This package provides tools to fit Mixture Cure Rate models via the Expectation-Maximization (EM) algorithm, allowing for flexible link functions in the cure component and various survival distributions in the latency part. The package supports user-specified link functions, includes methods for parameter estimation and model diagnostics, and provides residual analysis tailored for cure models. The classical theory methods used are described in Berkson, J. and Gage, R. P. (1952) <doi:10.2307/2281318>, Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977) <https://www.jstor.org/stable/2984875>, Bazán, J., Torres-Avilés, F., Suzuki, A. and Louzada, F. (2017)<doi:10.1002/asmb.2215>.

Treats language model evaluations as statistical experiments and supplies the inference they require. Provides central limit theorem and cluster-robust standard errors for evaluation scores, paired and unpaired model comparisons, variance decomposition when several responses are drawn per question, control-variate variance reduction, multiplicity adjustment across benchmark suites, and power and minimum detectable effect calculations for planning evaluations, following Miller (2024) <doi:10.48550/arXiv.2411.00640>. For evaluations scored by a model judge, implements agreement statistics against a human gold standard and prediction-powered inference (Angelopoulos et al. 2023) <doi:10.1126/science.adi6000> with the power-tuned estimator of Angelopoulos, Bates and Jordan (2023) <doi:10.48550/arXiv.2311.01453>, so a small set of human labels debiases a large set of judge scores. Leaderboards are supported through bootstrap rank intervals and Bradley-Terry ratings (Bradley and Terry 1952) <doi:10.2307/2334029>. Accepts scores from any evaluation harness.

This package provides functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. EHRtemporalVariability is particularly suitable for multi-modal data and categorical variables with a high number of values, common features of biomedical data where traditional statistical process control or time-series methods may not be appropriate. EHRtemporalVariability allows you to explore and identify dataset shifts through visual analytics formats such as Data Temporal heatmaps and Information Geometric Temporal (IGT) plots. An additional EHRtemporalVariability Shiny app can be used to load and explore the package results and even to allow the use of these functions to those users non-experienced in R coding. (Sáez et al. 2020) <doi:10.1093/gigascience/giaa079>.

Evaluation Platform in Chronic Obstructive Pulmonary Disease (EPIC) is a Discrete Event Simulation (DES) model that simulates health outcomes of patients with Chronic Obstructive Pulmonary Disease (COPD) based on demographics and individual-level risk factors, based on the model published in Sadatsafavi et al. (2019) <doi:10.1177/0272989X18824098>.

Reads EXIF data using ExifTool <https://exiftool.org> and returns results as a data frame. ExifTool is a platform-independent Perl library plus a command-line application for reading, writing and editing meta information in a wide variety of files. ExifTool supports many different metadata formats including EXIF, GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR, FujiFilm, GE, 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.

This package provides an automated workflow for Principal Component Analysis (PCA) that simplifies multivariate data analysis by performing essential preprocessing, statistical tests, component extraction, and visualization in a single function call. The package automatically standardizes variables, computes correlation matrices, performs Kaiser-Meyer-Olkin (KMO) and Bartlett's tests, determines the optimal number of principal components using multiple selection criteria, generates component loadings and scores, and produces publication-ready tables and graphical outputs for researchers and students. Methodological background is described in Shankar et al. (2024) <doi:10.1007/s12665-024-11985-5>.

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.

This package performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.

An implementation of multiple-locus association mapping on a genome-wide scale. Eagle can handle inbred and outbred study populations, populations of arbitrary unknown complexity, and data larger than the memory capacity of the computer. Since Eagle is based on linear mixed models, it is best suited to the analysis of data on continuous traits. However, it can tolerate non-normal data. Eagle reports, as its findings, the best set of snp in strongest association with a trait. For users unfamiliar with R, to perform an analysis, run OpenGUI()'. This opens a web browser to the menu-driven user interface for the input of data, and for performing genome-wide analysis.

Fits Leroux model in spectral domain to estimate causal spatial effect as detailed in Guan, Y; Page, G.L.; Reich, B.J.; Ventrucci, M.; Yang, S; (2020) <arXiv:2012.11767>. Both the parametric and semi-parametric models are available. The semi-parametric model relies on INLA'. The INLA package can be obtained from <https://www.r-inla.org/>.

This package contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). Appropriate print and summary statements are also written to facilitate interpretation wherever possible. Source code is commented throughout to facilitate modification. The target audience includes advanced undergraduate and graduate students in epidemiology or biostatistics courses, and clinical researchers.

Package for analysis of simple experimental designs (CRD, RBD and LSD), experiments in double factorial schemes (in CRD and RBD), experiments in a split plot in time schemes (in CRD and RBD), experiments in double factorial schemes with an additional treatment (in CRD and RBD), experiments in triple factorial scheme (in CRD and RBD) and experiments in triple factorial schemes with an additional treatment (in CRD and RBD), performing the analysis of variance and means comparison by fitting regression models until the third power (quantitative treatments) or by a multiple comparison test, Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott, Duncan test, t test (LSD) and Bonferroni t test (protected LSD) - for qualitative treatments; residual analysis (Ferreira, Cavalcanti and Nogueira, 2014) <doi:10.4236/am.2014.519280>.

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.

This package provides functions to quantify animal dominance hierarchies. The major focus is on Elo rating and its ability to deal with temporal dynamics in dominance interaction sequences. For static data, David's score and de Vries I&SI are also implemented. In addition, the package provides functions to assess transitivity, linearity and stability of dominance networks. See Neumann et al (2011) <doi:10.1016/j.anbehav.2011.07.016> for an introduction.

This package provides a rich toolkit of using the whole building simulation program EnergyPlus'(<https://energyplus.net>), which enables programmatic navigation, modification of EnergyPlus models and makes it less painful to do parametric simulations and analysis.

This package provides a collection of nice plotting functions directly from a data.frame with limited customisation possibilities.

This package implements an empirical Bayes, multi-state Cox model for survival analysis. Run "?'ebmstate-package'" for details. See also Schall (1991) <doi:10.1093/biomet/78.4.719>.

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.

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.

This package provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.

This package provides a flexible framework for calculating Elo ratings and resulting rankings of any two-team-per-matchup system (chess, sports leagues, Go', etc.). This implementation is capable of evaluating a variety of matchups, Elo rating updates, and win probabilities, all based on the basic Elo rating system. It also includes methods to benchmark performance, including logistic regression and Markov chain models.

Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.

Collection of functions related to benchmark with prediction models for data analysis and editing of clinical and epidemiological data.

An extension of knitr that adds flexibility in several ways. One common source of frustration with knitr is that it assumes the directory where the source file lives should be the working directory, which is often not true. ezknitr addresses this problem by giving you complete control over where all the inputs and outputs are, and adds several other convenient features to make rendering markdown/HTML documents easier.