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### Unnormalized log-posterior probability of a model.
logp.u <- function(model,X,y,lam,w)
{
if(is.logical(model)) model = which(model);
model = as.integer(model)
n = nrow(X)
logw = log(w/(1-w))
p0 = length(model)
if(p0 == 0)
return( -0.5*(n-1)*log(n-1) );
x.g = scale(X[,model,drop=FALSE])
ys = scale(y)
xtx = crossprod(x.g) + diag(x = lam,nrow = p0)
R = chol(xtx)
z = backsolve(R,crossprod(x.g,ys),transpose = T)
logp = 0.5*p0*log(lam) - sum(log(diag(R))) - 0.5*(n-1)*log(sum(ys^2) - sum(z^2)) + p0*logw
return(logp)
}
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