Block Gibbs sampler for adaptive CAR-LASSO

This function is for advanced users to build their own sampler use adaptive CARlasso as core. It will execute one round of Gibbs sampler of adaptive CAR-LASSO model. Be aware that the function is a void function implemented in C++, and all updated parameters e.g. Omega will be manipulate directly in memory to save space. Users should manage to do their own work to save the state. Also be aware that R uses shallow copy by default, which means one cannot save the state by simply give it to another object e.g. first Omega_old <- Omega_curr then update Omega_curr, Omega_old will also change. This function will NOT check dimensions of input. Below we assume n samples, k responses and p predictors.

rCARAlasso_(
  Z_curr,
  design,
  lambda2_beta,
  tau2_curr,
  beta_curr,
  lambda_Omega,
  Omega_curr,
  mu_curr,
  r_beta,
  delta_beta,
  r_Omega,
  delta_Omega,
  lambda_diag,
  k,
  p,
  n
)

Arguments

Z_curr	the current (latent) normal Z_curr, should be n*k. Will not be changed
design	the design matrix, should be n*p. Will not be changed
lambda2_beta	the current shrinkage parameter of regression coefficients, should be a vector with p*k entries. Will be updated
tau2_curr	the current latent scale parameter in the normal mixture representation of Laplace, for regression coefficients, should be a vector with p*k entries. Will be updated.
beta_curr	the current regression coefficients, should be a matrix sized p*k (p row and k columns). Will be updated.
lambda_Omega	the current shrinkage parameter for Omega, should be a vector with k*(k-1)/2 entries. Will be updated.
Omega_curr	the current Omega matrix, should be a matrix of size k*k. Will be updated.
mu_curr	the current mu, intercept, should be a vector of size k. Will be updated.
r_beta	hyperprior's parameter of shrinkage for regression coefficients, should be a scalar of type 'double' and positive. Will not be updated.
delta_beta	hyperprior's parameter of shrinkage for regression coefficients, should be a scalar of type 'double' and positive. Will not be updated.
r_Omega	hyperprior's parameter of shrinkage for precision Omega, should be a scalar of type 'double' and positive. Will not be updated.
delta_Omega	hyperprior's parameter of shrinkage for rprecision Omega, should be a scalar of type 'double' and positive. Will not be updated.
lambda_diag	shrinkage parameter of the diagonal of Omega, should be a vector of size k, should be non-negative. Will not be updated
k	integer, number of responses
p	integer, number of predictors
n	integer, number of Z_curr points

Value

Again this is a void function and will not return anything. All update happened in memory directly.