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

Author: stan-buildbot

stan/math/mix/prob/laplace_latent_rng.hpp

@@ -72,8 +72,8 @@ template <typename LLFunc, typename LLArgs, typename CovarFun,
 inline auto laplace_latent_rng(LLFunc&& L_f, LLArgs&& ll_args,
                                int hessian_block_size,
                                CovarFun&& covariance_function,
-                               CovarArgs&& covar_args,
-                               RNG& rng, std::ostream* msgs) {
+                               CovarArgs&& covar_args, RNG& rng,
+                               std::ostream* msgs) {
   auto options = laplace_options_default{hessian_block_size};
   return laplace_base_rng(
       std::forward<LLFunc>(L_f), std::forward<LLArgs>(ll_args),

stan/math/mix/prob/laplace_marginal.hpp

@@ -32,8 +32,8 @@ template <bool propto = false, typename LFun, typename LArgs, typename CovarFun,
 inline auto laplace_marginal_tol(LFun&& L_f, LArgs&& l_args,
                                  int hessian_block_size,
                                  CovarFun&& covariance_function,
-                                 CovarArgs&& covar_args,
-                                 OpsTuple&& ops, std::ostream* msgs) {
+                                 CovarArgs&& covar_args, OpsTuple&& ops,
+                                 std::ostream* msgs) {
   auto options
       = internal::tuple_to_laplace_options(std::forward<OpsTuple>(ops));
   options.hessian_block_size = hessian_block_size;
@@ -64,11 +64,9 @@ inline auto laplace_marginal_tol(LFun&& L_f, LArgs&& l_args,
  */
 template <bool propto = false, typename LFun, typename LArgs, typename CovarFun,
           typename CovarArgs>
-inline auto laplace_marginal(LFun&& L_f, LArgs&& l_args,
-                             int hessian_block_size,
+inline auto laplace_marginal(LFun&& L_f, LArgs&& l_args, int hessian_block_size,
                              CovarFun&& covariance_function,
-                             CovarArgs&& covar_args,
-                             std::ostream* msgs) {
+                             CovarArgs&& covar_args, std::ostream* msgs) {
   auto options = laplace_options_default{hessian_block_size};
   return laplace_marginal_density(
       std::forward<LFun>(L_f), std::forward<LArgs>(l_args),

test/unit/math/laplace/aki_ex_test.cpp

@@ -85,8 +85,8 @@ TEST(WriteArrayBodySimple, ExceededIteration) {
     double ll_laplace_val{0};
     try {
       ll_laplace_val = stan::math::laplace_marginal(
-          poisson_re_log_ll_functor(), std::forward_as_tuple(y, mu),
-          1, cov_fun_functor(), std::tuple<double, int>(sigmaz, 1), pstream);
+          poisson_re_log_ll_functor(), std::forward_as_tuple(y, mu), 1,
+          cov_fun_functor(), std::tuple<double, int>(sigmaz, 1), pstream);
     } catch (const std::domain_error& e) {
       // Log bad values to CSV files
       ADD_FAILURE() << "Laplace failed"
@@ -164,8 +164,8 @@ TEST(WriteArrayBodySimple, ExecutesBodyWithHardcodedData) {
       }
     }
     auto ll_laplace_all = stan::math::laplace_marginal(
-        poisson_re_log_ll_functor(), std::forward_as_tuple(y, mu),
-        1, cov_fun_functor(), std::tuple<double, int>(sigmaz, N), pstream);
+        poisson_re_log_ll_functor(), std::forward_as_tuple(y, mu), 1,
+        cov_fun_functor(), std::tuple<double, int>(sigmaz, N), pstream);
     stan::test::relative_tolerance sum_rel_tol(3e-2);
     expect_near_rel("sum laplace vs integrated sum", ll_laplace,
                     ll_integrate_1d, sum_rel_tol, "laplace_sum",

test/unit/math/laplace/laplace_marginal_bernoulli_logit_lpmf_test.cpp

@@ -45,8 +45,7 @@ TEST_P(laplace_marginal_bernoulli_logit_lpmf, phi_dim500) {
   using stan::math::test::sqr_exp_kernel_functor;
   double target = laplace_marginal_bernoulli_logit_lpmf(
       y, n_samples, 0, hessian_block_size, sqr_exp_kernel_functor{},
-      std::forward_as_tuple(x, phi_dbl(0), phi_dbl(1)),
-      nullptr);
+      std::forward_as_tuple(x, phi_dbl(0), phi_dbl(1)), nullptr);
   // Benchmark against gpstuff.
   constexpr double tol = 8e-4;
   EXPECT_NEAR(-195.368, target, tol);

test/unit/math/laplace/laplace_marginal_lpdf_moto_test.cpp

@@ -148,8 +148,8 @@ TEST_P(laplace_motorcyle_gp_test, gp_motorcycle_val) {
   LAPLACE_SKIP_IF_INVALID_TEST_COMBO(hessian_block_size, dim_theta);
 
   laplace_marginal_tol<false>(
-      normal_likelihood{}, std::forward_as_tuple(y, n_obs),
-      hessian_block_size, covariance_motorcycle_functor{},
+      normal_likelihood{}, std::forward_as_tuple(y, n_obs), hessian_block_size,
+      covariance_motorcycle_functor{},
       std::forward_as_tuple(x, phi_dbl(0), phi_dbl(1), phi_dbl(2), phi_dbl(3),
                             n_obs),
       std::make_tuple(theta0, tolerance, max_num_steps, solver_num,

test/unit/math/laplace/laplace_types_test.cpp

@@ -326,8 +326,8 @@ TEST_P(laplace_types, poisson_log_phi_dim_2_array_tuple) {
       alpha_tuple.push_back(std::make_tuple(alpha_rho(0), alpha_rho(1)));
       return laplace_marginal_tol<false>(
           poisson_log_likelihood_array_tuple{},
-          std::forward_as_tuple(sums, eta_tuple),
-          hessian_block_size, stan::math::test::squared_kernel_functor{},
+          std::forward_as_tuple(sums, eta_tuple), hessian_block_size,
+          stan::math::test::squared_kernel_functor{},
           std::forward_as_tuple(x, alpha_tuple),
           std::make_tuple(theta_0, tolerance, max_num_steps, solver_num,
                           max_steps_line_search, true),