This package provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.

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.

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>).

Exploratory and descriptive analysis of event based data. Provides methods for describing and selecting process data, and for preparing event log data for process mining. Builds on the S3-class for event logs implemented in the package bupaR'.

This package provides a set of functions for organising and analysing datasets from experiments run using Eyelink eye-trackers. Organising functions help to clean and prepare eye-tracking datasets for analysis, and mark up key events such as display changes and responses made by participants. Analysing functions help to create means for a wide range of standard measures (such as mean fixation durations'), which can then be fed into the appropriate statistical analyses and graphing packages as necessary.

Data that are collected through online sources such as Mechanical Turk may require excluding rows because of IP address duplication, geolocation, or completion duration. This package facilitates exclusion of these data for Qualtrics datasets.

Facilitates access to sample datasets from the EunomiaDatasets repository (<https://github.com/ohdsi/EunomiaDatasets>).

Simulate and fitting exponential multivariate Hawkes model. This package simulates a multivariate Hawkes model, introduced by Hawkes (1971) <doi:10.2307/2334319>, with an exponential kernel and fits the parameters from the data. Models with the constant parameters, as well as complex dependent structures, can also be simulated and estimated. The estimation is based on the maximum likelihood method, introduced by introduced by Ozaki (1979) <doi:10.1007/BF02480272>, with maxLik package.

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.

Enables simulation of water piping networks using EPANET'. The package provides functions from the EPANET programmer's toolkit as R functions so that basic or customized simulations can be carried out from R. The package uses EPANET version 2.2 from Open Water Analytics <https://github.com/OpenWaterAnalytics/EPANET/releases/tag/v2.2>.

User friendly interface based on the R package gstat to fit exponential parametric models to empirical semi-variograms in order to model the spatial correlation structure of health data. Geo-located health outcomes of survey participants may be used to model spatial effects on health in an ego-centred approach. The package contains a range of functions to help explore the spatial structure of the data as well as visualize the fit of exponential models for various metaparameter combinations with respect to the number of lag intervals and maximal distance. Furthermore, the outcome of interest can be adjusted for covariates by fitting a linear regression in a preliminary step before the semi-variogram fitting process.

This package provides a consistent representation of year-based time scales as a numeric vector with an associated era'. There are built-in era definitions for many year numbering systems used in contemporary and historic calendars (e.g. Common Era, Islamic Hijri years); year-based time scales used in archaeology, astronomy, geology, and other palaeosciences (e.g. Before Present, SI-prefixed annus'); and support for arbitrary user-defined eras. Years can converted from any one era to another using a generalised transformation function. Methods are also provided for robust casting and coercion between years and other numeric types, type-stable arithmetic with years, and pretty-printing in tables.

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial error (SEM), spatial Durbin (SDM), spatial error Durbin (SDEM) and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.

Simulates and estimates the Exponential Random Partition Model presented in the paper Hoffman, Block, and Snijders (2023) <doi:10.1177/00811750221145166>. It can also be used to estimate longitudinal partitions, following the model proposed in Hoffman and Chabot (2023) <doi:10.1016/j.socnet.2023.04.002>. The model is an exponential family distribution on the space of partitions (sets of non-overlapping groups) and is called in reference to the Exponential Random Graph Models (ERGM) for networks.

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>).

Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>. entropart provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity. Estimation-bias corrections are available.

Import data from Epidata XML files .epx and convert it to R data structures.

This package provides tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) <doi:10.1093/aje/kwh255>.

This package contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.

This package provides a principled framework for sampling Virtual Control Group (VCG) using energy distance-based covariate balancing. The package offers visualization tools to assess covariate balance and includes a permutation test to evaluate the statistical significance of observed deviations.

This package provides a collection of curated educational datasets for teaching ecology and agriculture concepts. Includes data on wildlife monitoring, plant treatments, and ecological observations with documentation and examples for educational use. All datasets are derived from published scientific studies and are available under CC0 or compatible licenses.

This package provides a dataframe-friendly implementation of ComBat Harmonization which uses an empirical Bayesian framework to remove batch effects. Johnson WE & Li C (2007) <doi:10.1093/biostatistics/kxj037> "Adjusting batch effects in microarray expression data using empirical Bayes methods." Fortin J-P, Cullen N, Sheline YI, Taylor WD, Aselcioglu I, Cook PA, Adams P, Cooper C, Fava M, McGrath PJ, McInnes M, Phillips ML, Trivedi MH, Weissman MM, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.11.024> "Harmonization of cortical thickness measurements across scanners and sites." Fortin J-P, Parker D, Tun<e7> B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE, Schultz RT, Verma R, & Shinohara RT (2017) <doi:10.1016/j.neuroimage.2017.08.047> "Harmonization of multi-site diffusion tensor imaging data.".

Process and analyze electronic health record (EHR) data. The EHR package provides modules to perform diverse medication-related studies using data from EHR databases. Especially, the package includes modules to perform pharmacokinetic/pharmacodynamic (PK/PD) analyses using EHRs, as outlined in Choi, Beck, McNeer, Weeks, Williams, James, Niu, Abou-Khalil, Birdwell, Roden, Stein, Bejan, Denny, and Van Driest (2020) <doi:10.1002/cpt.1787>. Additional modules will be added in future. In addition, this package provides various functions useful to perform Phenome Wide Association Study (PheWAS) to explore associations between drug exposure and phenotypes obtained from EHR data, as outlined in Choi, Carroll, Beck, Mosley, Roden, Denny, and Van Driest (2018) <doi:10.1093/bioinformatics/bty306>.

This package performs analyzes and estimates of environmental covariates and genetic parameters related to selection strategies and development of superior genotypes. It has two main functionalities, the first being about prediction models of covariates and environmental processes, while the second deals with the estimation of genetic parameters and selection strategies. Designed for researchers and professionals in genetics and environmental sciences, the package combines statistical methods for modeling and data analysis. This includes the plastochron estimate proposed by Porta et al. (2024) <doi:10.1590/1807-1929/agriambi.v28n10e278299>, Stress indices for genotype selection referenced by Ghazvini et al. (2024) <doi:10.1007/s10343-024-00981-1>, the Environmental Stress Index described by Tazzo et al. (2024) <https://revistas.ufg.br/vet/article/view/77035>, industrial quality indices of wheat genotypes (Szareski et al., 2019), <doi:10.4238/gmr18223>, Ear Indexes estimation (Rigotti et al., 2024), <doi:10.13083/reveng.v32i1.17394>, Selection index for protein and grain yield (de Pelegrin et al., 2017), <doi:10.4236/ajps.2017.813224>, Estimation of the ISGR - Genetic Selection Index for Resilience for environmental resilience (Bandeira et al., 2024) <https://www.cropj.com/Carvalho_18_12_2024_825_830.pdf>, estimation of Leaf Area Index (Meira et al., 2015) <https://www.fag.edu.br/upload/revista/cultivando_o_saber/55d1ef202e494.pdf>, Restriction of control variability (Carvalho et al., 2023) <doi:10.4025/actasciagron.v45i1.56156>, Risk of Disease Occurrence in Soybeans described by Engers et al. (2024) <doi:10.1007/s40858-024-00649-1> and estimation of genetic parameters for selection based on balanced experiments (Yadav et al., 2024) <doi:10.1155/2024/9946332>.