Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time the picture was taken on, from JPEG images. Incorporates the easyexif <https://github.com/mayanklahiri/easyexif> library.

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.

Deliver the full functionality of ECharts with minimal overhead. echarty users build R lists for ECharts API. Lean set of powerful commands.

Fit, plot and compare several (extreme value) distribution functions. Compute (truncated) distribution quantile estimates and plot return periods on a linear scale. On the fitting method, see Asquith (2011): Distributional Analysis with L-moment Statistics [...] ISBN 1463508417.

Dry seed germinates by imbibing water from soil where the physiological process of germination starts after sufficient water has been imbibed by the seed. The germination time of the seed is inversely proportion to the difference between soil water potential and the base seed water potential which is described by hydro time model (Bradford, 2002 <https://www.jstor.org/stable/4046371>). The parameters of the model like speed of germination, stress tolerance, uniformity of germination are unknown fixed values (Ghosh et al., 2026 <doi:10.1111/aab.70041>) which are to be estimated using statistical regression model where the validity of the adopted statistical model has been established theoretically. The package will help to estimate the tuning parameter for proportion of viable seeds along with standard error and p- values for inference.

The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided.

Combine pieces of evidence in the form of uncertainty representations.

Data sets from the book "Forecasting with exponential smoothing: the state space approach" by Hyndman, Koehler, Ord and Snyder (Springer, 2008).

Easily create interactive charts by leveraging the Echarts Javascript library which includes 36 chart types, themes, Shiny proxies and animations.

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 implements Escalation With Overdose Control trial designs using two drug combinations described by this paper <doi:10.1002/sim.6961>(Tighiouart et al., 2016). It calculates the recommended dose for next cohorts and perform simulations to obtain operating characteristics.

This package implements entropy balancing, a data preprocessing procedure described in Hainmueller (2012, <doi:10.1093/pan/mpr025>) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user-specified moment conditions. Useful for creating balanced samples in observational studies with a binary treatment where the control group is reweighted to match the covariate moments of the treatment group, and for reweighting a survey sample to known characteristics from a target population.

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.

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.

This package provides tools and pre-trained Machine Learning [ML] models for calibration of Agent-Based Models [ABMs] built with the R package epiworldR'. Implements methods described in Najafzadehkhoei, Vega Yon, Modenesi, and Meyer (2025) <doi:10.48550/arXiv.2509.07013>. Users can automatically calibrate ABMs in seconds with pre-trained ML models, effectively focusing on simulation rather than calibration. Bridges a gap by allowing public health practitioners to run their own ABMs without the advanced technical expertise often required by calibration.

This package provides a meta-package that installs and loads a set of packages from easystats ecosystem in a single step. This collection of packages provide a unifying and consistent framework for statistical modeling, visualization, and reporting. Additionally, it provides articles targeted at instructors for teaching easystats', and a dashboard targeted at new R users for easily conducting statistical analysis by accessing summary results, model fit indices, and visualizations with minimal programming.

This package provides a convenient toolbox to import data exported from Electronic Data Capture (EDC) software TrialMaster'.

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>.

Bayesian Model Averaging to create probabilistic forecasts from ensemble forecasts and weather observations <https://stat.uw.edu/sites/default/files/files/reports/2007/tr516.pdf>.

Univariate and multivariate methods for compositional data analysis, based on logratios. The package implements the approach in the book Compositional Data Analysis in Practice by Michael Greenacre (2018), where accent is given to simple pairwise logratios. Selection can be made of logratios that account for a maximum percentage of logratio variance. Various multivariate analyses of logratios are included in the package.

This package provides a novel concept for generating knowledge and gaining insights into laboratory data. You will be able to efficiently and easily explore your laboratory data from different perspectives. Janitza, S., Majumder, M., Mendolia, F., Jeske, S., & Kulmann, H. (2021) <doi:10.1007/s43441-021-00318-4>.

This package implements EKIO's visual identity for data visualization with ggplot2'. Supplies a coordinated theme, color palettes, discrete and continuous scales, and helpers for accessible text contrast. Includes high-level recipes for common charts so analysts can create consistent, publication-ready graphics with concise code.

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.

This package implements local influence diagnostics for the Extreme-Value Birnbaum-Saunders (EVBS) regression model: joint maximum likelihood estimation, conformal normal curvature diagnostics under three perturbation schemes (case-weight, response variable, and explanatory variable), randomized quantile residuals with simulation envelope, Monte Carlo simulation utilities, and publication-quality density and diagnostic plots. Version 1.1.0 adds the density, distribution and quantile functions, the finite upper endpoint, return levels and expected shortfall, block bootstrap standard errors for serially dependent series, local influence diagnostics for the generalized extreme-value regression model, and a GAMLSS family allowing the tail-shape parameter to depend on covariates. Version 1.2.0 adds a prospective control chart for endpoint identifiability. The methods are described in Ospina, Lima, Barros, and Macedo (2026, submitted) and are applied to monthly maximum wind gust data from Itajai, Brazil.