A collection of tools to facilitate standardized analysis 
    and graphical procedures when using the National Cancer Institute’s 
    Patient-Reported Outcomes version of the Common Terminology Criteria for 
    Adverse Events (PRO-CTCAE) and other PRO measurements.

Models can be improved by post-processing class
    probabilities, by: recalibration, conversion to hard probabilities,
    assessment of equivocal zones, and other activities. 'probably'
    contains tools for conducting these operations as well as calibration
    tools and conformal inference techniques for regression models.

The 'probaverse' is a suite of packages designed to facilitate
    creating advanced statistical models through probability distributions.
    These packages work best when loaded together because they share a common
    design philosophy and focus on different aspects of developing statistical
    models. Inspired by the 'tidyverse' package, the 'probaverse' package
    makes it easy to load the entire suite of 'probaverse' packages together.

Functions and datasets  to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).

Use probability theory under the Bayesian framework for calculating the risk of selecting candidates in a multi-environment context. Contained are functions used to fit a Bayesian multi-environment model (based on the available presets), extract posterior values and maximum posterior values, compute the variance components, check the model’s convergence, and calculate the probabilities. For both across and within-environments scopes, the package computes the probability of superior performance and the pairwise probability of superior performance. Furthermore, the probability of superior stability and the pairwise probability of superior stability across environments is estimated. A joint probability of superior performance and stability is also provided.

Implements an efficient and powerful Bayesian approach for sparse high-dimensional linear regression. It uses minimal prior assumptions on the parameters through plug-in empirical Bayes estimates of hyperparameters. An efficient Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm estimates maximum a posteriori (MAP) values of regression parameters and variable selection probabilities. The PX-ECM results in a robust computationally efficient coordinate-wise optimization, which adjusts for the impact of other predictor variables. The E-step is motivated by the popular two-group approach to multiple testing. The result is a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm applied to sparse high-dimensional linear regression, implemented using one-at-a-time or all-at-once type optimization. More information can be found in McLain, Zgodic, and Bondell (2022) <arXiv:2209.08139>.