Tailored explicitly for Experience Sampling Method (ESM) data, it contains a suite of functions designed to simplify preprocessing steps and create subsequent reporting. It empowers users with capabilities to extract critical insights during preprocessing, conducts thorough data quality assessments (e.g., design and sampling scheme checks, compliance rate, careless responses), and generates visualizations and concise summary tables tailored specifically for ESM data. Additionally, it streamlines the creation of informative and interactive preprocessing reports, enabling researchers to transparently share their dataset preprocessing methodologies. Finally, it is part of a larger ecosystem which includes a framework and a web gallery (<https://preprocess.esmtools.com/>).

Allows the user to determine minimum sample sizes that achieve target size and power at a specified alternative. For more information, see â Exact samples sizes for clinical trials subject to size and power constraintsâ by Lloyd, C.J. (2022) Preprint <doi:10.13140/RG.2.2.11828.94085>.

Download data from the European Social Survey directly from their website <http://www.europeansocialsurvey.org/>. There are two families of functions that allow you to download and interactively check all countries and rounds available.

This package provides estimation utilities for binary Emax dose-response models. Includes Expectation-Maximization based maximum likelihood estimation when the binary response is missing, as well as bias-reduced estimators including Jeffreys-penalized likelihood, Firth-score, and Cox-Snell corrections.The methodology is described in Zhang, Pradhan, and Zhao (2025) <doi:10.1177/09622802251403356> and Zhang, Pradhan, and Zhao (2026) <doi:10.1080/10543406.2026.2627387>.

Several functions, datasets, and sample codes related to empirical research in economics are included. They cover the marginal effects for binary or ordered choice models, static and dynamic Almost Ideal Demand System (AIDS) models, and a typical event analysis in finance.

Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies in an image. The image is first transformed into the wavelet domain in order to decorrelate any noise components, following which the coefficients at each resolution are standardised. Statistical tests (in a multiple hypothesis testing setting) are then carried out to find the anomalies. The power of EFDR exceeds that of standard FDR, which would carry out tests on every wavelet coefficient: EFDR choose which wavelets to test based on a criterion described in Shen et al. (2002). The package also provides elementary tools to interpolate spatially irregular data onto a grid of the required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N. Nonparametric hypothesis testing for a spatial signal. Journal of the American Statistical Association 97.460 (2002): 1122-1140.

This package contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through validation error, cross validation, or resubstitution. The function eztune.cv will compute a cross validated error rate. The purpose of eztune_cv is to provide a cross validated accuracy or MSE when resubstitution or validation data are used for optimization because error measures from both approaches can be misleading.

This package contains a large number of the goodness-of-fit tests for the Exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood based tests.

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.

Routines for combining causal effect estimates and study diagnostics across multiple data sites in a distributed study, without sharing patient-level data. Allows for normal and non-normal approximations of the data-site likelihood of the effect parameter.

Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors. A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.

This package performs automated morphological character partitioning for phylogenetic analyses and analyze macroevolutionary parameter outputs from clock (time-calibrated) Bayesian inference analyses, following concepts introduced by Simões and Pierce (2021) <doi:10.1038/s41559-021-01532-x>.

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 provides a set of functions to estimate capture probabilities and densities from multipass pass removal data.

This package provides a set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2022) <doi:10.3150/21-BEJ1375>, Davison et al. (2023) <doi:10.1080/07350015.2022.2078332>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>, Padoan and Rizzelli (2024) <doi:10.3150/23-BEJ1668>, Daouia et al. (2024) <doi:10.3150/23-BEJ1632>.

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>).

This package provides computational methods for detecting adverse high-order drug interactions from individual case safety reports using statistical techniques, allowing the exploration of higher-order interactions among drug cocktails.

This package provides tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).

Fit and sample from the ensemble model described in Spence et al (2018): "A general framework for combining ecosystem models"<doi:10.1111/faf.12310>.

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.>.

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.

Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) model. Empirical likelihood tests within a Cox model, for parameters defined via both baseline hazard function and regression parameters.

Presents two methods to estimate the parameters mu', sigma', and tau of an ex-Gaussian distribution. Those methods are Quantile Maximization Likelihood Estimation ('QMLE') and Bayesian. The QMLE method allows a choice between three different estimation algorithms for these parameters : neldermead ('NEMD'), fminsearch ('FMIN'), and nlminb ('NLMI'). For more details about the methods you can refer at the following list: Brown, S., & Heathcote, A. (2003) <doi:10.3758/BF03195527>; McCormack, P. D., & Wright, N. M. (1964) <doi:10.1037/h0083285>; Van Zandt, T. (2000) <doi:10.3758/BF03214357>; El Haj, A., Slaoui, Y., Solier, C., & Perret, C. (2021) <doi:10.19139/soic-2310-5070-1251>; Gilks, W. R., Best, N. G., & Tan, K. K. C. (1995) <doi:10.2307/2986138>.

Generation of bioclimatic rasters that are complementary to the typical 19 bioclim variables.