[Jenkins] auto-formatting by clang-format version 6.0.0-1ubuntu2~16.04.1 (tags/RELEASE_600/final)

Author: stan-buildbot

stan/math/opencl/prim/neg_binomial_2_lpmf.hpp

@@ -87,15 +87,16 @@ inline return_type_t<T_n_cl, T_location_cl, T_precision_cl> neg_binomial_2_lpmf(
       = select(mu_val < phi_val, log1p(-elt_divide(mu_val, mu_plus_phi)),
                log_phi - log_mu_plus_phi);
   auto phi_deriv = elt_divide(mu_val - n, mu_plus_phi) + log_term
-      - digamma(phi_val) + digamma(n_plus_phi);
+                   - digamma(phi_val) + digamma(n_plus_phi);
 
   matrix_cl<double> logp_cl;
   matrix_cl<double> mu_deriv_cl;
   matrix_cl<double> phi_deriv_cl;
 
-  results(check_n_nonnegative, check_mu_positive_finite, check_phi_positive_finite,
-          logp_cl, mu_deriv_cl, phi_deriv_cl)
-      = expressions(n_nonnegative, mu_positive_finite, phi_positive_finite, logp_expr,
+  results(check_n_nonnegative, check_mu_positive_finite,
+          check_phi_positive_finite, logp_cl, mu_deriv_cl, phi_deriv_cl)
+      = expressions(n_nonnegative, mu_positive_finite, phi_positive_finite,
+                    logp_expr,
                     calc_if<!is_constant<T_location_cl>::value>(mu_deriv),
                     calc_if<!is_constant<T_precision_cl>::value>(phi_deriv));
 

test/unit/math/opencl/rev/neg_binomial_2_lpmf_test.cpp

@@ -82,8 +82,8 @@ TEST(muProbDistributionsNegBinomial2, opencl_matches_cpu_small) {
   Eigen::VectorXd phi(N);
   phi << 0.3, 0.8, 1.3;
 
-  stan::math::test::compare_cpu_opencl_prim_rev(neg_binomial_2_lpmf_functor,
-                                                n, mu, phi);
+  stan::math::test::compare_cpu_opencl_prim_rev(neg_binomial_2_lpmf_functor, n,
+                                                mu, phi);
   stan::math::test::compare_cpu_opencl_prim_rev(
       neg_binomial_2_lpmf_functor_propto, n, mu, phi);
 }
@@ -143,8 +143,8 @@ TEST(muProbDistributionsNegBinomial2, opencl_matches_cpu_big) {
   Eigen::Matrix<double, Eigen::Dynamic, 1> phi
       = Eigen::Array<double, Eigen::Dynamic, 1>::Random(N, 1).abs();
 
-  stan::math::test::compare_cpu_opencl_prim_rev(neg_binomial_2_lpmf_functor,
-                                                n, mu, phi);
+  stan::math::test::compare_cpu_opencl_prim_rev(neg_binomial_2_lpmf_functor, n,
+                                                mu, phi);
   stan::math::test::compare_cpu_opencl_prim_rev(
       neg_binomial_2_lpmf_functor_propto, n, mu, phi);
 }

test/unit/math/opencl/rev/weibull_lpdf_test.cpp

@@ -116,8 +116,8 @@ TEST(ProbDistributionsWeibull, opencl_broadcast_alpha) {
   Eigen::VectorXd sigma(N);
   sigma << 0.3, 0.8, 1.0;
 
-  stan::math::test::test_opencl_broadcasting_prim_rev<1>(weibull_lpdf_functor, y,
-                                                         alpha_scal, sigma);
+  stan::math::test::test_opencl_broadcasting_prim_rev<1>(weibull_lpdf_functor,
+                                                         y, alpha_scal, sigma);
   stan::math::test::test_opencl_broadcasting_prim_rev<1>(
       weibull_lpdf_functor_propto, y, alpha_scal, sigma);
 }
@@ -131,8 +131,8 @@ TEST(ProbDistributionsWeibull, opencl_broadcast_sigma) {
   alpha << 0.3, 0.8, 1.0;
   double sigma_scal = 12.3;
 
-  stan::math::test::test_opencl_broadcasting_prim_rev<2>(weibull_lpdf_functor, y,
-                                                         alpha, sigma_scal);
+  stan::math::test::test_opencl_broadcasting_prim_rev<2>(weibull_lpdf_functor,
+                                                         y, alpha, sigma_scal);
   stan::math::test::test_opencl_broadcasting_prim_rev<2>(
       weibull_lpdf_functor_propto, y, alpha, sigma_scal);
 }