// Code generated by stanc v2.33.1
#include <stan/model/model_header.hpp>
namespace prophet_model_namespace {
using stan::model::model_base_crtp;
using namespace stan::math;
stan::math::profile_map profiles__;
static constexpr std::array<const char*, 96> locations_array__ =
  {" (found before start of program)",
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  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 124, column 4 to column 29)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 123, column 35 to line 125, column 3)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 123, column 9 to line 125, column 3)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 122, column 4 to column 64)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 121, column 35 to line 123, column 3)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 121, column 9 to line 125, column 3)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 120, column 4 to column 54)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 119, column 28 to line 121, column 3)",
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  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 131, column 2 to column 19)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 132, column 2 to column 37)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 133, column 2 to column 29)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 134, column 2 to column 27)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 137, column 2 to line 142, column 4)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 88, column 2 to column 8)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 89, column 2 to column 17)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 90, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 90, column 2 to column 14)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 91, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 91, column 2 to column 16)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 92, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 92, column 2 to column 14)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 93, column 2 to column 8)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 94, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 94, column 2 to column 21)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 95, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 95, column 11 to column 12)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 95, column 2 to column 16)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 96, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 96, column 2 to column 19)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 97, column 2 to column 20)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 98, column 2 to column 22)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 99, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 99, column 2 to column 16)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 100, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 100, column 2 to column 16)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 104, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 104, column 12 to column 13)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 104, column 2 to column 61)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 105, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 105, column 12 to column 13)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 105, column 2 to column 47)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 106, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 106, column 12 to column 13)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 106, column 2 to column 47)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 112, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 114, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 118, column 9 to column 10)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 9, column 11 to column 12)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 9, column 14 to column 15)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 9, column 4 to column 19)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 10, column 15 to column 16)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 10, column 4 to column 24)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 11, column 4 to column 15)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 14, column 4 to column 28)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 15, column 4 to column 33)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 16, column 4 to column 15)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 21, column 8 to column 26)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 22, column 8 to column 28)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 20, column 58 to line 23, column 7)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 20, column 6 to line 23, column 7)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 24, column 6 to column 19)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 19, column 19 to line 25, column 5)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 19, column 4 to line 25, column 5)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 26, column 4 to column 13)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 7, column 73 to line 27, column 3)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 32, column 11 to column 12)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 32, column 4 to column 20)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 33, column 11 to column 16)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 33, column 4 to column 22)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 34, column 4 to column 14)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 37, column 4 to column 51)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 40, column 4 to column 13)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 42, column 6 to column 66)",
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  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 41, column 4 to line 44, column 5)",
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  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 31, column 78 to line 46, column 3)",
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  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 83, column 4 to column 28)",
  " (in '/croot/prophet_1718022778575/work/build/lib.linux-x86_64-cpython-311/prophet/stan_model/prophet.stan', line 82, column 4 to line 84, column 3)"};
template <typename T0__, typename T1__, typename T2__, typename T3__,
          stan::require_all_t<stan::is_col_vector<T0__>,
                              stan::is_vt_not_complex<T0__>,
                              stan::is_col_vector<T1__>,
                              stan::is_vt_not_complex<T1__>,
                              std::is_integral<T2__>, std::is_integral<T3__>>* = nullptr>
Eigen::Matrix<stan::promote_args_t<stan::base_type_t<T0__>,
                stan::base_type_t<T1__>>,-1,-1>
get_changepoint_matrix(const T0__& t_arg__, const T1__& t_change_arg__,
                       const T2__& T, const T3__& S, std::ostream* pstream__);
template <typename T0__, typename T1__, typename T2__, typename T3__,
          typename T4__,
          stan::require_all_t<stan::math::disjunction<stan::is_autodiff<T0__>,
                                                      std::is_floating_point<T0__>>,
                              stan::math::disjunction<stan::is_autodiff<T1__>,
                                                      std::is_floating_point<T1__>>,
                              stan::is_col_vector<T2__>,
                              stan::is_vt_not_complex<T2__>,
                              stan::is_col_vector<T3__>,
                              stan::is_vt_not_complex<T3__>,
                              std::is_integral<T4__>>* = nullptr>
Eigen::Matrix<stan::promote_args_t<T0__, T1__, stan::base_type_t<T2__>,
                stan::base_type_t<T3__>>,-1,1>
logistic_gamma(const T0__& k, const T1__& m, const T2__& delta_arg__,
               const T3__& t_change_arg__, const T4__& S, std::ostream*
               pstream__);
template <typename T0__, typename T1__, typename T2__, typename T3__,
          typename T4__, typename T5__, typename T6__, typename T7__,
          stan::require_all_t<stan::math::disjunction<stan::is_autodiff<T0__>,
                                                      std::is_floating_point<T0__>>,
                              stan::math::disjunction<stan::is_autodiff<T1__>,
                                                      std::is_floating_point<T1__>>,
                              stan::is_col_vector<T2__>,
                              stan::is_vt_not_complex<T2__>,
                              stan::is_col_vector<T3__>,
                              stan::is_vt_not_complex<T3__>,
                              stan::is_col_vector<T4__>,
                              stan::is_vt_not_complex<T4__>,
                              stan::is_eigen_matrix_dynamic<T5__>,
                              stan::is_vt_not_complex<T5__>,
                              stan::is_col_vector<T6__>,
                              stan::is_vt_not_complex<T6__>,
                              std::is_integral<T7__>>* = nullptr>
Eigen::Matrix<stan::promote_args_t<T0__, T1__, stan::base_type_t<T2__>,
                stan::base_type_t<T3__>, stan::base_type_t<T4__>,
                stan::promote_args_t<stan::base_type_t<T5__>,
                  stan::base_type_t<T6__>>>,-1,1>
logistic_trend(const T0__& k, const T1__& m, const T2__& delta_arg__,
               const T3__& t_arg__, const T4__& cap_arg__, const T5__&
               A_arg__, const T6__& t_change_arg__, const T7__& S,
               std::ostream* pstream__);
template <typename T0__, typename T1__, typename T2__, typename T3__,
          typename T4__, typename T5__,
          stan::require_all_t<stan::math::disjunction<stan::is_autodiff<T0__>,
                                                      std::is_floating_point<T0__>>,
                              stan::math::disjunction<stan::is_autodiff<T1__>,
                                                      std::is_floating_point<T1__>>,
                              stan::is_col_vector<T2__>,
                              stan::is_vt_not_complex<T2__>,
                              stan::is_col_vector<T3__>,
                              stan::is_vt_not_complex<T3__>,
                              stan::is_eigen_matrix_dynamic<T4__>,
                              stan::is_vt_not_complex<T4__>,
                              stan::is_col_vector<T5__>,
                              stan::is_vt_not_complex<T5__>>* = nullptr>
Eigen::Matrix<stan::promote_args_t<T0__, T1__, stan::base_type_t<T2__>,
                stan::base_type_t<T3__>, stan::base_type_t<T4__>,
                stan::promote_args_t<stan::base_type_t<T5__>>>,-1,1>
linear_trend(const T0__& k, const T1__& m, const T2__& delta_arg__,
             const T3__& t_arg__, const T4__& A_arg__, const T5__&
             t_change_arg__, std::ostream* pstream__);
template <typename T0__, typename T1__,
          stan::require_all_t<stan::math::disjunction<stan::is_autodiff<T0__>,
                                                      std::is_floating_point<T0__>>,
                              std::is_integral<T1__>>* = nullptr>
Eigen::Matrix<stan::promote_args_t<T0__>,-1,1>
flat_trend(const T0__& m, const T1__& T, std::ostream* pstream__);
// matrix get_changepoint_matrix(vector, vector, int, int)
template <typename T0__, typename T1__, typename T2__, typename T3__,
          stan::require_all_t<stan::is_col_vector<T0__>,
                              stan::is_vt_not_complex<T0__>,
                              stan::is_col_vector<T1__>,
                              stan::is_vt_not_complex<T1__>,
                              std::is_integral<T2__>, std::is_integral<T3__>>*>
Eigen::Matrix<stan::promote_args_t<stan::base_type_t<T0__>,
                stan::base_type_t<T1__>>,-1,-1>
get_changepoint_matrix(const T0__& t_arg__, const T1__& t_change_arg__,
                       const T2__& T, const T3__& S, std::ostream* pstream__) {
  using local_scalar_t__ = stan::promote_args_t<stan::base_type_t<T0__>,
                             stan::base_type_t<T1__>>;
  int current_statement__ = 0;
  // suppress unused var warning
  (void) current_statement__;
  const auto& t = stan::math::to_ref(t_arg__);
  const auto& t_change = stan::math::to_ref(t_change_arg__);
  static constexpr bool propto__ = true;
  // suppress unused var warning
  (void) propto__;
  local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
  // suppress unused var warning
  (void) DUMMY_VAR__;
  try {
    current_statement__ = 56;
    stan::math::validate_non_negative_index("A", "T", T);
    current_statement__ = 57;
    stan::math::validate_non_negative_index("A", "S", S);
    Eigen::Matrix<local_scalar_t__,-1,-1> A =
      Eigen::Matrix<local_scalar_t__,-1,-1>::Constant(T, S, DUMMY_VAR__);
    current_statement__ = 59;
    stan::math::validate_non_negative_index("a_row", "S", S);
    Eigen::Matrix<local_scalar_t__,1,-1> a_row =
      Eigen::Matrix<local_scalar_t__,1,-1>::Constant(S, DUMMY_VAR__);
    int cp_idx = std::numeric_limits<int>::min();
    current_statement__ = 62;
    stan::model::assign(A, stan::math::rep_matrix(0, T, S),
      "assigning variable A");
    current_statement__ = 63;
    stan::model::assign(a_row, stan::math::rep_row_vector(0, S),
      "assigning variable a_row");
    current_statement__ = 64;
    cp_idx = 1;
    current_statement__ = 71;
    for (int i = 1; i <= T; ++i) {
      current_statement__ = 68;
      while ((stan::math::primitive_value(stan::math::logical_lte(cp_idx, S))
             &&
             stan::math::primitive_value(
               stan::math::logical_gte(
                 stan::model::rvalue(t, "t", stan::model::index_uni(i)),
                 stan::model::rvalue(t_change, "t_change",
                   stan::model::index_uni(cp_idx)))))) {
        current_statement__ = 65;
        stan::model::assign(a_row, 1, "assigning variable a_row",
          stan::model::index_uni(cp_idx));
        current_statement__ = 66;
        cp_idx = (cp_idx + 1);
      }
      current_statement__ = 69;
      stan::model::assign(A, a_row, "assigning variable A",
        stan::model::index_uni(i));
    }
    current_statement__ = 72;
    return A;
  } catch (const std::exception& e) {
    stan::lang::rethrow_located(e, locations_array__[current_statement__]);
  }
}
// vector logistic_gamma(real, real, vector, vector, int)
template <typename T0__, typename T1__, typename T2__, typename T3__,
          typename T4__,
          stan::require_all_t<stan::math::disjunction<stan::is_autodiff<T0__>,
                                                      std::is_floating_point<T0__>>,
                              stan::math::disjunction<stan::is_autodiff<T1__>,
                                                      std::is_floating_point<T1__>>,
                              stan::is_col_vector<T2__>,
                              stan::is_vt_not_complex<T2__>,
                              stan::is_col_vector<T3__>,
                              stan::is_vt_not_complex<T3__>,
                              std::is_integral<T4__>>*>
Eigen::Matrix<stan::promote_args_t<T0__, T1__, stan::base_type_t<T2__>,
                stan::base_type_t<T3__>>,-1,1>
logistic_gamma(const T0__& k, const T1__& m, const T2__& delta_arg__,
               const T3__& t_change_arg__, const T4__& S, std::ostream*
               pstream__) {
  using local_scalar_t__ = stan::promote_args_t<T0__, T1__,
                             stan::base_type_t<T2__>,
                             stan::base_type_t<T3__>>;
  int current_statement__ = 0;
  // suppress unused var warning
  (void) current_statement__;
  const auto& delta = stan::math::to_ref(delta_arg__);
  const auto& t_change = stan::math::to_ref(t_change_arg__);
  static constexpr bool propto__ = true;
  // suppress unused var warning
  (void) propto__;
  local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
  // suppress unused var warning
  (void) DUMMY_VAR__;
  try {
    current_statement__ = 74;
    stan::math::validate_non_negative_index("gamma", "S", S);
    Eigen::Matrix<local_scalar_t__,-1,1> gamma =
      Eigen::Matrix<local_scalar_t__,-1,1>::Constant(S, DUMMY_VAR__);
    current_statement__ = 76;
    stan::math::validate_non_negative_index("k_s", "S + 1", (S + 1));
    Eigen::Matrix<local_scalar_t__,-1,1> k_s =
      Eigen::Matrix<local_scalar_t__,-1,1>::Constant((S + 1), DUMMY_VAR__);
    local_scalar_t__ m_pr = DUMMY_VAR__;
    current_statement__ = 79;
    stan::model::assign(k_s,
      stan::math::append_row(k,
        stan::math::add(k, stan::math::cumulative_sum(delta))),
      "assigning variable k_s");
    current_statement__ = 80;
    m_pr = m;
    current_statement__ = 84;
    for (int i = 1; i <= S; ++i) {
      current_statement__ = 81;
      stan::model::assign(gamma,
        ((stan::model::rvalue(t_change, "t_change", stan::model::index_uni(i))
        - m_pr) * (1 -
        (stan::model::rvalue(k_s, "k_s", stan::model::index_uni(i)) /
        stan::model::rvalue(k_s, "k_s", stan::model::index_uni((i + 1)))))),
        "assigning variable gamma", stan::model::index_uni(i));
      current_statement__ = 82;
      m_pr = (m_pr +
        stan::model::rvalue(gamma, "gamma", stan::model::index_uni(i)));
    }
    current_statement__ = 85;
    return gamma;
  } catch (const std::exception& e) {
    stan::lang::rethrow_located(e, locations_array__[current_statement__]);
  }
}
/* vector
   logistic_trend(real, real, vector, vector, vector, matrix, vector, int)
 */
template <typename T0__, typename T1__, typename T2__, typename T3__,
          typename T4__, typename T5__, typename T6__, typename T7__,
          stan::require_all_t<stan::math::disjunction<stan::is_autodiff<T0__>,
                                                      std::is_floating_point<T0__>>,
                              stan::math::disjunction<stan::is_autodiff<T1__>,
                                                      std::is_floating_point<T1__>>,
                              stan::is_col_vector<T2__>,
                              stan::is_vt_not_complex<T2__>,
                              stan::is_col_vector<T3__>,
                              stan::is_vt_not_complex<T3__>,
                              stan::is_col_vector<T4__>,
                              stan::is_vt_not_complex<T4__>,
                              stan::is_eigen_matrix_dynamic<T5__>,
                              stan::is_vt_not_complex<T5__>,
                              stan::is_col_vector<T6__>,
                              stan::is_vt_not_complex<T6__>,
                              std::is_integral<T7__>>*>
Eigen::Matrix<stan::promote_args_t<T0__, T1__, stan::base_type_t<T2__>,
                stan::base_type_t<T3__>, stan::base_type_t<T4__>,
                stan::promote_args_t<stan::base_type_t<T5__>,
                  stan::base_type_t<T6__>>>,-1,1>
logistic_trend(const T0__& k, const T1__& m, const T2__& delta_arg__,
               const T3__& t_arg__, const T4__& cap_arg__, const T5__&
               A_arg__, const T6__& t_change_arg__, const T7__& S,
               std::ostream* pstream__) {
  using local_scalar_t__ = stan::promote_args_t<T0__, T1__,
                             stan::base_type_t<T2__>,
                             stan::base_type_t<T3__>,
                             stan::base_type_t<T4__>,
                             stan::promote_args_t<stan::base_type_t<T5__>,
                               stan::base_type_t<T6__>>>;
  int current_statement__ = 0;
  // suppress unused var warning
  (void) current_statement__;
  const auto& delta = stan::math::to_ref(delta_arg__);
  const auto& t = stan::math::to_ref(t_arg__);
  const auto& cap = stan::math::to_ref(cap_arg__);
  const auto& A = stan::math::to_ref(A_arg__);
  const auto& t_change = stan::math::to_ref(t_change_arg__);
  static constexpr bool propto__ = true;
  // suppress unused var warning
  (void) propto__;
  local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
  // suppress unused var warning
  (void) DUMMY_VAR__;
  try {
    current_statement__ = 87;
    stan::math::validate_non_negative_index("gamma", "S", S);
    Eigen::Matrix<local_scalar_t__,-1,1> gamma =
      Eigen::Matrix<local_scalar_t__,-1,1>::Constant(S, DUMMY_VAR__);
    current_statement__ = 89;
    stan::model::assign(gamma,
      logistic_gamma(k, m, delta, t_change, S, pstream__),
      "assigning variable gamma");
    current_statement__ = 90;
    return stan::math::elt_multiply(cap,
             stan::math::inv_logit(
               stan::math::elt_multiply(
                 stan::math::add(k, stan::math::multiply(A, delta)),
                 stan::math::subtract(t,
                   stan::math::add(m, stan::math::multiply(A, gamma))))));
  } catch (const std::exception& e) {
    stan::lang::rethrow_located(e, locations_array__[current_statement__]);
  }
}
// vector linear_trend(real, real, vector, vector, matrix, vector)
template <typename T0__, typename T1__, typename T2__, typename T3__,
          typename T4__, typename T5__,
          stan::require_all_t<stan::math::disjunction<stan::is_autodiff<T0__>,
                                                      std::is_floating_point<T0__>>,
                              stan::math::disjunction<stan::is_autodiff<T1__>,
                                                      std::is_floating_point<T1__>>,
                              stan::is_col_vector<T2__>,
                              stan::is_vt_not_complex<T2__>,
                              stan::is_col_vector<T3__>,
                              stan::is_vt_not_complex<T3__>,
                              stan::is_eigen_matrix_dynamic<T4__>,
                              stan::is_vt_not_complex<T4__>,
                              stan::is_col_vector<T5__>,
                              stan::is_vt_not_complex<T5__>>*>
Eigen::Matrix<stan::promote_args_t<T0__, T1__, stan::base_type_t<T2__>,
                stan::base_type_t<T3__>, stan::base_type_t<T4__>,
                stan::promote_args_t<stan::base_type_t<T5__>>>,-1,1>
linear_trend(const T0__& k, const T1__& m, const T2__& delta_arg__,
             const T3__& t_arg__, const T4__& A_arg__, const T5__&
             t_change_arg__, std::ostream* pstream__) {
  using local_scalar_t__ = stan::promote_args_t<T0__, T1__,
                             stan::base_type_t<T2__>,
                             stan::base_type_t<T3__>,
                             stan::base_type_t<T4__>,
                             stan::promote_args_t<stan::base_type_t<T5__>>>;
  int current_statement__ = 0;
  // suppress unused var warning
  (void) current_statement__;
  const auto& delta = stan::math::to_ref(delta_arg__);
  const auto& t = stan::math::to_ref(t_arg__);
  const auto& A = stan::math::to_ref(A_arg__);
  const auto& t_change = stan::math::to_ref(t_change_arg__);
  static constexpr bool propto__ = true;
  // suppress unused var warning
  (void) propto__;
  local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
  // suppress unused var warning
  (void) DUMMY_VAR__;
  try {
    current_statement__ = 92;
    return stan::math::add(
             stan::math::elt_multiply(
               stan::math::add(k, stan::math::multiply(A, delta)), t),
             stan::math::add(m,
               stan::math::multiply(A,
                 stan::math::elt_multiply(stan::math::minus(t_change), delta))));
  } catch (const std::exception& e) {
    stan::lang::rethrow_located(e, locations_array__[current_statement__]);
  }
}
// vector flat_trend(real, int)
template <typename T0__, typename T1__,
          stan::require_all_t<stan::math::disjunction<stan::is_autodiff<T0__>,
                                                      std::is_floating_point<T0__>>,
                              std::is_integral<T1__>>*>
Eigen::Matrix<stan::promote_args_t<T0__>,-1,1>
flat_trend(const T0__& m, const T1__& T, std::ostream* pstream__) {
  using local_scalar_t__ = stan::promote_args_t<T0__>;
  int current_statement__ = 0;
  // suppress unused var warning
  (void) current_statement__;
  static constexpr bool propto__ = true;
  // suppress unused var warning
  (void) propto__;
  local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
  // suppress unused var warning
  (void) DUMMY_VAR__;
  try {
    current_statement__ = 94;
    return stan::math::rep_vector(m, T);
  } catch (const std::exception& e) {
    stan::lang::rethrow_located(e, locations_array__[current_statement__]);
  }
}
class prophet_model final : public model_base_crtp<prophet_model> {
 private:
  int T;
  int K;
  Eigen::Matrix<double,-1,1> t_data__;
  Eigen::Matrix<double,-1,1> cap_data__;
  Eigen::Matrix<double,-1,1> y_data__;
  int S;
  Eigen::Matrix<double,-1,1> t_change_data__;
  Eigen::Matrix<double,-1,-1> X_data__;
  Eigen::Matrix<double,-1,1> sigmas_data__;
  double tau;
  int trend_indicator;
  Eigen::Matrix<double,-1,1> s_a_data__;
  Eigen::Matrix<double,-1,1> s_m_data__;
  Eigen::Matrix<double,-1,-1> A_data__;
  Eigen::Matrix<double,-1,-1> X_sa_data__;
  Eigen::Matrix<double,-1,-1> X_sm_data__;
  Eigen::Map<Eigen::Matrix<double,-1,1>> t{nullptr, 0};
  Eigen::Map<Eigen::Matrix<double,-1,1>> cap{nullptr, 0};
  Eigen::Map<Eigen::Matrix<double,-1,1>> y{nullptr, 0};
  Eigen::Map<Eigen::Matrix<double,-1,1>> t_change{nullptr, 0};
  Eigen::Map<Eigen::Matrix<double,-1,-1>> X{nullptr, 0, 0};
  Eigen::Map<Eigen::Matrix<double,-1,1>> sigmas{nullptr, 0};
  Eigen::Map<Eigen::Matrix<double,-1,1>> s_a{nullptr, 0};
  Eigen::Map<Eigen::Matrix<double,-1,1>> s_m{nullptr, 0};
  Eigen::Map<Eigen::Matrix<double,-1,-1>> A{nullptr, 0, 0};
  Eigen::Map<Eigen::Matrix<double,-1,-1>> X_sa{nullptr, 0, 0};
  Eigen::Map<Eigen::Matrix<double,-1,-1>> X_sm{nullptr, 0, 0};
 public:
  ~prophet_model() {}
  prophet_model(stan::io::var_context& context__, unsigned int
                random_seed__ = 0, std::ostream* pstream__ = nullptr)
      : model_base_crtp(0) {
    int current_statement__ = 0;
    // suppress unused var warning
    (void) current_statement__;
    using local_scalar_t__ = double;
    boost::ecuyer1988 base_rng__ =
      stan::services::util::create_rng(random_seed__, 0);
    // suppress unused var warning
    (void) base_rng__;
    static constexpr const char* function__ =
      "prophet_model_namespace::prophet_model";
    // suppress unused var warning
    (void) function__;
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    // suppress unused var warning
    (void) DUMMY_VAR__;
    try {
      int pos__ = std::numeric_limits<int>::min();
      pos__ = 1;
      current_statement__ = 22;
      context__.validate_dims("data initialization", "T", "int",
        std::vector<size_t>{});
      T = std::numeric_limits<int>::min();
      current_statement__ = 22;
      T = context__.vals_i("T")[(1 - 1)];
      current_statement__ = 23;
      context__.validate_dims("data initialization", "K", "int",
        std::vector<size_t>{});
      K = std::numeric_limits<int>::min();
      current_statement__ = 23;
      K = context__.vals_i("K")[(1 - 1)];
      current_statement__ = 23;
      stan::math::check_greater_or_equal(function__, "K", K, 1);
      current_statement__ = 24;
      stan::math::validate_non_negative_index("t", "T", T);
      current_statement__ = 25;
      context__.validate_dims("data initialization", "t", "double",
        std::vector<size_t>{static_cast<size_t>(T)});
      t_data__ = Eigen::Matrix<double,-1,1>::Constant(T,
                   std::numeric_limits<double>::quiet_NaN());
      new (&t) Eigen::Map<Eigen::Matrix<double,-1,1>>(t_data__.data(), T);
      {
        std::vector<local_scalar_t__> t_flat__;
        current_statement__ = 25;
        t_flat__ = context__.vals_r("t");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= T; ++sym1__) {
          stan::model::assign(t, t_flat__[(pos__ - 1)],
            "assigning variable t", stan::model::index_uni(sym1__));
          pos__ = (pos__ + 1);
        }
      }
      current_statement__ = 26;
      stan::math::validate_non_negative_index("cap", "T", T);
      current_statement__ = 27;
      context__.validate_dims("data initialization", "cap", "double",
        std::vector<size_t>{static_cast<size_t>(T)});
      cap_data__ = Eigen::Matrix<double,-1,1>::Constant(T,
                     std::numeric_limits<double>::quiet_NaN());
      new (&cap) Eigen::Map<Eigen::Matrix<double,-1,1>>(cap_data__.data(), T);
      {
        std::vector<local_scalar_t__> cap_flat__;
        current_statement__ = 27;
        cap_flat__ = context__.vals_r("cap");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= T; ++sym1__) {
          stan::model::assign(cap, cap_flat__[(pos__ - 1)],
            "assigning variable cap", stan::model::index_uni(sym1__));
          pos__ = (pos__ + 1);
        }
      }
      current_statement__ = 28;
      stan::math::validate_non_negative_index("y", "T", T);
      current_statement__ = 29;
      context__.validate_dims("data initialization", "y", "double",
        std::vector<size_t>{static_cast<size_t>(T)});
      y_data__ = Eigen::Matrix<double,-1,1>::Constant(T,
                   std::numeric_limits<double>::quiet_NaN());
      new (&y) Eigen::Map<Eigen::Matrix<double,-1,1>>(y_data__.data(), T);
      {
        std::vector<local_scalar_t__> y_flat__;
        current_statement__ = 29;
        y_flat__ = context__.vals_r("y");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= T; ++sym1__) {
          stan::model::assign(y, y_flat__[(pos__ - 1)],
            "assigning variable y", stan::model::index_uni(sym1__));
          pos__ = (pos__ + 1);
        }
      }
      current_statement__ = 30;
      context__.validate_dims("data initialization", "S", "int",
        std::vector<size_t>{});
      S = std::numeric_limits<int>::min();
      current_statement__ = 30;
      S = context__.vals_i("S")[(1 - 1)];
      current_statement__ = 31;
      stan::math::validate_non_negative_index("t_change", "S", S);
      current_statement__ = 32;
      context__.validate_dims("data initialization", "t_change", "double",
        std::vector<size_t>{static_cast<size_t>(S)});
      t_change_data__ = Eigen::Matrix<double,-1,1>::Constant(S,
                          std::numeric_limits<double>::quiet_NaN());
      new (&t_change)
        Eigen::Map<Eigen::Matrix<double,-1,1>>(t_change_data__.data(), S);
      {
        std::vector<local_scalar_t__> t_change_flat__;
        current_statement__ = 32;
        t_change_flat__ = context__.vals_r("t_change");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= S; ++sym1__) {
          stan::model::assign(t_change, t_change_flat__[(pos__ - 1)],
            "assigning variable t_change", stan::model::index_uni(sym1__));
          pos__ = (pos__ + 1);
        }
      }
      current_statement__ = 33;
      stan::math::validate_non_negative_index("X", "T", T);
      current_statement__ = 34;
      stan::math::validate_non_negative_index("X", "K", K);
      current_statement__ = 35;
      context__.validate_dims("data initialization", "X", "double",
        std::vector<size_t>{static_cast<size_t>(T), static_cast<size_t>(K)});
      X_data__ = Eigen::Matrix<double,-1,-1>::Constant(T, K,
                   std::numeric_limits<double>::quiet_NaN());
      new (&X) Eigen::Map<Eigen::Matrix<double,-1,-1>>(X_data__.data(), T, K);
      {
        std::vector<local_scalar_t__> X_flat__;
        current_statement__ = 35;
        X_flat__ = context__.vals_r("X");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= K; ++sym1__) {
          for (int sym2__ = 1; sym2__ <= T; ++sym2__) {
            stan::model::assign(X, X_flat__[(pos__ - 1)],
              "assigning variable X", stan::model::index_uni(sym2__),
              stan::model::index_uni(sym1__));
            pos__ = (pos__ + 1);
          }
        }
      }
      current_statement__ = 36;
      stan::math::validate_non_negative_index("sigmas", "K", K);
      current_statement__ = 37;
      context__.validate_dims("data initialization", "sigmas", "double",
        std::vector<size_t>{static_cast<size_t>(K)});
      sigmas_data__ = Eigen::Matrix<double,-1,1>::Constant(K,
                        std::numeric_limits<double>::quiet_NaN());
      new (&sigmas)
        Eigen::Map<Eigen::Matrix<double,-1,1>>(sigmas_data__.data(), K);
      {
        std::vector<local_scalar_t__> sigmas_flat__;
        current_statement__ = 37;
        sigmas_flat__ = context__.vals_r("sigmas");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= K; ++sym1__) {
          stan::model::assign(sigmas, sigmas_flat__[(pos__ - 1)],
            "assigning variable sigmas", stan::model::index_uni(sym1__));
          pos__ = (pos__ + 1);
        }
      }
      current_statement__ = 38;
      context__.validate_dims("data initialization", "tau", "double",
        std::vector<size_t>{});
      tau = std::numeric_limits<double>::quiet_NaN();
      current_statement__ = 38;
      tau = context__.vals_r("tau")[(1 - 1)];
      current_statement__ = 38;
      stan::math::check_greater_or_equal(function__, "tau", tau, 0);
      current_statement__ = 39;
      context__.validate_dims("data initialization", "trend_indicator",
        "int", std::vector<size_t>{});
      trend_indicator = std::numeric_limits<int>::min();
      current_statement__ = 39;
      trend_indicator = context__.vals_i("trend_indicator")[(1 - 1)];
      current_statement__ = 40;
      stan::math::validate_non_negative_index("s_a", "K", K);
      current_statement__ = 41;
      context__.validate_dims("data initialization", "s_a", "double",
        std::vector<size_t>{static_cast<size_t>(K)});
      s_a_data__ = Eigen::Matrix<double,-1,1>::Constant(K,
                     std::numeric_limits<double>::quiet_NaN());
      new (&s_a) Eigen::Map<Eigen::Matrix<double,-1,1>>(s_a_data__.data(), K);
      {
        std::vector<local_scalar_t__> s_a_flat__;
        current_statement__ = 41;
        s_a_flat__ = context__.vals_r("s_a");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= K; ++sym1__) {
          stan::model::assign(s_a, s_a_flat__[(pos__ - 1)],
            "assigning variable s_a", stan::model::index_uni(sym1__));
          pos__ = (pos__ + 1);
        }
      }
      current_statement__ = 42;
      stan::math::validate_non_negative_index("s_m", "K", K);
      current_statement__ = 43;
      context__.validate_dims("data initialization", "s_m", "double",
        std::vector<size_t>{static_cast<size_t>(K)});
      s_m_data__ = Eigen::Matrix<double,-1,1>::Constant(K,
                     std::numeric_limits<double>::quiet_NaN());
      new (&s_m) Eigen::Map<Eigen::Matrix<double,-1,1>>(s_m_data__.data(), K);
      {
        std::vector<local_scalar_t__> s_m_flat__;
        current_statement__ = 43;
        s_m_flat__ = context__.vals_r("s_m");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= K; ++sym1__) {
          stan::model::assign(s_m, s_m_flat__[(pos__ - 1)],
            "assigning variable s_m", stan::model::index_uni(sym1__));
          pos__ = (pos__ + 1);
        }
      }
      current_statement__ = 44;
      stan::math::validate_non_negative_index("A", "T", T);
      current_statement__ = 45;
      stan::math::validate_non_negative_index("A", "S", S);
      current_statement__ = 46;
      A_data__ = Eigen::Matrix<double,-1,-1>::Constant(T, S,
                   std::numeric_limits<double>::quiet_NaN());
      new (&A) Eigen::Map<Eigen::Matrix<double,-1,-1>>(A_data__.data(), T, S);
      current_statement__ = 46;
      stan::model::assign(A,
        get_changepoint_matrix(t, t_change, T, S, pstream__),
        "assigning variable A");
      current_statement__ = 47;
      stan::math::validate_non_negative_index("X_sa", "T", T);
      current_statement__ = 48;
      stan::math::validate_non_negative_index("X_sa", "K", K);
      current_statement__ = 49;
      X_sa_data__ = Eigen::Matrix<double,-1,-1>::Constant(T, K,
                      std::numeric_limits<double>::quiet_NaN());
      new (&X_sa) Eigen::Map<Eigen::Matrix<double,-1,-1>>(X_sa_data__.data(),
        T, K);
      current_statement__ = 49;
      stan::model::assign(X_sa,
        stan::math::elt_multiply(X,
          stan::math::rep_matrix(stan::math::transpose(s_a), T)),
        "assigning variable X_sa");
      current_statement__ = 50;
      stan::math::validate_non_negative_index("X_sm", "T", T);
      current_statement__ = 51;
      stan::math::validate_non_negative_index("X_sm", "K", K);
      current_statement__ = 52;
      X_sm_data__ = Eigen::Matrix<double,-1,-1>::Constant(T, K,
                      std::numeric_limits<double>::quiet_NaN());
      new (&X_sm) Eigen::Map<Eigen::Matrix<double,-1,-1>>(X_sm_data__.data(),
        T, K);
      current_statement__ = 52;
      stan::model::assign(X_sm,
        stan::math::elt_multiply(X,
          stan::math::rep_matrix(stan::math::transpose(s_m), T)),
        "assigning variable X_sm");
      current_statement__ = 53;
      stan::math::validate_non_negative_index("delta", "S", S);
      current_statement__ = 54;
      stan::math::validate_non_negative_index("beta", "K", K);
      current_statement__ = 55;
      stan::math::validate_non_negative_index("trend", "T", T);
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
    }
    num_params_r__ = 1 + 1 + S + 1 + K;
  }
  inline std::string model_name() const final {
    return "prophet_model";
  }
  inline std::vector<std::string> model_compile_info() const noexcept {
    return std::vector<std::string>{"stanc_version = stanc3 v2.33.1",
             "stancflags = "};
  }
  // Base log prob
  template <bool propto__, bool jacobian__, typename VecR, typename VecI,
            stan::require_vector_like_t<VecR>* = nullptr,
            stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr,
            stan::require_not_st_var<VecR>* = nullptr>
  inline stan::scalar_type_t<VecR>
  log_prob_impl(VecR& params_r__, VecI& params_i__, std::ostream*
                pstream__ = nullptr) const {
    using T__ = stan::scalar_type_t<VecR>;
    using local_scalar_t__ = T__;
    T__ lp__(0.0);
    stan::math::accumulator<T__> lp_accum__;
    stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);
    int current_statement__ = 0;
    // suppress unused var warning
    (void) current_statement__;
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    // suppress unused var warning
    (void) DUMMY_VAR__;
    static constexpr const char* function__ =
      "prophet_model_namespace::log_prob";
    // suppress unused var warning
    (void) function__;
    try {
      local_scalar_t__ k = DUMMY_VAR__;
      current_statement__ = 1;
      k = in__.template read<local_scalar_t__>();
      local_scalar_t__ m = DUMMY_VAR__;
      current_statement__ = 2;
      m = in__.template read<local_scalar_t__>();
      Eigen::Matrix<local_scalar_t__,-1,1> delta =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(S, DUMMY_VAR__);
      current_statement__ = 3;
      delta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(S);
      local_scalar_t__ sigma_obs = DUMMY_VAR__;
      current_statement__ = 4;
      sigma_obs = in__.template read_constrain_lb<local_scalar_t__,
                    jacobian__>(0, lp__);
      Eigen::Matrix<local_scalar_t__,-1,1> beta =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(K, DUMMY_VAR__);
      current_statement__ = 5;
      beta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(K);
      Eigen::Matrix<local_scalar_t__,-1,1> trend =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(T, DUMMY_VAR__);
      current_statement__ = 15;
      if (stan::math::logical_eq(trend_indicator, 0)) {
        current_statement__ = 13;
        stan::model::assign(trend,
          linear_trend(k, m, delta, t, A, t_change, pstream__),
          "assigning variable trend");
      } else {
        current_statement__ = 12;
        if (stan::math::logical_eq(trend_indicator, 1)) {
          current_statement__ = 10;
          stan::model::assign(trend,
            logistic_trend(k, m, delta, t, cap, A, t_change, S, pstream__),
            "assigning variable trend");
        } else {
          current_statement__ = 9;
          if (stan::math::logical_eq(trend_indicator, 2)) {
            current_statement__ = 7;
            stan::model::assign(trend, flat_trend(m, T, pstream__),
              "assigning variable trend");
          }
        }
      }
      {
        current_statement__ = 16;
        lp_accum__.add(stan::math::normal_lpdf<propto__>(k, 0, 5));
        current_statement__ = 17;
        lp_accum__.add(stan::math::normal_lpdf<propto__>(m, 0, 5));
        current_statement__ = 18;
        lp_accum__.add(stan::math::double_exponential_lpdf<propto__>(delta,
                         0, tau));
        current_statement__ = 19;
        lp_accum__.add(stan::math::normal_lpdf<propto__>(sigma_obs, 0, 0.5));
        current_statement__ = 20;
        lp_accum__.add(stan::math::normal_lpdf<propto__>(beta, 0, sigmas));
        current_statement__ = 21;
        lp_accum__.add(stan::math::normal_id_glm_lpdf<propto__>(y, X_sa,
                         stan::math::elt_multiply(trend,
                           stan::math::add(1,
                             stan::math::multiply(X_sm, beta))), beta,
                         sigma_obs));
      }
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
    }
    lp_accum__.add(lp__);
    return lp_accum__.sum();
  }
  // Reverse mode autodiff log prob
  template <bool propto__, bool jacobian__, typename VecR, typename VecI,
            stan::require_vector_like_t<VecR>* = nullptr,
            stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr,
            stan::require_st_var<VecR>* = nullptr>
  inline stan::scalar_type_t<VecR>
  log_prob_impl(VecR& params_r__, VecI& params_i__, std::ostream*
                pstream__ = nullptr) const {
    using T__ = stan::scalar_type_t<VecR>;
    using local_scalar_t__ = T__;
    T__ lp__(0.0);
    stan::math::accumulator<T__> lp_accum__;
    stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);
    int current_statement__ = 0;
    // suppress unused var warning
    (void) current_statement__;
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    // suppress unused var warning
    (void) DUMMY_VAR__;
    static constexpr const char* function__ =
      "prophet_model_namespace::log_prob";
    // suppress unused var warning
    (void) function__;
    try {
      local_scalar_t__ k = DUMMY_VAR__;
      current_statement__ = 1;
      k = in__.template read<local_scalar_t__>();
      local_scalar_t__ m = DUMMY_VAR__;
      current_statement__ = 2;
      m = in__.template read<local_scalar_t__>();
      Eigen::Matrix<local_scalar_t__,-1,1> delta =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(S, DUMMY_VAR__);
      current_statement__ = 3;
      delta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(S);
      local_scalar_t__ sigma_obs = DUMMY_VAR__;
      current_statement__ = 4;
      sigma_obs = in__.template read_constrain_lb<local_scalar_t__,
                    jacobian__>(0, lp__);
      Eigen::Matrix<local_scalar_t__,-1,1> beta =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(K, DUMMY_VAR__);
      current_statement__ = 5;
      beta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(K);
      Eigen::Matrix<local_scalar_t__,-1,1> trend =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(T, DUMMY_VAR__);
      current_statement__ = 15;
      if (stan::math::logical_eq(trend_indicator, 0)) {
        current_statement__ = 13;
        stan::model::assign(trend,
          linear_trend(k, m, delta, t, A, t_change, pstream__),
          "assigning variable trend");
      } else {
        current_statement__ = 12;
        if (stan::math::logical_eq(trend_indicator, 1)) {
          current_statement__ = 10;
          stan::model::assign(trend,
            logistic_trend(k, m, delta, t, cap, A, t_change, S, pstream__),
            "assigning variable trend");
        } else {
          current_statement__ = 9;
          if (stan::math::logical_eq(trend_indicator, 2)) {
            current_statement__ = 7;
            stan::model::assign(trend, flat_trend(m, T, pstream__),
              "assigning variable trend");
          }
        }
      }
      {
        current_statement__ = 16;
        lp_accum__.add(stan::math::normal_lpdf<propto__>(k, 0, 5));
        current_statement__ = 17;
        lp_accum__.add(stan::math::normal_lpdf<propto__>(m, 0, 5));
        current_statement__ = 18;
        lp_accum__.add(stan::math::double_exponential_lpdf<propto__>(delta,
                         0, tau));
        current_statement__ = 19;
        lp_accum__.add(stan::math::normal_lpdf<propto__>(sigma_obs, 0, 0.5));
        current_statement__ = 20;
        lp_accum__.add(stan::math::normal_lpdf<propto__>(beta, 0, sigmas));
        current_statement__ = 21;
        lp_accum__.add(stan::math::normal_id_glm_lpdf<propto__>(y, X_sa,
                         stan::math::elt_multiply(trend,
                           stan::math::add(1,
                             stan::math::multiply(X_sm, beta))), beta,
                         sigma_obs));
      }
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
    }
    lp_accum__.add(lp__);
    return lp_accum__.sum();
  }
  template <typename RNG, typename VecR, typename VecI, typename VecVar,
            stan::require_vector_like_vt<std::is_floating_point,
            VecR>* = nullptr, stan::require_vector_like_vt<std::is_integral,
            VecI>* = nullptr, stan::require_vector_vt<std::is_floating_point,
            VecVar>* = nullptr>
  inline void
  write_array_impl(RNG& base_rng__, VecR& params_r__, VecI& params_i__,
                   VecVar& vars__, const bool
                   emit_transformed_parameters__ = true, const bool
                   emit_generated_quantities__ = true, std::ostream*
                   pstream__ = nullptr) const {
    using local_scalar_t__ = double;
    stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);
    stan::io::serializer<local_scalar_t__> out__(vars__);
    static constexpr bool propto__ = true;
    // suppress unused var warning
    (void) propto__;
    double lp__ = 0.0;
    // suppress unused var warning
    (void) lp__;
    int current_statement__ = 0;
    // suppress unused var warning
    (void) current_statement__;
    stan::math::accumulator<double> lp_accum__;
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    // suppress unused var warning
    (void) DUMMY_VAR__;
    constexpr bool jacobian__ = false;
    // suppress unused var warning
    (void) jacobian__;
    static constexpr const char* function__ =
      "prophet_model_namespace::write_array";
    // suppress unused var warning
    (void) function__;
    try {
      double k = std::numeric_limits<double>::quiet_NaN();
      current_statement__ = 1;
      k = in__.template read<local_scalar_t__>();
      double m = std::numeric_limits<double>::quiet_NaN();
      current_statement__ = 2;
      m = in__.template read<local_scalar_t__>();
      Eigen::Matrix<double,-1,1> delta =
        Eigen::Matrix<double,-1,1>::Constant(S,
          std::numeric_limits<double>::quiet_NaN());
      current_statement__ = 3;
      delta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(S);
      double sigma_obs = std::numeric_limits<double>::quiet_NaN();
      current_statement__ = 4;
      sigma_obs = in__.template read_constrain_lb<local_scalar_t__,
                    jacobian__>(0, lp__);
      Eigen::Matrix<double,-1,1> beta =
        Eigen::Matrix<double,-1,1>::Constant(K,
          std::numeric_limits<double>::quiet_NaN());
      current_statement__ = 5;
      beta = in__.template read<Eigen::Matrix<local_scalar_t__,-1,1>>(K);
      Eigen::Matrix<double,-1,1> trend =
        Eigen::Matrix<double,-1,1>::Constant(T,
          std::numeric_limits<double>::quiet_NaN());
      out__.write(k);
      out__.write(m);
      out__.write(delta);
      out__.write(sigma_obs);
      out__.write(beta);
      if (stan::math::logical_negation(
            (stan::math::primitive_value(emit_transformed_parameters__) ||
            stan::math::primitive_value(emit_generated_quantities__)))) {
        return ;
      }
      current_statement__ = 15;
      if (stan::math::logical_eq(trend_indicator, 0)) {
        current_statement__ = 13;
        stan::model::assign(trend,
          linear_trend(k, m, delta, t, A, t_change, pstream__),
          "assigning variable trend");
      } else {
        current_statement__ = 12;
        if (stan::math::logical_eq(trend_indicator, 1)) {
          current_statement__ = 10;
          stan::model::assign(trend,
            logistic_trend(k, m, delta, t, cap, A, t_change, S, pstream__),
            "assigning variable trend");
        } else {
          current_statement__ = 9;
          if (stan::math::logical_eq(trend_indicator, 2)) {
            current_statement__ = 7;
            stan::model::assign(trend, flat_trend(m, T, pstream__),
              "assigning variable trend");
          }
        }
      }
      if (emit_transformed_parameters__) {
        out__.write(trend);
      }
      if (stan::math::logical_negation(emit_generated_quantities__)) {
        return ;
      }
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
    }
  }
  template <typename VecVar, typename VecI,
            stan::require_vector_t<VecVar>* = nullptr,
            stan::require_vector_like_vt<std::is_integral, VecI>* = nullptr>
  inline void
  unconstrain_array_impl(const VecVar& params_r__, const VecI& params_i__,
                         VecVar& vars__, std::ostream* pstream__ = nullptr) const {
    using local_scalar_t__ = double;
    stan::io::deserializer<local_scalar_t__> in__(params_r__, params_i__);
    stan::io::serializer<local_scalar_t__> out__(vars__);
    int current_statement__ = 0;
    // suppress unused var warning
    (void) current_statement__;
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    // suppress unused var warning
    (void) DUMMY_VAR__;
    try {
      local_scalar_t__ k = DUMMY_VAR__;
      current_statement__ = 1;
      k = in__.read<local_scalar_t__>();
      out__.write(k);
      local_scalar_t__ m = DUMMY_VAR__;
      current_statement__ = 2;
      m = in__.read<local_scalar_t__>();
      out__.write(m);
      Eigen::Matrix<local_scalar_t__,-1,1> delta =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(S, DUMMY_VAR__);
      current_statement__ = 3;
      stan::model::assign(delta,
        in__.read<Eigen::Matrix<local_scalar_t__,-1,1>>(S),
        "assigning variable delta");
      out__.write(delta);
      local_scalar_t__ sigma_obs = DUMMY_VAR__;
      current_statement__ = 4;
      sigma_obs = in__.read<local_scalar_t__>();
      out__.write_free_lb(0, sigma_obs);
      Eigen::Matrix<local_scalar_t__,-1,1> beta =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(K, DUMMY_VAR__);
      current_statement__ = 5;
      stan::model::assign(beta,
        in__.read<Eigen::Matrix<local_scalar_t__,-1,1>>(K),
        "assigning variable beta");
      out__.write(beta);
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
    }
  }
  template <typename VecVar, stan::require_vector_t<VecVar>* = nullptr>
  inline void
  transform_inits_impl(const stan::io::var_context& context__, VecVar&
                       vars__, std::ostream* pstream__ = nullptr) const {
    using local_scalar_t__ = double;
    stan::io::serializer<local_scalar_t__> out__(vars__);
    int current_statement__ = 0;
    // suppress unused var warning
    (void) current_statement__;
    local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
    // suppress unused var warning
    (void) DUMMY_VAR__;
    try {
      current_statement__ = 1;
      context__.validate_dims("parameter initialization", "k", "double",
        std::vector<size_t>{});
      current_statement__ = 2;
      context__.validate_dims("parameter initialization", "m", "double",
        std::vector<size_t>{});
      current_statement__ = 3;
      context__.validate_dims("parameter initialization", "delta", "double",
        std::vector<size_t>{static_cast<size_t>(S)});
      current_statement__ = 4;
      context__.validate_dims("parameter initialization", "sigma_obs",
        "double", std::vector<size_t>{});
      current_statement__ = 5;
      context__.validate_dims("parameter initialization", "beta", "double",
        std::vector<size_t>{static_cast<size_t>(K)});
      int pos__ = std::numeric_limits<int>::min();
      pos__ = 1;
      local_scalar_t__ k = DUMMY_VAR__;
      current_statement__ = 1;
      k = context__.vals_r("k")[(1 - 1)];
      out__.write(k);
      local_scalar_t__ m = DUMMY_VAR__;
      current_statement__ = 2;
      m = context__.vals_r("m")[(1 - 1)];
      out__.write(m);
      Eigen::Matrix<local_scalar_t__,-1,1> delta =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(S, DUMMY_VAR__);
      {
        std::vector<local_scalar_t__> delta_flat__;
        current_statement__ = 3;
        delta_flat__ = context__.vals_r("delta");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= S; ++sym1__) {
          stan::model::assign(delta, delta_flat__[(pos__ - 1)],
            "assigning variable delta", stan::model::index_uni(sym1__));
          pos__ = (pos__ + 1);
        }
      }
      out__.write(delta);
      local_scalar_t__ sigma_obs = DUMMY_VAR__;
      current_statement__ = 4;
      sigma_obs = context__.vals_r("sigma_obs")[(1 - 1)];
      out__.write_free_lb(0, sigma_obs);
      Eigen::Matrix<local_scalar_t__,-1,1> beta =
        Eigen::Matrix<local_scalar_t__,-1,1>::Constant(K, DUMMY_VAR__);
      {
        std::vector<local_scalar_t__> beta_flat__;
        current_statement__ = 5;
        beta_flat__ = context__.vals_r("beta");
        pos__ = 1;
        for (int sym1__ = 1; sym1__ <= K; ++sym1__) {
          stan::model::assign(beta, beta_flat__[(pos__ - 1)],
            "assigning variable beta", stan::model::index_uni(sym1__));
          pos__ = (pos__ + 1);
        }
      }
      out__.write(beta);
    } catch (const std::exception& e) {
      stan::lang::rethrow_located(e, locations_array__[current_statement__]);
    }
  }
  inline void
  get_param_names(std::vector<std::string>& names__, const bool
                  emit_transformed_parameters__ = true, const bool
                  emit_generated_quantities__ = true) const {
    names__ = std::vector<std::string>{"k", "m", "delta", "sigma_obs",
                "beta"};
    if (emit_transformed_parameters__) {
      std::vector<std::string> temp{"trend"};
      names__.reserve(names__.size() + temp.size());
      names__.insert(names__.end(), temp.begin(), temp.end());
    }
    if (emit_generated_quantities__) {}
  }
  inline void
  get_dims(std::vector<std::vector<size_t>>& dimss__, const bool
           emit_transformed_parameters__ = true, const bool
           emit_generated_quantities__ = true) const {
    dimss__ = std::vector<std::vector<size_t>>{std::vector<size_t>{},
                std::vector<size_t>{},
                std::vector<size_t>{static_cast<size_t>(S)},
                std::vector<size_t>{},
                std::vector<size_t>{static_cast<size_t>(K)}};
    if (emit_transformed_parameters__) {
      std::vector<std::vector<size_t>>
        temp{std::vector<size_t>{static_cast<size_t>(T)}};
      dimss__.reserve(dimss__.size() + temp.size());
      dimss__.insert(dimss__.end(), temp.begin(), temp.end());
    }
    if (emit_generated_quantities__) {}
  }
  inline void
  constrained_param_names(std::vector<std::string>& param_names__, bool
                          emit_transformed_parameters__ = true, bool
                          emit_generated_quantities__ = true) const final {
    param_names__.emplace_back(std::string() + "k");
    param_names__.emplace_back(std::string() + "m");
    for (int sym1__ = 1; sym1__ <= S; ++sym1__) {
      param_names__.emplace_back(std::string() + "delta" + '.' +
        std::to_string(sym1__));
    }
    param_names__.emplace_back(std::string() + "sigma_obs");
    for (int sym1__ = 1; sym1__ <= K; ++sym1__) {
      param_names__.emplace_back(std::string() + "beta" + '.' +
        std::to_string(sym1__));
    }
    if (emit_transformed_parameters__) {
      for (int sym1__ = 1; sym1__ <= T; ++sym1__) {
        param_names__.emplace_back(std::string() + "trend" + '.' +
          std::to_string(sym1__));
      }
    }
    if (emit_generated_quantities__) {}
  }
  inline void
  unconstrained_param_names(std::vector<std::string>& param_names__, bool
                            emit_transformed_parameters__ = true, bool
                            emit_generated_quantities__ = true) const final {
    param_names__.emplace_back(std::string() + "k");
    param_names__.emplace_back(std::string() + "m");
    for (int sym1__ = 1; sym1__ <= S; ++sym1__) {
      param_names__.emplace_back(std::string() + "delta" + '.' +
        std::to_string(sym1__));
    }
    param_names__.emplace_back(std::string() + "sigma_obs");
    for (int sym1__ = 1; sym1__ <= K; ++sym1__) {
      param_names__.emplace_back(std::string() + "beta" + '.' +
        std::to_string(sym1__));
    }
    if (emit_transformed_parameters__) {
      for (int sym1__ = 1; sym1__ <= T; ++sym1__) {
        param_names__.emplace_back(std::string() + "trend" + '.' +
          std::to_string(sym1__));
      }
    }
    if (emit_generated_quantities__) {}
  }
  inline std::string get_constrained_sizedtypes() const {
    return std::string("[{\"name\":\"k\",\"type\":{\"name\":\"real\"},\"block\":\"parameters\"},{\"name\":\"m\",\"type\":{\"name\":\"real\"},\"block\":\"parameters\"},{\"name\":\"delta\",\"type\":{\"name\":\"vector\",\"length\":" + std::to_string(S) + "},\"block\":\"parameters\"},{\"name\":\"sigma_obs\",\"type\":{\"name\":\"real\"},\"block\":\"parameters\"},{\"name\":\"beta\",\"type\":{\"name\":\"vector\",\"length\":" + std::to_string(K) + "},\"block\":\"parameters\"},{\"name\":\"trend\",\"type\":{\"name\":\"vector\",\"length\":" + std::to_string(T) + "},\"block\":\"transformed_parameters\"}]");
  }
  inline std::string get_unconstrained_sizedtypes() const {
    return std::string("[{\"name\":\"k\",\"type\":{\"name\":\"real\"},\"block\":\"parameters\"},{\"name\":\"m\",\"type\":{\"name\":\"real\"},\"block\":\"parameters\"},{\"name\":\"delta\",\"type\":{\"name\":\"vector\",\"length\":" + std::to_string(S) + "},\"block\":\"parameters\"},{\"name\":\"sigma_obs\",\"type\":{\"name\":\"real\"},\"block\":\"parameters\"},{\"name\":\"beta\",\"type\":{\"name\":\"vector\",\"length\":" + std::to_string(K) + "},\"block\":\"parameters\"},{\"name\":\"trend\",\"type\":{\"name\":\"vector\",\"length\":" + std::to_string(T) + "},\"block\":\"transformed_parameters\"}]");
  }
  // Begin method overload boilerplate
  template <typename RNG> inline void
  write_array(RNG& base_rng, Eigen::Matrix<double,-1,1>& params_r,
              Eigen::Matrix<double,-1,1>& vars, const bool
              emit_transformed_parameters = true, const bool
              emit_generated_quantities = true, std::ostream*
              pstream = nullptr) const {
    const size_t num_params__ = ((((1 + 1) + S) + 1) + K);
    const size_t num_transformed = emit_transformed_parameters * (T);
    const size_t num_gen_quantities = emit_generated_quantities * (0);
    const size_t num_to_write = num_params__ + num_transformed +
      num_gen_quantities;
    std::vector<int> params_i;
    vars = Eigen::Matrix<double,-1,1>::Constant(num_to_write,
             std::numeric_limits<double>::quiet_NaN());
    write_array_impl(base_rng, params_r, params_i, vars,
      emit_transformed_parameters, emit_generated_quantities, pstream);
  }
  template <typename RNG> inline void
  write_array(RNG& base_rng, std::vector<double>& params_r, std::vector<int>&
              params_i, std::vector<double>& vars, bool
              emit_transformed_parameters = true, bool
              emit_generated_quantities = true, std::ostream*
              pstream = nullptr) const {
    const size_t num_params__ = ((((1 + 1) + S) + 1) + K);
    const size_t num_transformed = emit_transformed_parameters * (T);
    const size_t num_gen_quantities = emit_generated_quantities * (0);
    const size_t num_to_write = num_params__ + num_transformed +
      num_gen_quantities;
    vars = std::vector<double>(num_to_write,
             std::numeric_limits<double>::quiet_NaN());
    write_array_impl(base_rng, params_r, params_i, vars,
      emit_transformed_parameters, emit_generated_quantities, pstream);
  }
  template <bool propto__, bool jacobian__, typename T_> inline T_
  log_prob(Eigen::Matrix<T_,-1,1>& params_r, std::ostream* pstream = nullptr) const {
    Eigen::Matrix<int,-1,1> params_i;
    return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);
  }
  template <bool propto__, bool jacobian__, typename T_> inline T_
  log_prob(std::vector<T_>& params_r, std::vector<int>& params_i,
           std::ostream* pstream = nullptr) const {
    return log_prob_impl<propto__, jacobian__>(params_r, params_i, pstream);
  }
  inline void
  transform_inits(const stan::io::var_context& context,
                  Eigen::Matrix<double,-1,1>& params_r, std::ostream*
                  pstream = nullptr) const final {
    std::vector<double> params_r_vec(params_r.size());
    std::vector<int> params_i;
    transform_inits(context, params_i, params_r_vec, pstream);
    params_r = Eigen::Map<Eigen::Matrix<double,-1,1>>(params_r_vec.data(),
                 params_r_vec.size());
  }
  inline void
  transform_inits(const stan::io::var_context& context, std::vector<int>&
                  params_i, std::vector<double>& vars, std::ostream*
                  pstream__ = nullptr) const {
    vars.resize(num_params_r__);
    transform_inits_impl(context, vars, pstream__);
  }
  inline void
  unconstrain_array(const std::vector<double>& params_constrained,
                    std::vector<double>& params_unconstrained, std::ostream*
                    pstream = nullptr) const {
    const std::vector<int> params_i;
    params_unconstrained = std::vector<double>(num_params_r__,
                             std::numeric_limits<double>::quiet_NaN());
    unconstrain_array_impl(params_constrained, params_i,
      params_unconstrained, pstream);
  }
  inline void
  unconstrain_array(const Eigen::Matrix<double,-1,1>& params_constrained,
                    Eigen::Matrix<double,-1,1>& params_unconstrained,
                    std::ostream* pstream = nullptr) const {
    const std::vector<int> params_i;
    params_unconstrained = Eigen::Matrix<double,-1,1>::Constant(num_params_r__,
                             std::numeric_limits<double>::quiet_NaN());
    unconstrain_array_impl(params_constrained, params_i,
      params_unconstrained, pstream);
  }
};
}
using stan_model = prophet_model_namespace::prophet_model;
#ifndef USING_R
// Boilerplate
stan::model::model_base&
new_model(stan::io::var_context& data_context, unsigned int seed,
          std::ostream* msg_stream) {
  stan_model* m = new stan_model(data_context, seed, msg_stream);
  return *m;
}
stan::math::profile_map& get_stan_profile_data() {
  return prophet_model_namespace::profiles__;
}
#endif