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

Author: stan-buildbot

stan/math/prim/fun/log_gamma_q_dgamma.hpp

@@ -48,8 +48,7 @@ struct log_gamma_q_result {
  */
 template <typename T_a, typename T_z>
 inline log_gamma_q_result<return_type_t<T_a, T_z>> log_gamma_q_dgamma(
-    const T_a& a, const T_z& z, int max_steps = 250,
-    double precision = 1e-16) {
+    const T_a& a, const T_z& z, int max_steps = 250, double precision = 1e-16) {
   using std::exp;
   using std::fabs;
   using std::log;
@@ -99,7 +98,8 @@ inline log_gamma_q_result<return_type_t<T_a, T_z>> log_gamma_q_dgamma(
     // For gradient, use: d/da log(Q) = (1/Q) * dQ/da
     // grad_reg_inc_gamma computes dQ/da
     const double Q_val = exp(result.log_q);
-    const double dQ_da = grad_reg_inc_gamma(a_dbl, z_dbl, tgamma(a_dbl), digamma(a_dbl));
+    const double dQ_da
+        = grad_reg_inc_gamma(a_dbl, z_dbl, tgamma(a_dbl), digamma(a_dbl));
     result.dlog_q_da = dQ_da / Q_val;
 
   } else {
@@ -111,7 +111,8 @@ inline log_gamma_q_result<return_type_t<T_a, T_z>> log_gamma_q_dgamma(
     // grad_reg_inc_gamma computes dQ/da
     const double Q_val = exp(result.log_q);
     if (Q_val > 0) {
-      const double dQ_da = grad_reg_inc_gamma(a_dbl, z_dbl, tgamma(a_dbl), digamma(a_dbl));
+      const double dQ_da
+          = grad_reg_inc_gamma(a_dbl, z_dbl, tgamma(a_dbl), digamma(a_dbl));
       result.dlog_q_da = dQ_da / Q_val;
     } else {
       // Fallback if Q rounds to zero - use asymptotic approximation

stan/math/prim/prob/gamma_lccdf.hpp

@@ -30,7 +30,8 @@ namespace internal {
 
 /**
  * Compute log(Q(a,x)) using continued fraction expansion for upper incomplete
- * gamma function. When used with fvar types, automatically computes derivatives.
+ * gamma function. When used with fvar types, automatically computes
+ * derivatives.
  *
  * @tparam T_a Type of shape parameter a (double or fvar types)
  * @param a Shape parameter
@@ -118,10 +119,9 @@ return_type_t<T_y, T_shape, T_inv_scale> gamma_lccdf(const T_y& y,
   const size_t N = max_size(y, alpha, beta);
 
   constexpr bool need_y_beta_deriv = !is_constant_all<T_y, T_inv_scale>::value;
-  constexpr bool any_fvar
-      = is_fvar<scalar_type_t<T_y>>::value
-        || is_fvar<scalar_type_t<T_shape>>::value
-        || is_fvar<scalar_type_t<T_inv_scale>>::value;
+  constexpr bool any_fvar = is_fvar<scalar_type_t<T_y>>::value
+                            || is_fvar<scalar_type_t<T_shape>>::value
+                            || is_fvar<scalar_type_t<T_inv_scale>>::value;
   constexpr bool partials_fvar = is_fvar<T_partials_return>::value;
 
   for (size_t n = 0; n < N; n++) {
@@ -164,8 +164,7 @@ return_type_t<T_y, T_shape, T_inv_scale> gamma_lccdf(const T_y& y,
             alpha_unit.d_ = 1;
             auto beta_unit = beta_y;
             beta_unit.d_ = 0;
-            auto log_Qn_fvar
-                = internal::log_q_gamma_cf(alpha_unit, beta_unit);
+            auto log_Qn_fvar = internal::log_q_gamma_cf(alpha_unit, beta_unit);
             dlogQ_dalpha = log_Qn_fvar.d_;
           } else {
             const T_partials_return Qn = exp(log_Qn);