Minimal and memory efficient implementation of the junction tree
algorithm using the Lauritzen-Spiegelhalter scheme;
S. L. Lauritzen and D. J. Spiegelhalter (1988)
<https://www.jstor.org/stable/2345762?seq=1>.

person(given = "Mads",
family = "Lindskou",
role = c("aut", "cre"),
email = "madslindskou@gmail.com")

Analyzing and Presenting Social Scientific Data
person(c("Jacob","A."), "Long", email = "long.1377@osu.edu",
role = c("aut", "cre"))
This is a collection of tools that the author (Jacob) has written
for the purpose of more efficiently understanding and sharing the results of
(primarily) regression analyses. There are a number of functions focused
specifically on the interpretation and presentation of interactions in linear
models. Just about everything supports models from the survey package.
ggplot2, lmtest, sandwich, car, survey, weights,
RColorBrewer, cowplot, knitr, rmarkdown

Analysis and Presentation of Social Scientific Data
ggplot2, lmtest, sandwich, car, survey, weights,
RColorBrewer, cowplot, knitr, rmarkdown, testthat

ggplot2, lmtest, sandwich, car, survey, weights,
RColorBrewer, cowplot, lme4, knitr, rmarkdown, testthat

ggplot2, lmtest, sandwich, survey, car, weights,
RColorBrewer, cowplot, lme4, knitr, rmarkdown, testthat

boot, broom, car, cowplot, ggplot2, huxtable, lme4, lmtest,
methods, nlme, pbkrtest, RColorBrewer, ReporteRs, sandwich,
survey, weights, knitr, rmarkdown, testthat

boot, broom, car, cowplot, flextable, ggplot2, huxtable (>=
1.0.0), lme4, methods, nlme, officer, pbkrtest, RColorBrewer,
sandwich, stats4, survey, weights, knitr, rmarkdown, testthat

boot, broom, car, cowplot, flextable, grid, huxtable (>=
1.0.0), lme4, methods, nlme, officer, pbkrtest, plyr,
RColorBrewer, sandwich, stats4, survey, weights, knitr,
rmarkdown, testthat

boot, broom, car, cowplot, flextable, grid, huxtable (>=
1.0.0), lme4, methods, officer, pbkrtest, plyr, RColorBrewer,
sandwich, stats4, survey, weights, knitr, rmarkdown, testthat

This is a collection of tools that the author (Jacob) has written
for the purpose of more efficiently understanding and sharing the results of
(primarily) regression analyses. There are a number of functions focused
specifically on the interpretation and presentation of interactions.
Just about everything supports models from the survey package.
boot, broom, brms, cowplot, ggstance, huxtable (>= 3.0.0),
lme4, methods, pbkrtest, quantreg, RColorBrewer, rstanarm,
sandwich, stats4, survey, weights, knitr, rmarkdown, testthat