#' Compute the value of the count density function from the count model
#' residuals.
#' 
#' Calculate density values from a normal: $f(x) = 1/(sqrt (2 pi ) sigma )
#' e^-((x - mu )^2/(2 sigma^2))$.  Maximum-likelihood estimates are
#' approximated using the EM algorithm where we treat mixture membership
#' $deta_ij$ = 1 if $y_ij$ is generated from the zero point mass as latent
#' indicator variables. The density is defined as $f_zig(y_ij = pi_j(S_j) cdot
#' f_0(y_ij) +(1-pi_j (S_j))cdot f_count(y_ij;mu_i,sigma_i^2)$. The
#' log-likelihood in this extended model is $(1-delta_ij) log
#' f_count(y;mu_i,sigma_i^2 )+delta_ij log pi_j(s_j)+(1-delta_ij)log (1-pi_j
#' (sj))$. The responsibilities are defined as $z_ij = pr(delta_ij=1 | data)$.
#' 
#' 
#' @param residuals Residuals from the count model.
#' @param log Whether or not we are calculating from a log-normal distribution.
#' @return Density values from the count model residuals.
#' @seealso \code{\link{fitZig}}
getCountDensity <-
function(residuals, log=FALSE){
	dnorm(residuals,log=log)
}