Finished tidying

Author: Andrew Johnson

basic_estimators/bernoulli.stan

@@ -1,12 +1,11 @@
 data { 
   int<lower=0> N; 
-  int<lower=0,upper=1> y[N];
+  int<lower=0, upper=1> y[N];
 } 
 parameters {
-  real<lower=0,upper=1> theta;
+  real<lower=0, upper=1> theta;
 } 
 model {
-  theta ~ beta(1,1);
-  for (n in 1:N) 
-    y[n] ~ bernoulli(theta);
+  theta ~ beta(1, 1);
+  y ~ bernoulli(theta);
 }

basic_estimators/bernoulli_new.stan

@@ -7,8 +7,7 @@ parameters {
   real<lower=0> beta;
 }
 model { 
-  alpha ~ cauchy(0,10);
-  beta ~ cauchy(0,10);
-  for (i in 1:N)
-    y[i] ~ neg_binomial(alpha, beta);
+  alpha ~ cauchy(0, 10);
+  beta ~ cauchy(0, 10);
+  y ~ neg_binomial(alpha, beta);
 }

basic_estimators/negative_binomial.stan

@@ -4,13 +4,11 @@ data {
 }
 parameters {
   real<lower=0> alpha;
-  real<lower=0,upper=1> p_success;
+  real<lower=0, upper=1> p_success;
 }
 transformed parameters {
-   real<lower=0.0> beta;
-   beta <- p_success / (1.0 - p_success);
+   real<lower=0.0> beta = p_success / (1.0 - p_success);
 }
 model { 
-  for (i in 1:N)
-    y[i] ~ neg_binomial(alpha, beta);
+  y ~ neg_binomial(alpha, beta);
 }

basic_estimators/negative_binomial2.stan

@@ -8,10 +8,10 @@ parameters {
   real mu;
 }
 model {
+  // Vectorization with truncation is not yet supported
   for (n in 1:N_observed)
     y[n] ~ normal(mu,1.0) T[,U];
-  increment_log_prob(N_censored 
-                     * log1m(normal_cdf(U,mu,1.0)));
+  target += N_censored * normal_lccdf(U | mu, 1);
 }