The parametric bootstrap joint (pbj) methods are designed for voxel-wise and cluster-extent inference and include semi-PBJ (sPBJ) inference that is robust to variance misspecification using an estimating equations approach. The package is designed for neuroimaging data and allows for input and output of neuroimaging data.

# Ongoing projects

The pbj package is in alpha stage as many features are still being added.

• A graphical user interface
• Semiparametric multidimensional inference (F-tests)
• Semiparametric longitudinal multidimensional inference (F-test in repeated measurements models)
• Semiparametric confidence set inference
• Effect size based inference
• Nonparametric bootstrap-based inference

## Installation

You can install the released version of pbj from GitHub:

devtools::install_github("simonvandekar/pbj")