Fast Bayesian estimation and forecasting of age-specific rates, probabilities, and means, based on Template Model Builder'.

State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.

Implementation of the bootstrapping approach for the estimation of clustering stability and its application in estimating the number of clusters, as introduced by Yu et al (2016)<doi:10.1142/9789814749411_0007>. Implementation of the non-parametric bootstrap approach to assessing the stability of module detection in a graph, the extension for the selection of a parameter set that defines a graph from data in a way that optimizes stability and the corresponding visualization functions, as introduced by Tian et al (2021) <doi:10.1002/sam.11495>. Implemented out-of-bag stability estimation function and k-select Smin-based k-selection function as introduced by Liu et al (2022) <doi:10.1002/sam.11593>. Implemented ensemble clustering method based-on k-means clustering method, spectral clustering method and hierarchical clustering method.

This package provides a set of functions for doing analysis of A/B split test data and web metrics in general.

This package provides a comprehensive package to aid in the analysis of blood pressure data of all forms by providing both descriptive and visualization tools for researchers.

This package produces an economic evaluation of a sample of suitable variables of cost and effectiveness / utility for two or more interventions, e.g. from a Bayesian model in the form of MCMC simulations. This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis, see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.

This package provides a fully Bayesian approach in order to estimate a general family of cure rate models under the presence of covariates, see Papastamoulis and Milienos (2024) <doi:10.1007/s11749-024-00942-w> and Papastamoulis and Milienos (2024b) <doi:10.48550/arXiv.2409.10221>. The promotion time can be modelled (a) parametrically using typical distributional assumptions for time to event data (including the Weibull, Exponential, Gompertz, log-Logistic distributions), or (b) semiparametrically using finite mixtures of distributions. In both cases, user-defined families of distributions are allowed under some specific requirements. Posterior inference is carried out by constructing a Metropolis-coupled Markov chain Monte Carlo (MCMC) sampler, which combines Gibbs sampling for the latent cure indicators and Metropolis-Hastings steps with Langevin diffusion dynamics for parameter updates. The main MCMC algorithm is embedded within a parallel tempering scheme by considering heated versions of the target posterior distribution.

Two practical tests are provided for assessing whether multiple covariates in a treatment group and a matched control group are balanced in observational studies.

Combines the magick and imager packages to streamline image analysis, focusing on feature extraction and quantification from biological images, especially microparticles. By providing high throughput pipelines and clustering capabilities, biopixR facilitates efficient insight generation for researchers (Schneider J. et al. (2019) <doi:10.21037/jlpm.2019.04.05>).

Skinfold measurements is one of the most popular and practical methods for estimating percent body fat. Body composition is a term that describes the relative proportions of fat, bone, and muscle mass in the human body. Following the collection of skinfold measurements, regression analysis (a statistical procedure used to predict a dependent variable based on one or more independent or predictor variables) is used to estimate total percent body fat in humans. <doi:10.4324/9780203868744>.

The blocked weighted bootstrap (BBW) is an estimation technique for use with data from two-stage cluster sampled surveys in which either prior weighting (e.g. population-proportional sampling or PPS as used in Standardized Monitoring and Assessment of Relief and Transitions or SMART surveys) or posterior weighting (e.g. as used in rapid assessment method or RAM and simple spatial sampling method or S3M surveys) is implemented. See Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of bootstrap to cluster samples. See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) <doi:10.1371/journal.pone.0162462> for application of the blocked weighted bootstrap to estimate indicators from two-stage cluster sampled surveys.

General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.

Bayesian models for accurately estimating conditional distributions by race, using Bayesian Improved Surname Geocoding (BISG) probability estimates of individual race. Implements the methods described in McCartan, Fisher, Goldin, Ho and Imai (2025) <doi:10.1080/01621459.2025.2526695>.

Estimates conditional binary quantile models developed by Lu (2020) <doi:10.1017/pan.2019.29>. The estimation procedure is implemented based on Markov chain Monte Carlo methods.

This package provides a set of tools to read, analyze and write lists of click sequences on websites (i.e., clickstream). A click can be represented by a number, character or string. Clickstreams can be modeled as zero- (only computes occurrence probabilities), first- or higher-order Markov chains.

For those wishing to interact with the Charles Schwab Individual Trader API (<https://developer.schwab.com/products/trader-api--individual>) with R in a simplified manner, this package offers wrapper functions around authentication and the available API calls to streamline the process.

Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) <arXiv:2012.14159> are implemented.

Computes community climate statistics for volume and mismatch using species climate niches either unscaled or scaled relative to a regional species pool. These statistics can be used to describe biogeographic patterns and infer community assembly processes. Includes a vignette outlining usage.

Clustering multi-subject resting state functional Magnetic Resonance Imaging data. This methods enables the clustering of subjects based on multi-subject resting state functional Magnetic Resonance Imaging data. Objects are clustered based on similarities and differences in cluster-specific estimated components obtained by Independent Component Analysis.

Fit flexible and fully parametric hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. Our formulation allows for arbitrary functional forms of time and its interactions with other predictors for time-dependent hazards and hazard ratios. From the fitted hazard model, we provide functions to readily calculate and plot cumulative incidence and survival curves for a given covariate profile. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Based on the case-base sampling approach of Hanley and Miettinen (2009) <DOI:10.2202/1557-4679.1125>, Saarela and Arjas (2015) <DOI:10.1111/sjos.12125>, and Saarela (2015) <DOI:10.1007/s10985-015-9352-x>.

Provide step by step guided tours of Shiny applications.

Set of forecasting tools to predict ICU beds using a Vector Error Correction model with a single cointegrating vector. Method described in Berta, P. Lovaglio, P.G. Paruolo, P. Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, <https://www.york.ac.uk/media/economics/documents/hedg/workingpapers/2020/2016.pdf>.

Significance tests are provided for canonical correlation analysis, including asymptotic tests and a Monte Carlo method.

Example data sets to run the example problems from causal inference textbooks. Currently, contains data sets for Huntington-Klein, Nick (2021 and 2025) "The Effect" <https://theeffectbook.net>, first and second edition, Cunningham, Scott (2021 and 2025, ISBN-13: 978-0-300-25168-5) "Causal Inference: The Mixtape", and Hernán, Miguel and James Robins (2020) "Causal Inference: What If" <https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/>.