Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".

Up-and-Down is the most popular design approach for dose-finding, but has been severely under-served by the statistical computing community. This is the first package to address Up-and-Down's needs. For a recent methodological tutorial on Up-and-Down, see Oron et al. (2022) <doi:10.1097/ALN.0000000000004282>.

'UpSet.js' is a re-implementation of 'UpSetR' to create interactive set visualizations for more than three sets.
  This is a 'htmlwidget' wrapper around the 'JavaScript' library 'UpSet.js'.

Creates visualizations of intersecting sets using a novel matrix
    design, along with visualizations of several common set, element and attribute
    related tasks (Conway 2017) <doi:10.1093/bioinformatics/btx364>.

Using matrix layout to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explained variation on canonical analysis. These contributions were derived from variance partitioning analysis (VPA) and hierarchical partitioning (HP), applying the algorithm of Lai J., Zou Y., Zhang J., Peres-Neto P. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 <doi:10.1111/2041-210X.13800>.

Core functions necessary for using The Globe and Mail's R data journalism template, 'startr', along with utilities for day-to-day data journalism tasks, such as reading and writing files, producing graphics and cleaning up datasets.