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-------- spatial error model estimation functions --------
compare_models : An example of using sar_g() sem_g() Gibbs sampling
compare_models2 : An example of using sar_g() sem_g() Gibbs sampling
compare_weights : An example of using sem_c() function
compare_weights2 : An example of using sem_c() function
f2_sem : evaluates SEM log-likelihood -- given ML parameters using sparse matrix algorithms
f_sem : evaluates SEM concentrated log-likelihood using sparse matrix algorithms
prt_bmae : print results from sar_gcbma, sem_gcbma functions
prt_sem : Prints output using spatial error model results structures
sem : computes spatial error model estimates
sem_c : Bayesian log-marginal posterior for the spatial error model
sem_d : An example of using sem
sem_d2 : An example of using sem() on a large data set
sem_d3 : An example of using sem() on a large data set
sem_g : Bayesian estimates of the spatial error model
sem_gcbma : MC^3 x-matrix specification for homoscedastic SEM model
sem_gcbmad : compute posterior probabilities of
sem_gd : An example of using sem_g()
sem_gd2 : An example of using sem_g()
sem_testd : A timing comparison of heteroscedastic versus homoscedastic on small and large datasets
semp_g : Bayesian estimates of the spatial probit error model
semp_gd : An example of using sempp_g() Gibbs sampling spatial autoregressive probit model
semp_gd2 : An example of using semp_g()
test_bayes : A comparison of Bayesian and ML estimates
test_bayes2 : A comparison of Bayesian and ML estimates
test_bayes3 : A comparison of Bayesian and ML estimates
test_bayes4 : A comparison of Bayesian and ML estimates
test_bayes5 : A comparison of Bayesian and ML estimates
test_maxlik : A test of the accuracy of max-like estimates