Nothing
# library(Rcpp)
#
# stat_pois <- '
# Rcpp::NumericVector stat_poisson_cp(Rcpp::NumericVector yin,
# double ty,
# Rcpp::NumericVector ein,
# Rcpp::NumericVector eout,
# unsigned int min_cases,
# Rcpp::NumericVector popin,
# double max_pop) {
# unsigned int yin_length = yin.length();
# double lrin, lrout;
# Rcpp::NumericVector tall(yin_length, 0);
# Rcpp::NumericVector yout(yin_length, 0);
#
# // determine if there will be any problematic statistics
# for (unsigned int i = 0; i < yin_length; i++) {
# // compute statistic for good locations
# // yin > 0 and yin/ein > yout/ein
# if (yin[i] >= min_cases & popin[i] <= max_pop) {
# yout[i] = ty - yin[i];
# lrin = log(yin[i]) - log(ein[i]);
# lrout = log(yout[i]) - log(eout[i]);
# if (lrin > lrout) {
# tall[i] = yin[i] * lrin + yout[i] * lrout;
# }
# }
# }
#
# return tall;
# }
# '
#
# cppFunction(stat_pois)
#
# smerc::stat.poisson(
# yin = 106, yout = 552 - 106,
# ein = 62.13, eout = 552 - 62.13
# )
#
# stat_poisson_cp(106,
# 552,
# 62.13,
# 552 - 62.13,
# 2,
# 15000,
# 20000)
#
#
# mst_seq <- 'Rcpp::List(int region,
# Rcpp::IntegerVector neighbors,
# Rcpp::NumericVector cases,
# Rcpp::NumericVector pop,
# Rcpp::NumericVector ex,
# Rcpp::IntegerMatrix w,
# double ty,
# double max_pop,
# unsigned int type,
# unsigned int nlinks,
# bool early
# unsigned int min_cases) {
# // initialize objects
# unsigned int nneighbs = neighbors.size();
# loglikrat = Rcpp::NumericVector(nneighbs);
# yin = Rcpp::NumericVector(nneighbs);
# ein = Rcpp::NumericVector(nneighbs);
# popin = Rcpp::NumericVector(nneighbs);
#
# // first step
# yin[0] = cases[region];
# ein[0] = ex[region];
# popin[0] = pop[region];
# loglikrat = stat_poisson_cp(yin, ty, ein, eout, min_cases, popin, max_pop);
#
# // loglikrat[1] <- scan.stat(yin[1], ein[1], ty - ein[1], ty)
# // uz <- max_neighbors <- vector("list", length(neighbors))
# // uz[[1]] <- region
# // max_neighbors[[1]] <- setdiff(neighbors, region)
#
# return List::create(
# Named("loglikrat") = loglikrat,
# Named("cases") = yin,
# Named("expected") = ein,
# Named("population") = popin
# );
# }
# '
#
# cppFunction(code = mst_seq, includes = stat_pois)
#
#
#
# start, neighbors, cases, pop, w, ex, ty,
# max_pop, type = "maxonly", nlinks = "one",
# early = FALSE) {
# loglikrat <- yin <- ein <- popin <- numeric(length(neighbors))
# region <- start
# yin[1] <- cases[region]
# ein[1] <- ex[region]
# popin[1] <- pop[region]
# loglikrat[1] <- scan.stat(yin[1], ein[1], ty - ein[1], ty)
# uz <- max_neighbors <- vector("list", length(neighbors))
# uz[[1]] <- region
# max_neighbors[[1]] <- setdiff(neighbors, region)
#
# # body5 <-
# # '
# # arma::mat cov_spBayes(arma::mat D, int sp_type, double sigmasq,
# # double phi, double nu, double ev, double fv)
# # {
# # int nr = D.n_rows;
# # int nc = D.n_cols;
# #
# # arma::mat V = arma::zeros(nc, nc);
# #
# # for(int i = 0; i < nr; i++)
# # {
# # for(int j = 0; j < nc; j++)
# # {
# # if(D(i, j) == 0)
# # {
# # V(i, j) = sigmasq + ev + fv;
# # }
# # else
# # {
# # if(sp_type == 1)
# # {
# # V(i, j) = sigmasq * exp(-D(i, j) * phi);
# # }
# # else if(sp_type == 2)
# # {
# # V(i, j) = sigmasq * exp(-pow(D(i, j) * phi, 2));
# # }
# # else if(sp_type == 3)
# # {
# # V(i, j) = sigmasq * pow(D(i,j)*phi, nu)/(pow(2, nu-1)*R::gammafn(nu))*R::bessel_k(D(i,j)*phi, nu, 1.0);
# # }
# # else
# # {
# # if(D(i, j) <= 1.0/phi)
# # {
# # V(i, j) = sigmasq * (1.0 - 1.5*phi*D(i,j) + 0.5*pow(phi*D(i,j),3));
# # }
# #
# # }
# # }
# # }
# # }
# # return V;
# # }
# # '
# #
# # cppFunction(body5, depends = "RcppArmadillo")
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