# making table data sets
library(dplyr)
library(tidyr)

cavi_update_tau <- function(y, x, m_z, s_z, m_lambda, s_lambda, m_alpha, m_beta, s_alpha, s_beta, m_mu, s_mu, a, b) {
.Call

` (should match variable in the model exactly)
#' @param symsize size of symbols (default = 1)
#' @param s_alpha opacity

symsize size of symbols (default = 2)
#' @param s_colour colour of symbols (default = `ok_orange`)
#' @param s_alpha

a progress bar
#' @author Avants BB, Pustina D
#' @examples

, then the first column of \code{X} must be all 1s.
#' @param y Quantitative response N*1 matrix.
#' @param s_alpha Hyperparameter

)*dzdphi
s_alpha <-  1/alpha + nu/alpha^2*log(A) - nu/alpha/A*( r*(c*g)^(alpha*r)*log(c*g) - 1/r*(c*g)^(-alpha/r)*log(c

IV_PRW <- function(data,
beta=0, mu_gamma=NA, s_gamma=NA, s_alpha=NA, rho=0

(getANTsRData("r16"))
mi <- antsImageRead(getANTsRData("r64"))
rig <- antsRegistration(

)*dzdphi
s_alpha <-  1/alpha + nu/alpha^2*log(A) - nu/alpha/A*( r*(c*g)^(alpha*r)*log(c*g) - 1/r*(c*g)^(-alpha/r)*log(c

)*dzdphi
s_alpha <-  1/alpha + nu/alpha^2*log(A) - nu/alpha/A*( r*(c*g)^(alpha*r)*log(c*g) - 1/r*(c*g)^(-alpha/r)*log(c

MRCIP <- function(data,
beta=0, mu_gamma=NA, s_gamma=NA, s_alpha=NA, rho=0,
h=NA

for alpha
#' @param s_alpha Positive real number, standard deviation of the prior normal distribution for alpha

for alpha
#' @param s_alpha Positive real number, standard deviation of the prior normal distribution for alpha

("^covariate", colnames(data), value = TRUE),
constraint = FALSE,
s_alpha = 30,

s_alpha = theta[4]
rho = theta[5]
cov = s_gamma * s_alpha * rho

= "patient",
covariates = grep("^covariate", colnames(data), value = TRUE),
s_alpha = 30,

s_alpha = 30,
s_delta = 30,
s_beta = 30,

for alpha
#' @param s_alpha Positive real number, standard deviation of the prior normal distribution for alpha