[Jenkins] auto-formatting by clang-format version 10.0.0-4ubuntu1

Author: stan-buildbot

test/unit/math/rev/core/precomputed_gradients_test.cpp

@@ -131,8 +131,7 @@ TEST_F(AgradRev, StanAgradRevInternal_precomputed_gradients_containers) {
   stan::math::recover_memory();
 }
 
-TEST_F(AgradRev,
-       precomputed_gradients_containers_direct_construction) {
+TEST_F(AgradRev, precomputed_gradients_containers_direct_construction) {
   double value = 1;
   std::vector<stan::math::var> vars;
   std::vector<double> gradients;

test/unit/math/rev/functor/integrate_1d_impl_test.cpp

@@ -278,9 +278,8 @@ TEST_F(AgradRev, integrate_1d_impl_rev_TestDerivatives_zero_crossing) {
                                   -19.06340613646808, 21.41380852375568);
 }
 
-TEST_F(
-    AgradRev,
-    integrate_1d_impl_rev_TestDerivatives_var_right_endpoint_var_params) {
+TEST_F(AgradRev,
+       integrate_1d_impl_rev_TestDerivatives_var_right_endpoint_var_params) {
   // Zero crossing integral + test x_r + vars at right endpoint
   using stan::math::var;
   test_derivatives<double, var, var>(
@@ -290,9 +289,8 @@ TEST_F(
       {5 * pow(0.5, 1.5), 12 * 1.75 * 1.75, 4.0}, 0.0, 21.41380852375568);
 }
 
-TEST_F(
-    AgradRev,
-    integrate_1d_impl_rev_TestDerivatives_var_left_endpoint_var_params) {
+TEST_F(AgradRev,
+       integrate_1d_impl_rev_TestDerivatives_var_left_endpoint_var_params) {
   // Zero crossing integral + test x_r + var at left endpoint
   using stan::math::var;
   test_derivatives<var, double, var>(
@@ -312,8 +310,7 @@ TEST_F(AgradRev, integrate_1d_impl_rev_TestDerivatives_no_param_vars) {
                                      {}, -19.06340613646808, 21.41380852375568);
 }
 
-TEST_F(AgradRev,
-       integrate_1d_impl_rev_TestDerivatives_left_limit_var) {
+TEST_F(AgradRev, integrate_1d_impl_rev_TestDerivatives_left_limit_var) {
   // No param vars, only left limit var
   using stan::math::var;
   test_derivatives<var, double, double>(f3{}, -1.0, 1.0, {0.5, 1.75, 3.9},
@@ -323,8 +320,7 @@ TEST_F(AgradRev,
                                         {}, -19.06340613646808, 0.0);
 }
 
-TEST_F(AgradRev,
-       integrate_1d_impl_rev_TestDerivatives_right_limit_var) {
+TEST_F(AgradRev, integrate_1d_impl_rev_TestDerivatives_right_limit_var) {
   // No param vars, only right limit var
   using stan::math::var;
   test_derivatives<double, var, double>(f3{}, -1.0, 1.0, {0.5, 1.75, 3.9},
@@ -359,8 +355,7 @@ TEST_F(AgradRev, integrate_1d_impl_rev_TestDerivatives_tricky3) {
       f6{}, 0.0, 1.0, {0.75}, {}, {}, 0.851926727945904, {0.4814066053874294});
 }
 
-TEST_F(AgradRev,
-       integrate_1d_impl_rev_TestDerivatives_zero_crossing2) {
+TEST_F(AgradRev, integrate_1d_impl_rev_TestDerivatives_zero_crossing2) {
   // Zero crossing integral + limit at infinity + var at left limit
   using stan::math::var;
   test_derivatives<var, double, var>(
@@ -369,8 +364,7 @@ TEST_F(AgradRev,
       std::numeric_limits<double>::quiet_NaN());
 }
 
-TEST_F(AgradRev,
-       integrate_1d_impl_rev_TestDerivatives_zero_crossing3) {
+TEST_F(AgradRev, integrate_1d_impl_rev_TestDerivatives_zero_crossing3) {
   // Zero crossing integral + limit at negative infinity + var at right limit
   using stan::math::var;
   test_derivatives<double, var, var>(
@@ -388,8 +382,7 @@ TEST_F(AgradRev, integrate_1d_impl_rev_TestDerivatives_indefinite) {
       2.536571480364399, {});
 }
 
-TEST_F(AgradRev,
-       integrate_1d_impl_rev_TestDerivatives_endpoint_precision) {
+TEST_F(AgradRev, integrate_1d_impl_rev_TestDerivatives_endpoint_precision) {
   // Various integrals of beta function
   using stan::math::var;
   test_derivatives<double, double, var>(f11{}, 0.0, 1.0, {0.1, 0.1}, {}, {},

test/unit/math/rev/prob/categorical_logit_glm_lpmf_test.cpp

@@ -30,9 +30,7 @@ categorical_logit_glm_simple_lpmf(const std::vector<int>& y, const T_x& x,
   return lpmf;
 }
 
-TEST_F(
-    AgradRev,
-    CategoricalLogitGLM_glm_matches_categorical_logit_doubles) {
+TEST_F(AgradRev, CategoricalLogitGLM_glm_matches_categorical_logit_doubles) {
   using Eigen::Dynamic;
   using Eigen::Matrix;
   using Eigen::MatrixXd;

test/unit/math/rev/prob/neg_binomial_2_log_glm_lpmf_test.cpp

@@ -7,9 +7,7 @@
 
 //  We check that the values of the new regression match those of one built
 //  from existing primitives.
-TEST_F(
-    AgradRev,
-    NegBinomial2LogGLM_glm_matches_neg_binomial_2_log_doubles) {
+TEST_F(AgradRev, NegBinomial2LogGLM_glm_matches_neg_binomial_2_log_doubles) {
   using Eigen::Dynamic;
   using Eigen::Matrix;
   using stan::math::var;
@@ -33,9 +31,8 @@ TEST_F(
 }
 //  We check that the values of the new regression match those of one built
 //  from existing primitives.
-TEST_F(
-    AgradRev,
-    NegBinomial2LogGLM_glm_matches_neg_binomial_2_log_doubles_rand) {
+TEST_F(AgradRev,
+       NegBinomial2LogGLM_glm_matches_neg_binomial_2_log_doubles_rand) {
   using Eigen::Dynamic;
   using Eigen::Matrix;
   using stan::math::var;
@@ -543,9 +540,8 @@ TYPED_TEST(ProbDistributionsNegBinomial2LogGLM,
 }
 
 //  We check that the right errors are thrown.
-TEST_F(
-    AgradRev,
-    NegBinomial2LogGLM_glm_matches_neg_binomial_2_log_error_checking) {
+TEST_F(AgradRev,
+       NegBinomial2LogGLM_glm_matches_neg_binomial_2_log_error_checking) {
   using Eigen::Dynamic;
   using Eigen::Matrix;
   using stan::math::var;

test/unit/math/rev/prob/ordered_logistic_glm_lpmf_test.cpp

@@ -14,9 +14,7 @@ ordered_logistic_glm_simple_lpmf(const std::vector<int>& y, T_x&& x,
   return stan::math::ordered_logistic_lpmf<propto>(y, location, cuts);
 }
 
-TEST_F(
-    AgradRev,
-    OrderedLogisticGLM_glm_matches_ordered_logistic_doubles) {
+TEST_F(AgradRev, OrderedLogisticGLM_glm_matches_ordered_logistic_doubles) {
   using Eigen::MatrixXd;
   using Eigen::VectorXd;
   using std::vector;
@@ -34,9 +32,8 @@ TEST_F(
                   ordered_logistic_glm_simple_lpmf<false>(y, x, beta, cuts));
 }
 
-TEST_F(
-    AgradRev,
-    OrderedLogisticGLM_glm_matches_ordered_logistic_doubles_broadcast_y) {
+TEST_F(AgradRev,
+       OrderedLogisticGLM_glm_matches_ordered_logistic_doubles_broadcast_y) {
   using Eigen::MatrixXd;
   using Eigen::VectorXd;
   using std::vector;

test/unit/math/rev/prob/skew_double_exponential_ccdf_log_test.cpp

@@ -26,8 +26,7 @@ inline auto skew_de_ccdf_test(const T1& y, const T2& mu, const T3& sigma,
   }
 }
 
-TEST_F(AgradRev,
-       SkewedDoubleExponential_lccdf_computes_correct_gradients) {
+TEST_F(AgradRev, SkewedDoubleExponential_lccdf_computes_correct_gradients) {
   using stan::math::skew_double_exponential_lccdf;
 
   for (double ys : {-1.7, 0.2, 0.5, 0.9, 1.1, 3.2, 8.3}) {