Showing 2 of total 2 results (show query)
donaldrwilliams
BGGM:Bayesian Gaussian Graphical Models
Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.
Maintained by Philippe Rast. Last updated 3 months ago.
bayes-factorsbayesian-hypothesis-testinggaussian-graphical-modelsopenblascppopenmp
55 stars 9.61 score 102 scripts 1 dependents
dtkaplan
LSTbook:Data and Software for "Lessons in Statistical Thinking"
"Lessons in Statistical Thinking" D.T. Kaplan (2014) <https://dtkaplan.github.io/Lessons-in-statistical-thinking/> is a textbook for a first or second course in statistics that embraces data wrangling, causal reasoning, modeling, statistical adjustment, and simulation. 'LSTbook' supports the student-centered, tidy, pipeline-oriented computing style featured in the book.
Maintained by Daniel Kaplan. Last updated 4 days ago.
4 stars 6.32 score 27 scripts