ridge.plots: Plot posterior distributions from model fit
In abnormally-distributed/abdisttools: Random Functions for Personal Use

Plot posterior distributions from model fit

ridge.plots(mcmc, family. = NA, description. = NA, burnin. = FALSE,
  par_labels. = NA, sort. = TRUE, inc_warmup. = FALSE,
  stan_include_auxiliar. = FALSE, droppars = c("lp__", "hs_c2", "nu",
  "sigma"), alpha = 0.65, hline.col = "#95bdd6", hline.alpha = 0.9)

`family.`	group of parameters with the same name but different numerical value between square brackets (as beta[1], beta[2])
`description.`	Character vector giving a short descriptive text that identifies the model.
`par_labels.`	data frame with two colums. One named "Parameter" with the same names of the parameters of the model.
`sort.`	Logical. When TRUE (the default), parameters are sorted first by family name and then by numerical value.
`inc_warmup.`	Logical. When dealing with stanfit objects from rstan, logical value whether the warmup samples are included. Defaults to FALSE.
`stan_include_auxiliar.`	Logical value to include "lp__" parameter in rstan, and "lp__", "treedepth__" and "stepsize__" in stan running without rstan. Defaults to FALSE.
`droppars`	variables to exclude. Defaults to droppars = c("lp__","hs_c2","nu","sigma").
`mcmc.list.`	an MCMC list