize and compare groups of ecological trajectories in multidimensional spaces defined by state variables. The package includes the RETRA-EDR algorithm to identify representative trajectories, functions to generate, summarize, and visualize representative trajectories, and several metrics to quantify the distribution and heterogeneity of trajectories in an ecological dynamic regime and quantify the dissimilarity between two or more ecological dynamic regimes. The package also includes a set of functions to assess ecological resilience based on ecological dynamic regimes (Sánchez-Pinillos et al., 2024 <doi:10.1016/j.biocon.2023.110409>).

This package provides various statistical methods for designing and analyzing randomized experiments. One functionality of the package is the implementation of randomized-block and matched-pair designs based on possibly multivariate pre-treatment covariates. The package also provides the tools to analyze various randomized experiments including cluster randomized experiments, two-stage randomized experiments, randomized experiments with noncompliance, and randomized experiments with missing data.