Higher Order Likelihood Inference (hoa)
Performs likelihood-based inference for a wide range of regression models. Provides higher-order approximations for inference based on extensions of saddlepoint type arguments as discussed in the book Applied Asymptotics: Case Studies in Small-Sample Statistics by Brazzale, Davison, and Reid (2007).
P-Value Weighting (pweight)
This R package contains open source implementations of several p-value weighting methods, including Spjotvoll, exponential and Bayes weights. These are methods for improving power in multiple testing via the use of prior information.
Principal Covariates Regression (PCovR)
Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components. Several rotation options are provided in this package, as well as model selection options.
PCovR: An R Package for Principal Covariates Regression
Classical Test Theory via Shiny (CTTShiny)
Interactive shiny application for running classical test theory (item analysis).
Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models (loo)
We efficiently approximate leave-one-out cross-validation (LOO) using very good importance sampling (VGIS), a new procedure for regularizing importance weights. As a byproduct of our calculations, we also obtain approximate standard errors for estimated predictive errors, and for the comparison of predictive errors between two models. We also compute the widely applicable information criterion (WAIC).