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stanExports_master.h
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// Generated by rstantools. Do not edit by hand.
/*
geoBAMr is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
geoBAMr is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with geoBAMr. If not, see <http://www.gnu.org/licenses/>.
*/
#ifndef MODELS_HPP
#define MODELS_HPP
#define STAN__SERVICES__COMMAND_HPP
#include <rstan/rstaninc.hpp>
// Code generated by Stan version 2.21.0
#include <stan/model/model_header.hpp>
namespace model_master_namespace {
using std::istream;
using std::string;
using std::stringstream;
using std::vector;
using stan::io::dump;
using stan::math::lgamma;
using stan::model::prob_grad;
using namespace stan::math;
static int current_statement_begin__;
stan::io::program_reader prog_reader__() {
stan::io::program_reader reader;
reader.add_event(0, 0, "start", "model_master");
reader.add_event(320, 318, "end", "model_master");
return reader;
}
template <typename T0__>
Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic, 1>
ragged_vec(const std::vector<Eigen::Matrix<T0__, Eigen::Dynamic, 1> >& x,
const std::vector<std::vector<int> >& bin, std::ostream* pstream__) {
typedef typename boost::math::tools::promote_args<T0__>::type local_scalar_t__;
typedef local_scalar_t__ fun_return_scalar_t__;
const static bool propto__ = true;
(void) propto__;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
(void) DUMMY_VAR__; // suppress unused var warning
int current_statement_begin__ = -1;
try {
{
current_statement_begin__ = 9;
validate_non_negative_index("out", "num_elements(x)", num_elements(x));
Eigen::Matrix<local_scalar_t__, Eigen::Dynamic, 1> out(num_elements(x));
stan::math::initialize(out, DUMMY_VAR__);
stan::math::fill(out, DUMMY_VAR__);
current_statement_begin__ = 10;
int ind(0);
(void) ind; // dummy to suppress unused var warning
stan::math::fill(ind, std::numeric_limits<int>::min());
current_statement_begin__ = 12;
stan::math::assign(ind, 1);
current_statement_begin__ = 13;
for (int i = 1; i <= size(x); ++i) {
current_statement_begin__ = 14;
for (int t = 1; t <= num_elements(get_base1(x, 1, "x", 1)); ++t) {
current_statement_begin__ = 15;
if (as_bool(logical_eq(get_base1(get_base1(bin, i, "bin", 1), t, "bin", 2), 1))) {
current_statement_begin__ = 16;
stan::model::assign(out,
stan::model::cons_list(stan::model::index_uni(ind), stan::model::nil_index_list()),
get_base1(get_base1(x, i, "x", 1), t, "x", 2),
"assigning variable out");
current_statement_begin__ = 17;
stan::math::assign(ind, (ind + 1));
}
}
}
current_statement_begin__ = 23;
return stan::math::promote_scalar<fun_return_scalar_t__>(stan::model::rvalue(out, stan::model::cons_list(stan::model::index_min_max(1, (ind - 1)), stan::model::nil_index_list()), "out"));
}
} catch (const std::exception& e) {
stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
// Next line prevents compiler griping about no return
throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
}
}
struct ragged_vec_functor__ {
template <typename T0__>
Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic, 1>
operator()(const std::vector<Eigen::Matrix<T0__, Eigen::Dynamic, 1> >& x,
const std::vector<std::vector<int> >& bin, std::ostream* pstream__) const {
return ragged_vec(x, bin, pstream__);
}
};
template <typename T0__>
Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic, 1>
ragged_row(const Eigen::Matrix<T0__, Eigen::Dynamic, 1>& x,
const std::vector<std::vector<int> >& bin, std::ostream* pstream__) {
typedef typename boost::math::tools::promote_args<T0__>::type local_scalar_t__;
typedef local_scalar_t__ fun_return_scalar_t__;
const static bool propto__ = true;
(void) propto__;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
(void) DUMMY_VAR__; // suppress unused var warning
int current_statement_begin__ = -1;
try {
{
current_statement_begin__ = 28;
validate_non_negative_index("out", "num_elements(bin)", num_elements(bin));
Eigen::Matrix<local_scalar_t__, Eigen::Dynamic, 1> out(num_elements(bin));
stan::math::initialize(out, DUMMY_VAR__);
stan::math::fill(out, DUMMY_VAR__);
current_statement_begin__ = 29;
int ind(0);
(void) ind; // dummy to suppress unused var warning
stan::math::fill(ind, std::numeric_limits<int>::min());
current_statement_begin__ = 31;
stan::math::assign(ind, 0);
current_statement_begin__ = 32;
for (int i = 1; i <= size(bin); ++i) {
current_statement_begin__ = 33;
for (int t = 1; t <= num_elements(get_base1(bin, 1, "bin", 1)); ++t) {
current_statement_begin__ = 34;
if (as_bool(logical_eq(get_base1(get_base1(bin, i, "bin", 1), t, "bin", 2), 1))) {
current_statement_begin__ = 35;
stan::math::assign(ind, (ind + 1));
current_statement_begin__ = 36;
stan::model::assign(out,
stan::model::cons_list(stan::model::index_uni(ind), stan::model::nil_index_list()),
get_base1(x, t, "x", 1),
"assigning variable out");
}
}
}
current_statement_begin__ = 40;
return stan::math::promote_scalar<fun_return_scalar_t__>(stan::model::rvalue(out, stan::model::cons_list(stan::model::index_min_max(1, ind), stan::model::nil_index_list()), "out"));
}
} catch (const std::exception& e) {
stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
// Next line prevents compiler griping about no return
throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
}
}
struct ragged_row_functor__ {
template <typename T0__>
Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic, 1>
operator()(const Eigen::Matrix<T0__, Eigen::Dynamic, 1>& x,
const std::vector<std::vector<int> >& bin, std::ostream* pstream__) const {
return ragged_row(x, bin, pstream__);
}
};
template <typename T0__>
Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic, 1>
ragged_col(const Eigen::Matrix<T0__, Eigen::Dynamic, 1>& x,
const std::vector<std::vector<int> >& bin, std::ostream* pstream__) {
typedef typename boost::math::tools::promote_args<T0__>::type local_scalar_t__;
typedef local_scalar_t__ fun_return_scalar_t__;
const static bool propto__ = true;
(void) propto__;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
(void) DUMMY_VAR__; // suppress unused var warning
int current_statement_begin__ = -1;
try {
{
current_statement_begin__ = 46;
validate_non_negative_index("out", "num_elements(bin)", num_elements(bin));
Eigen::Matrix<local_scalar_t__, Eigen::Dynamic, 1> out(num_elements(bin));
stan::math::initialize(out, DUMMY_VAR__);
stan::math::fill(out, DUMMY_VAR__);
current_statement_begin__ = 47;
int ind(0);
(void) ind; // dummy to suppress unused var warning
stan::math::fill(ind, std::numeric_limits<int>::min());
current_statement_begin__ = 49;
stan::math::assign(ind, 0);
current_statement_begin__ = 50;
for (int i = 1; i <= size(bin); ++i) {
current_statement_begin__ = 51;
for (int t = 1; t <= num_elements(get_base1(bin, 1, "bin", 1)); ++t) {
current_statement_begin__ = 52;
if (as_bool(logical_eq(get_base1(get_base1(bin, i, "bin", 1), t, "bin", 2), 1))) {
current_statement_begin__ = 53;
stan::math::assign(ind, (ind + 1));
current_statement_begin__ = 54;
stan::model::assign(out,
stan::model::cons_list(stan::model::index_uni(ind), stan::model::nil_index_list()),
get_base1(x, i, "x", 1),
"assigning variable out");
}
}
}
current_statement_begin__ = 58;
return stan::math::promote_scalar<fun_return_scalar_t__>(stan::model::rvalue(out, stan::model::cons_list(stan::model::index_min_max(1, ind), stan::model::nil_index_list()), "out"));
}
} catch (const std::exception& e) {
stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
// Next line prevents compiler griping about no return
throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
}
}
struct ragged_col_functor__ {
template <typename T0__>
Eigen::Matrix<typename boost::math::tools::promote_args<T0__>::type, Eigen::Dynamic, 1>
operator()(const Eigen::Matrix<T0__, Eigen::Dynamic, 1>& x,
const std::vector<std::vector<int> >& bin, std::ostream* pstream__) const {
return ragged_col(x, bin, pstream__);
}
};
std::vector<int>
commoninds(const std::vector<std::vector<int> >& bin1,
const std::vector<std::vector<int> >& bin2, std::ostream* pstream__) {
typedef double local_scalar_t__;
typedef int fun_return_scalar_t__;
const static bool propto__ = true;
(void) propto__;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
(void) DUMMY_VAR__; // suppress unused var warning
int current_statement_begin__ = -1;
try {
{
current_statement_begin__ = 64;
validate_non_negative_index("out", "num_elements(bin1)", num_elements(bin1));
std::vector<int > out(num_elements(bin1), int(0));
stan::math::fill(out, std::numeric_limits<int>::min());
current_statement_begin__ = 65;
validate_non_negative_index("vecinds", "size(bin1)", size(bin1));
validate_non_negative_index("vecinds", "num_elements(get_base1(bin1, 1, \"bin1\", 1))", num_elements(get_base1(bin1, 1, "bin1", 1)));
std::vector<std::vector<int > > vecinds(size(bin1), std::vector<int>(num_elements(get_base1(bin1, 1, "bin1", 1)), int(0)));
stan::math::fill(vecinds, std::numeric_limits<int>::min());
current_statement_begin__ = 66;
int ctr(0);
(void) ctr; // dummy to suppress unused var warning
stan::math::fill(ctr, std::numeric_limits<int>::min());
current_statement_begin__ = 67;
int ind(0);
(void) ind; // dummy to suppress unused var warning
stan::math::fill(ind, std::numeric_limits<int>::min());
current_statement_begin__ = 69;
stan::math::assign(ctr, 0);
current_statement_begin__ = 70;
for (int i = 1; i <= size(bin1); ++i) {
current_statement_begin__ = 71;
for (int t = 1; t <= num_elements(get_base1(bin1, 1, "bin1", 1)); ++t) {
current_statement_begin__ = 72;
if (as_bool(logical_eq(get_base1(get_base1(bin1, i, "bin1", 1), t, "bin1", 2), 1))) {
current_statement_begin__ = 73;
stan::math::assign(ctr, (ctr + 1));
current_statement_begin__ = 74;
stan::model::assign(vecinds,
stan::model::cons_list(stan::model::index_uni(i), stan::model::cons_list(stan::model::index_uni(t), stan::model::nil_index_list())),
ctr,
"assigning variable vecinds");
}
}
}
current_statement_begin__ = 79;
stan::math::assign(ind, 0);
current_statement_begin__ = 80;
for (int i = 1; i <= size(vecinds); ++i) {
current_statement_begin__ = 81;
for (int t = 1; t <= size(get_base1(vecinds, 1, "vecinds", 1)); ++t) {
current_statement_begin__ = 82;
if (as_bool(logical_eq(get_base1(get_base1(bin2, i, "bin2", 1), t, "bin2", 2), 1))) {
current_statement_begin__ = 83;
stan::math::assign(ind, (ind + 1));
current_statement_begin__ = 84;
stan::model::assign(out,
stan::model::cons_list(stan::model::index_uni(ind), stan::model::nil_index_list()),
get_base1(get_base1(vecinds, i, "vecinds", 1), t, "vecinds", 2),
"assigning variable out");
}
}
}
current_statement_begin__ = 88;
return stan::math::promote_scalar<fun_return_scalar_t__>(stan::model::rvalue(out, stan::model::cons_list(stan::model::index_min_max(1, ind), stan::model::nil_index_list()), "out"));
}
} catch (const std::exception& e) {
stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
// Next line prevents compiler griping about no return
throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
}
}
struct commoninds_functor__ {
std::vector<int>
operator()(const std::vector<std::vector<int> >& bin1,
const std::vector<std::vector<int> >& bin2, std::ostream* pstream__) const {
return commoninds(bin1, bin2, pstream__);
}
};
#include <stan_meta_header.hpp>
class model_master
: public stan::model::model_base_crtp<model_master> {
private:
int inc_m;
int inc_a;
int meas_err;
int nx;
int nt;
int ntot_man;
int ntot_amhg;
std::vector<std::vector<int> > hasdat_man;
std::vector<std::vector<int> > hasdat_amhg;
std::vector<vector_d> Wobs;
std::vector<vector_d> Sobs;
std::vector<vector_d> dAobs;
vector_d dA_shift;
double Werr_sd;
double Serr_sd;
double dAerr_sd;
double lowerbound_logQ;
double upperbound_logQ;
double lowerbound_A0;
double upperbound_A0;
double lowerbound_logn;
double upperbound_logn;
double lowerbound_logQc;
double upperbound_logQc;
double lowerbound_logWc;
double upperbound_logWc;
double lowerbound_b;
double upperbound_b;
double lowerbound_logr;
double upperbound_logr;
double lowerbound_logWb;
double upperbound_logWb;
double lowerbound_logDb;
double upperbound_logDb;
std::vector<vector_d> sigma_man;
std::vector<vector_d> sigma_amhg;
vector_d logQ_hat;
double logQc_hat;
double logWc_hat;
std::vector<double> b_hat;
std::vector<double> logA0_hat;
std::vector<double> logn_hat;
std::vector<double> logWb_hat;
std::vector<double> logDb_hat;
std::vector<double> logr_hat;
vector_d logQ_sd;
double logQc_sd;
double logWc_sd;
std::vector<double> b_sd;
std::vector<double> logA0_sd;
std::vector<double> logn_sd;
std::vector<double> logr_sd;
std::vector<double> logWb_sd;
std::vector<double> logDb_sd;
std::vector<vector_d> dApos_array;
vector_d Wobsvec_man;
vector_d Wobsvec_amhg;
vector_d Wobsvec;
vector_d Sobsvec_man;
vector_d Sobsvec_amhg;
vector_d logWobs_man;
vector_d logWobs_amhg;
vector_d logSobs_man;
vector_d logSobs_amhg;
vector_d dApos_obs;
vector_d sigmavec_man;
vector_d sigmavec_amhg;
std::vector<int> maninds_amhg;
int ntot_w;
public:
model_master(stan::io::var_context& context__,
std::ostream* pstream__ = 0)
: model_base_crtp(0) {
ctor_body(context__, 0, pstream__);
}
model_master(stan::io::var_context& context__,
unsigned int random_seed__,
std::ostream* pstream__ = 0)
: model_base_crtp(0) {
ctor_body(context__, random_seed__, pstream__);
}
void ctor_body(stan::io::var_context& context__,
unsigned int random_seed__,
std::ostream* pstream__) {
typedef double local_scalar_t__;
boost::ecuyer1988 base_rng__ =
stan::services::util::create_rng(random_seed__, 0);
(void) base_rng__; // suppress unused var warning
current_statement_begin__ = -1;
static const char* function__ = "model_master_namespace::model_master";
(void) function__; // dummy to suppress unused var warning
size_t pos__;
(void) pos__; // dummy to suppress unused var warning
std::vector<int> vals_i__;
std::vector<double> vals_r__;
local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
(void) DUMMY_VAR__; // suppress unused var warning
try {
// initialize data block variables from context__
current_statement_begin__ = 95;
context__.validate_dims("data initialization", "inc_m", "int", context__.to_vec());
inc_m = int(0);
vals_i__ = context__.vals_i("inc_m");
pos__ = 0;
inc_m = vals_i__[pos__++];
check_greater_or_equal(function__, "inc_m", inc_m, 0);
check_less_or_equal(function__, "inc_m", inc_m, 1);
current_statement_begin__ = 96;
context__.validate_dims("data initialization", "inc_a", "int", context__.to_vec());
inc_a = int(0);
vals_i__ = context__.vals_i("inc_a");
pos__ = 0;
inc_a = vals_i__[pos__++];
check_greater_or_equal(function__, "inc_a", inc_a, 0);
check_less_or_equal(function__, "inc_a", inc_a, 1);
current_statement_begin__ = 97;
context__.validate_dims("data initialization", "meas_err", "int", context__.to_vec());
meas_err = int(0);
vals_i__ = context__.vals_i("meas_err");
pos__ = 0;
meas_err = vals_i__[pos__++];
check_greater_or_equal(function__, "meas_err", meas_err, 0);
check_less_or_equal(function__, "meas_err", meas_err, 1);
current_statement_begin__ = 100;
context__.validate_dims("data initialization", "nx", "int", context__.to_vec());
nx = int(0);
vals_i__ = context__.vals_i("nx");
pos__ = 0;
nx = vals_i__[pos__++];
check_greater_or_equal(function__, "nx", nx, 0);
current_statement_begin__ = 101;
context__.validate_dims("data initialization", "nt", "int", context__.to_vec());
nt = int(0);
vals_i__ = context__.vals_i("nt");
pos__ = 0;
nt = vals_i__[pos__++];
check_greater_or_equal(function__, "nt", nt, 0);
current_statement_begin__ = 102;
context__.validate_dims("data initialization", "ntot_man", "int", context__.to_vec());
ntot_man = int(0);
vals_i__ = context__.vals_i("ntot_man");
pos__ = 0;
ntot_man = vals_i__[pos__++];
check_greater_or_equal(function__, "ntot_man", ntot_man, 0);
current_statement_begin__ = 103;
context__.validate_dims("data initialization", "ntot_amhg", "int", context__.to_vec());
ntot_amhg = int(0);
vals_i__ = context__.vals_i("ntot_amhg");
pos__ = 0;
ntot_amhg = vals_i__[pos__++];
check_greater_or_equal(function__, "ntot_amhg", ntot_amhg, 0);
current_statement_begin__ = 106;
validate_non_negative_index("hasdat_man", "nx", nx);
validate_non_negative_index("hasdat_man", "nt", nt);
context__.validate_dims("data initialization", "hasdat_man", "int", context__.to_vec(nx,nt));
hasdat_man = std::vector<std::vector<int> >(nx, std::vector<int>(nt, int(0)));
vals_i__ = context__.vals_i("hasdat_man");
pos__ = 0;
size_t hasdat_man_k_0_max__ = nx;
size_t hasdat_man_k_1_max__ = nt;
for (size_t k_1__ = 0; k_1__ < hasdat_man_k_1_max__; ++k_1__) {
for (size_t k_0__ = 0; k_0__ < hasdat_man_k_0_max__; ++k_0__) {
hasdat_man[k_0__][k_1__] = vals_i__[pos__++];
}
}
size_t hasdat_man_i_0_max__ = nx;
size_t hasdat_man_i_1_max__ = nt;
for (size_t i_0__ = 0; i_0__ < hasdat_man_i_0_max__; ++i_0__) {
for (size_t i_1__ = 0; i_1__ < hasdat_man_i_1_max__; ++i_1__) {
check_greater_or_equal(function__, "hasdat_man[i_0__][i_1__]", hasdat_man[i_0__][i_1__], 0);
check_less_or_equal(function__, "hasdat_man[i_0__][i_1__]", hasdat_man[i_0__][i_1__], 1);
}
}
current_statement_begin__ = 107;
validate_non_negative_index("hasdat_amhg", "nx", nx);
validate_non_negative_index("hasdat_amhg", "nt", nt);
context__.validate_dims("data initialization", "hasdat_amhg", "int", context__.to_vec(nx,nt));
hasdat_amhg = std::vector<std::vector<int> >(nx, std::vector<int>(nt, int(0)));
vals_i__ = context__.vals_i("hasdat_amhg");
pos__ = 0;
size_t hasdat_amhg_k_0_max__ = nx;
size_t hasdat_amhg_k_1_max__ = nt;
for (size_t k_1__ = 0; k_1__ < hasdat_amhg_k_1_max__; ++k_1__) {
for (size_t k_0__ = 0; k_0__ < hasdat_amhg_k_0_max__; ++k_0__) {
hasdat_amhg[k_0__][k_1__] = vals_i__[pos__++];
}
}
size_t hasdat_amhg_i_0_max__ = nx;
size_t hasdat_amhg_i_1_max__ = nt;
for (size_t i_0__ = 0; i_0__ < hasdat_amhg_i_0_max__; ++i_0__) {
for (size_t i_1__ = 0; i_1__ < hasdat_amhg_i_1_max__; ++i_1__) {
check_greater_or_equal(function__, "hasdat_amhg[i_0__][i_1__]", hasdat_amhg[i_0__][i_1__], 0);
check_less_or_equal(function__, "hasdat_amhg[i_0__][i_1__]", hasdat_amhg[i_0__][i_1__], 1);
}
}
current_statement_begin__ = 110;
validate_non_negative_index("Wobs", "nt", nt);
validate_non_negative_index("Wobs", "nx", nx);
context__.validate_dims("data initialization", "Wobs", "vector_d", context__.to_vec(nx,nt));
Wobs = std::vector<Eigen::Matrix<double, Eigen::Dynamic, 1> >(nx, Eigen::Matrix<double, Eigen::Dynamic, 1>(nt));
vals_r__ = context__.vals_r("Wobs");
pos__ = 0;
size_t Wobs_j_1_max__ = nt;
size_t Wobs_k_0_max__ = nx;
for (size_t j_1__ = 0; j_1__ < Wobs_j_1_max__; ++j_1__) {
for (size_t k_0__ = 0; k_0__ < Wobs_k_0_max__; ++k_0__) {
Wobs[k_0__](j_1__) = vals_r__[pos__++];
}
}
current_statement_begin__ = 111;
validate_non_negative_index("Sobs", "nt", nt);
validate_non_negative_index("Sobs", "nx", nx);
context__.validate_dims("data initialization", "Sobs", "vector_d", context__.to_vec(nx,nt));
Sobs = std::vector<Eigen::Matrix<double, Eigen::Dynamic, 1> >(nx, Eigen::Matrix<double, Eigen::Dynamic, 1>(nt));
vals_r__ = context__.vals_r("Sobs");
pos__ = 0;
size_t Sobs_j_1_max__ = nt;
size_t Sobs_k_0_max__ = nx;
for (size_t j_1__ = 0; j_1__ < Sobs_j_1_max__; ++j_1__) {
for (size_t k_0__ = 0; k_0__ < Sobs_k_0_max__; ++k_0__) {
Sobs[k_0__](j_1__) = vals_r__[pos__++];
}
}
current_statement_begin__ = 112;
validate_non_negative_index("dAobs", "nt", nt);
validate_non_negative_index("dAobs", "nx", nx);
context__.validate_dims("data initialization", "dAobs", "vector_d", context__.to_vec(nx,nt));
dAobs = std::vector<Eigen::Matrix<double, Eigen::Dynamic, 1> >(nx, Eigen::Matrix<double, Eigen::Dynamic, 1>(nt));
vals_r__ = context__.vals_r("dAobs");
pos__ = 0;
size_t dAobs_j_1_max__ = nt;
size_t dAobs_k_0_max__ = nx;
for (size_t j_1__ = 0; j_1__ < dAobs_j_1_max__; ++j_1__) {
for (size_t k_0__ = 0; k_0__ < dAobs_k_0_max__; ++k_0__) {
dAobs[k_0__](j_1__) = vals_r__[pos__++];
}
}
current_statement_begin__ = 113;
validate_non_negative_index("dA_shift", "nx", nx);
context__.validate_dims("data initialization", "dA_shift", "vector_d", context__.to_vec(nx));
dA_shift = Eigen::Matrix<double, Eigen::Dynamic, 1>(nx);
vals_r__ = context__.vals_r("dA_shift");
pos__ = 0;
size_t dA_shift_j_1_max__ = nx;
for (size_t j_1__ = 0; j_1__ < dA_shift_j_1_max__; ++j_1__) {
dA_shift(j_1__) = vals_r__[pos__++];
}
current_statement_begin__ = 115;
context__.validate_dims("data initialization", "Werr_sd", "double", context__.to_vec());
Werr_sd = double(0);
vals_r__ = context__.vals_r("Werr_sd");
pos__ = 0;
Werr_sd = vals_r__[pos__++];
check_greater_or_equal(function__, "Werr_sd", Werr_sd, 0);
current_statement_begin__ = 116;
context__.validate_dims("data initialization", "Serr_sd", "double", context__.to_vec());
Serr_sd = double(0);
vals_r__ = context__.vals_r("Serr_sd");
pos__ = 0;
Serr_sd = vals_r__[pos__++];
check_greater_or_equal(function__, "Serr_sd", Serr_sd, 0);
current_statement_begin__ = 117;
context__.validate_dims("data initialization", "dAerr_sd", "double", context__.to_vec());
dAerr_sd = double(0);
vals_r__ = context__.vals_r("dAerr_sd");
pos__ = 0;
dAerr_sd = vals_r__[pos__++];
check_greater_or_equal(function__, "dAerr_sd", dAerr_sd, 0);
current_statement_begin__ = 120;
context__.validate_dims("data initialization", "lowerbound_logQ", "double", context__.to_vec());
lowerbound_logQ = double(0);
vals_r__ = context__.vals_r("lowerbound_logQ");
pos__ = 0;
lowerbound_logQ = vals_r__[pos__++];
current_statement_begin__ = 121;
context__.validate_dims("data initialization", "upperbound_logQ", "double", context__.to_vec());
upperbound_logQ = double(0);
vals_r__ = context__.vals_r("upperbound_logQ");
pos__ = 0;
upperbound_logQ = vals_r__[pos__++];
current_statement_begin__ = 123;
context__.validate_dims("data initialization", "lowerbound_A0", "double", context__.to_vec());
lowerbound_A0 = double(0);
vals_r__ = context__.vals_r("lowerbound_A0");
pos__ = 0;
lowerbound_A0 = vals_r__[pos__++];
current_statement_begin__ = 124;
context__.validate_dims("data initialization", "upperbound_A0", "double", context__.to_vec());
upperbound_A0 = double(0);
vals_r__ = context__.vals_r("upperbound_A0");
pos__ = 0;
upperbound_A0 = vals_r__[pos__++];
current_statement_begin__ = 125;
context__.validate_dims("data initialization", "lowerbound_logn", "double", context__.to_vec());
lowerbound_logn = double(0);
vals_r__ = context__.vals_r("lowerbound_logn");
pos__ = 0;
lowerbound_logn = vals_r__[pos__++];
current_statement_begin__ = 126;
context__.validate_dims("data initialization", "upperbound_logn", "double", context__.to_vec());
upperbound_logn = double(0);
vals_r__ = context__.vals_r("upperbound_logn");
pos__ = 0;
upperbound_logn = vals_r__[pos__++];
current_statement_begin__ = 127;
context__.validate_dims("data initialization", "lowerbound_logQc", "double", context__.to_vec());
lowerbound_logQc = double(0);
vals_r__ = context__.vals_r("lowerbound_logQc");
pos__ = 0;
lowerbound_logQc = vals_r__[pos__++];
current_statement_begin__ = 128;
context__.validate_dims("data initialization", "upperbound_logQc", "double", context__.to_vec());
upperbound_logQc = double(0);
vals_r__ = context__.vals_r("upperbound_logQc");
pos__ = 0;
upperbound_logQc = vals_r__[pos__++];
current_statement_begin__ = 129;
context__.validate_dims("data initialization", "lowerbound_logWc", "double", context__.to_vec());
lowerbound_logWc = double(0);
vals_r__ = context__.vals_r("lowerbound_logWc");
pos__ = 0;
lowerbound_logWc = vals_r__[pos__++];
current_statement_begin__ = 130;
context__.validate_dims("data initialization", "upperbound_logWc", "double", context__.to_vec());
upperbound_logWc = double(0);
vals_r__ = context__.vals_r("upperbound_logWc");
pos__ = 0;
upperbound_logWc = vals_r__[pos__++];
current_statement_begin__ = 131;
context__.validate_dims("data initialization", "lowerbound_b", "double", context__.to_vec());
lowerbound_b = double(0);
vals_r__ = context__.vals_r("lowerbound_b");
pos__ = 0;
lowerbound_b = vals_r__[pos__++];
current_statement_begin__ = 132;
context__.validate_dims("data initialization", "upperbound_b", "double", context__.to_vec());
upperbound_b = double(0);
vals_r__ = context__.vals_r("upperbound_b");
pos__ = 0;
upperbound_b = vals_r__[pos__++];
current_statement_begin__ = 133;
context__.validate_dims("data initialization", "lowerbound_logr", "double", context__.to_vec());
lowerbound_logr = double(0);
vals_r__ = context__.vals_r("lowerbound_logr");
pos__ = 0;
lowerbound_logr = vals_r__[pos__++];
current_statement_begin__ = 134;
context__.validate_dims("data initialization", "upperbound_logr", "double", context__.to_vec());
upperbound_logr = double(0);
vals_r__ = context__.vals_r("upperbound_logr");
pos__ = 0;
upperbound_logr = vals_r__[pos__++];
current_statement_begin__ = 135;
context__.validate_dims("data initialization", "lowerbound_logWb", "double", context__.to_vec());
lowerbound_logWb = double(0);
vals_r__ = context__.vals_r("lowerbound_logWb");
pos__ = 0;
lowerbound_logWb = vals_r__[pos__++];
current_statement_begin__ = 136;
context__.validate_dims("data initialization", "upperbound_logWb", "double", context__.to_vec());
upperbound_logWb = double(0);
vals_r__ = context__.vals_r("upperbound_logWb");
pos__ = 0;
upperbound_logWb = vals_r__[pos__++];
current_statement_begin__ = 137;
context__.validate_dims("data initialization", "lowerbound_logDb", "double", context__.to_vec());
lowerbound_logDb = double(0);
vals_r__ = context__.vals_r("lowerbound_logDb");
pos__ = 0;
lowerbound_logDb = vals_r__[pos__++];
current_statement_begin__ = 138;
context__.validate_dims("data initialization", "upperbound_logDb", "double", context__.to_vec());
upperbound_logDb = double(0);
vals_r__ = context__.vals_r("upperbound_logDb");
pos__ = 0;
upperbound_logDb = vals_r__[pos__++];
current_statement_begin__ = 141;
validate_non_negative_index("sigma_man", "nt", nt);
validate_non_negative_index("sigma_man", "nx", nx);
context__.validate_dims("data initialization", "sigma_man", "vector_d", context__.to_vec(nx,nt));
sigma_man = std::vector<Eigen::Matrix<double, Eigen::Dynamic, 1> >(nx, Eigen::Matrix<double, Eigen::Dynamic, 1>(nt));
vals_r__ = context__.vals_r("sigma_man");
pos__ = 0;
size_t sigma_man_j_1_max__ = nt;
size_t sigma_man_k_0_max__ = nx;
for (size_t j_1__ = 0; j_1__ < sigma_man_j_1_max__; ++j_1__) {
for (size_t k_0__ = 0; k_0__ < sigma_man_k_0_max__; ++k_0__) {
sigma_man[k_0__](j_1__) = vals_r__[pos__++];
}
}
size_t sigma_man_i_0_max__ = nx;
for (size_t i_0__ = 0; i_0__ < sigma_man_i_0_max__; ++i_0__) {
check_greater_or_equal(function__, "sigma_man[i_0__]", sigma_man[i_0__], 0);
}
current_statement_begin__ = 142;
validate_non_negative_index("sigma_amhg", "nt", nt);
validate_non_negative_index("sigma_amhg", "nx", nx);
context__.validate_dims("data initialization", "sigma_amhg", "vector_d", context__.to_vec(nx,nt));
sigma_amhg = std::vector<Eigen::Matrix<double, Eigen::Dynamic, 1> >(nx, Eigen::Matrix<double, Eigen::Dynamic, 1>(nt));
vals_r__ = context__.vals_r("sigma_amhg");
pos__ = 0;
size_t sigma_amhg_j_1_max__ = nt;
size_t sigma_amhg_k_0_max__ = nx;
for (size_t j_1__ = 0; j_1__ < sigma_amhg_j_1_max__; ++j_1__) {
for (size_t k_0__ = 0; k_0__ < sigma_amhg_k_0_max__; ++k_0__) {
sigma_amhg[k_0__](j_1__) = vals_r__[pos__++];
}
}
size_t sigma_amhg_i_0_max__ = nx;
for (size_t i_0__ = 0; i_0__ < sigma_amhg_i_0_max__; ++i_0__) {
check_greater_or_equal(function__, "sigma_amhg[i_0__]", sigma_amhg[i_0__], 0);
}
current_statement_begin__ = 145;
validate_non_negative_index("logQ_hat", "nt", nt);
context__.validate_dims("data initialization", "logQ_hat", "vector_d", context__.to_vec(nt));
logQ_hat = Eigen::Matrix<double, Eigen::Dynamic, 1>(nt);
vals_r__ = context__.vals_r("logQ_hat");
pos__ = 0;
size_t logQ_hat_j_1_max__ = nt;
for (size_t j_1__ = 0; j_1__ < logQ_hat_j_1_max__; ++j_1__) {
logQ_hat(j_1__) = vals_r__[pos__++];
}
current_statement_begin__ = 146;
context__.validate_dims("data initialization", "logQc_hat", "double", context__.to_vec());
logQc_hat = double(0);
vals_r__ = context__.vals_r("logQc_hat");
pos__ = 0;
logQc_hat = vals_r__[pos__++];
current_statement_begin__ = 147;
context__.validate_dims("data initialization", "logWc_hat", "double", context__.to_vec());
logWc_hat = double(0);
vals_r__ = context__.vals_r("logWc_hat");
pos__ = 0;
logWc_hat = vals_r__[pos__++];
current_statement_begin__ = 148;
validate_non_negative_index("b_hat", "nx", nx);
context__.validate_dims("data initialization", "b_hat", "double", context__.to_vec(nx));
b_hat = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("b_hat");
pos__ = 0;
size_t b_hat_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < b_hat_k_0_max__; ++k_0__) {
b_hat[k_0__] = vals_r__[pos__++];
}
current_statement_begin__ = 149;
validate_non_negative_index("logA0_hat", "nx", nx);
context__.validate_dims("data initialization", "logA0_hat", "double", context__.to_vec(nx));
logA0_hat = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logA0_hat");
pos__ = 0;
size_t logA0_hat_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logA0_hat_k_0_max__; ++k_0__) {
logA0_hat[k_0__] = vals_r__[pos__++];
}
current_statement_begin__ = 150;
validate_non_negative_index("logn_hat", "nx", nx);
context__.validate_dims("data initialization", "logn_hat", "double", context__.to_vec(nx));
logn_hat = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logn_hat");
pos__ = 0;
size_t logn_hat_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logn_hat_k_0_max__; ++k_0__) {
logn_hat[k_0__] = vals_r__[pos__++];
}
current_statement_begin__ = 151;
validate_non_negative_index("logWb_hat", "nx", nx);
context__.validate_dims("data initialization", "logWb_hat", "double", context__.to_vec(nx));
logWb_hat = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logWb_hat");
pos__ = 0;
size_t logWb_hat_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logWb_hat_k_0_max__; ++k_0__) {
logWb_hat[k_0__] = vals_r__[pos__++];
}
current_statement_begin__ = 152;
validate_non_negative_index("logDb_hat", "nx", nx);
context__.validate_dims("data initialization", "logDb_hat", "double", context__.to_vec(nx));
logDb_hat = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logDb_hat");
pos__ = 0;
size_t logDb_hat_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logDb_hat_k_0_max__; ++k_0__) {
logDb_hat[k_0__] = vals_r__[pos__++];
}
current_statement_begin__ = 153;
validate_non_negative_index("logr_hat", "nx", nx);
context__.validate_dims("data initialization", "logr_hat", "double", context__.to_vec(nx));
logr_hat = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logr_hat");
pos__ = 0;
size_t logr_hat_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logr_hat_k_0_max__; ++k_0__) {
logr_hat[k_0__] = vals_r__[pos__++];
}
current_statement_begin__ = 155;
validate_non_negative_index("logQ_sd", "nt", nt);
context__.validate_dims("data initialization", "logQ_sd", "vector_d", context__.to_vec(nt));
logQ_sd = Eigen::Matrix<double, Eigen::Dynamic, 1>(nt);
vals_r__ = context__.vals_r("logQ_sd");
pos__ = 0;
size_t logQ_sd_j_1_max__ = nt;
for (size_t j_1__ = 0; j_1__ < logQ_sd_j_1_max__; ++j_1__) {
logQ_sd(j_1__) = vals_r__[pos__++];
}
check_greater_or_equal(function__, "logQ_sd", logQ_sd, 0);
current_statement_begin__ = 156;
context__.validate_dims("data initialization", "logQc_sd", "double", context__.to_vec());
logQc_sd = double(0);
vals_r__ = context__.vals_r("logQc_sd");
pos__ = 0;
logQc_sd = vals_r__[pos__++];
check_greater_or_equal(function__, "logQc_sd", logQc_sd, 0);
current_statement_begin__ = 157;
context__.validate_dims("data initialization", "logWc_sd", "double", context__.to_vec());
logWc_sd = double(0);
vals_r__ = context__.vals_r("logWc_sd");
pos__ = 0;
logWc_sd = vals_r__[pos__++];
check_greater_or_equal(function__, "logWc_sd", logWc_sd, 0);
current_statement_begin__ = 158;
validate_non_negative_index("b_sd", "nx", nx);
context__.validate_dims("data initialization", "b_sd", "double", context__.to_vec(nx));
b_sd = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("b_sd");
pos__ = 0;
size_t b_sd_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < b_sd_k_0_max__; ++k_0__) {
b_sd[k_0__] = vals_r__[pos__++];
}
size_t b_sd_i_0_max__ = nx;
for (size_t i_0__ = 0; i_0__ < b_sd_i_0_max__; ++i_0__) {
check_greater_or_equal(function__, "b_sd[i_0__]", b_sd[i_0__], 0);
}
current_statement_begin__ = 159;
validate_non_negative_index("logA0_sd", "nx", nx);
context__.validate_dims("data initialization", "logA0_sd", "double", context__.to_vec(nx));
logA0_sd = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logA0_sd");
pos__ = 0;
size_t logA0_sd_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logA0_sd_k_0_max__; ++k_0__) {
logA0_sd[k_0__] = vals_r__[pos__++];
}
size_t logA0_sd_i_0_max__ = nx;
for (size_t i_0__ = 0; i_0__ < logA0_sd_i_0_max__; ++i_0__) {
check_greater_or_equal(function__, "logA0_sd[i_0__]", logA0_sd[i_0__], 0);
}
current_statement_begin__ = 160;
validate_non_negative_index("logn_sd", "nx", nx);
context__.validate_dims("data initialization", "logn_sd", "double", context__.to_vec(nx));
logn_sd = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logn_sd");
pos__ = 0;
size_t logn_sd_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logn_sd_k_0_max__; ++k_0__) {
logn_sd[k_0__] = vals_r__[pos__++];
}
size_t logn_sd_i_0_max__ = nx;
for (size_t i_0__ = 0; i_0__ < logn_sd_i_0_max__; ++i_0__) {
check_greater_or_equal(function__, "logn_sd[i_0__]", logn_sd[i_0__], 0);
}
current_statement_begin__ = 161;
validate_non_negative_index("logr_sd", "nx", nx);
context__.validate_dims("data initialization", "logr_sd", "double", context__.to_vec(nx));
logr_sd = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logr_sd");
pos__ = 0;
size_t logr_sd_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logr_sd_k_0_max__; ++k_0__) {
logr_sd[k_0__] = vals_r__[pos__++];
}
size_t logr_sd_i_0_max__ = nx;
for (size_t i_0__ = 0; i_0__ < logr_sd_i_0_max__; ++i_0__) {
check_greater_or_equal(function__, "logr_sd[i_0__]", logr_sd[i_0__], 0);
}
current_statement_begin__ = 162;
validate_non_negative_index("logWb_sd", "nx", nx);
context__.validate_dims("data initialization", "logWb_sd", "double", context__.to_vec(nx));
logWb_sd = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logWb_sd");
pos__ = 0;
size_t logWb_sd_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logWb_sd_k_0_max__; ++k_0__) {
logWb_sd[k_0__] = vals_r__[pos__++];
}
size_t logWb_sd_i_0_max__ = nx;
for (size_t i_0__ = 0; i_0__ < logWb_sd_i_0_max__; ++i_0__) {
check_greater_or_equal(function__, "logWb_sd[i_0__]", logWb_sd[i_0__], 0);
}
current_statement_begin__ = 163;
validate_non_negative_index("logDb_sd", "nx", nx);
context__.validate_dims("data initialization", "logDb_sd", "double", context__.to_vec(nx));
logDb_sd = std::vector<double>(nx, double(0));
vals_r__ = context__.vals_r("logDb_sd");
pos__ = 0;
size_t logDb_sd_k_0_max__ = nx;
for (size_t k_0__ = 0; k_0__ < logDb_sd_k_0_max__; ++k_0__) {
logDb_sd[k_0__] = vals_r__[pos__++];
}
size_t logDb_sd_i_0_max__ = nx;
for (size_t i_0__ = 0; i_0__ < logDb_sd_i_0_max__; ++i_0__) {
check_greater_or_equal(function__, "logDb_sd[i_0__]", logDb_sd[i_0__], 0);
}
// initialize transformed data variables
current_statement_begin__ = 168;
validate_non_negative_index("dApos_array", "nt", nt);
validate_non_negative_index("dApos_array", "nx", nx);
dApos_array = std::vector<Eigen::Matrix<double, Eigen::Dynamic, 1> >(nx, Eigen::Matrix<double, Eigen::Dynamic, 1>(nt));
stan::math::fill(dApos_array, DUMMY_VAR__);
current_statement_begin__ = 169;
validate_non_negative_index("Wobsvec_man", "ntot_man", ntot_man);
Wobsvec_man = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_man);
stan::math::fill(Wobsvec_man, DUMMY_VAR__);
current_statement_begin__ = 170;
validate_non_negative_index("Wobsvec_amhg", "ntot_amhg", ntot_amhg);
Wobsvec_amhg = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_amhg);
stan::math::fill(Wobsvec_amhg, DUMMY_VAR__);
current_statement_begin__ = 171;
validate_non_negative_index("Wobsvec", "(inc_a ? ntot_amhg : ntot_man )", (inc_a ? ntot_amhg : ntot_man ));
Wobsvec = Eigen::Matrix<double, Eigen::Dynamic, 1>((inc_a ? ntot_amhg : ntot_man ));
stan::math::fill(Wobsvec, DUMMY_VAR__);
current_statement_begin__ = 172;
validate_non_negative_index("Sobsvec_man", "ntot_man", ntot_man);
Sobsvec_man = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_man);
stan::math::fill(Sobsvec_man, DUMMY_VAR__);
current_statement_begin__ = 173;
validate_non_negative_index("Sobsvec_amhg", "ntot_man", ntot_man);
Sobsvec_amhg = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_man);
stan::math::fill(Sobsvec_amhg, DUMMY_VAR__);
current_statement_begin__ = 175;
validate_non_negative_index("logWobs_man", "ntot_man", ntot_man);
logWobs_man = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_man);
stan::math::fill(logWobs_man, DUMMY_VAR__);
current_statement_begin__ = 176;
validate_non_negative_index("logWobs_amhg", "ntot_amhg", ntot_amhg);
logWobs_amhg = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_amhg);
stan::math::fill(logWobs_amhg, DUMMY_VAR__);
current_statement_begin__ = 177;
validate_non_negative_index("logSobs_man", "ntot_man", ntot_man);
logSobs_man = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_man);
stan::math::fill(logSobs_man, DUMMY_VAR__);
current_statement_begin__ = 178;
validate_non_negative_index("logSobs_amhg", "ntot_amhg", ntot_amhg);
logSobs_amhg = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_amhg);
stan::math::fill(logSobs_amhg, DUMMY_VAR__);
current_statement_begin__ = 179;
validate_non_negative_index("dApos_obs", "ntot_man", ntot_man);
dApos_obs = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_man);
stan::math::fill(dApos_obs, DUMMY_VAR__);
current_statement_begin__ = 180;
validate_non_negative_index("sigmavec_man", "ntot_man", ntot_man);
sigmavec_man = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_man);
stan::math::fill(sigmavec_man, DUMMY_VAR__);
current_statement_begin__ = 181;
validate_non_negative_index("sigmavec_amhg", "ntot_amhg", ntot_amhg);
sigmavec_amhg = Eigen::Matrix<double, Eigen::Dynamic, 1>(ntot_amhg);
stan::math::fill(sigmavec_amhg, DUMMY_VAR__);
current_statement_begin__ = 183;
validate_non_negative_index("maninds_amhg", "ntot_man", ntot_man);
maninds_amhg = std::vector<int>(ntot_man, int(0));
stan::math::fill(maninds_amhg, std::numeric_limits<int>::min());
current_statement_begin__ = 185;
ntot_w = int(0);
stan::math::fill(ntot_w, std::numeric_limits<int>::min());
// execute transformed data statements
current_statement_begin__ = 186;
stan::math::assign(ntot_w, (inc_a ? ntot_amhg : ntot_man ));
current_statement_begin__ = 188;
for (int i = 1; i <= nx; ++i) {
current_statement_begin__ = 189;
stan::model::assign(dApos_array,
stan::model::cons_list(stan::model::index_uni(i), stan::model::nil_index_list()),
subtract(get_base1(dAobs, i, "dAobs", 1), min(get_base1(dAobs, i, "dAobs", 1))),
"assigning variable dApos_array");
}
current_statement_begin__ = 193;
stan::math::assign(Wobsvec_man, ragged_vec(Wobs, hasdat_man, pstream__));
current_statement_begin__ = 194;
stan::math::assign(Wobsvec_amhg, ragged_vec(Wobs, hasdat_amhg, pstream__));
current_statement_begin__ = 195;
stan::math::assign(Wobsvec, (inc_a ? stan::math::promote_scalar<double>(Wobsvec_amhg) : stan::math::promote_scalar<double>(Wobsvec_man) ));
current_statement_begin__ = 196;
stan::math::assign(Sobsvec_man, ragged_vec(Sobs, hasdat_man, pstream__));
current_statement_begin__ = 197;
stan::math::assign(Sobsvec_amhg, ragged_vec(Sobs, hasdat_amhg, pstream__));
current_statement_begin__ = 198;
stan::math::assign(dApos_obs, ragged_vec(dApos_array, hasdat_man, pstream__));
current_statement_begin__ = 200;
stan::math::assign(logWobs_man, stan::math::log(Wobsvec_man));
current_statement_begin__ = 201;
stan::math::assign(logSobs_man, stan::math::log(Sobsvec_man));
current_statement_begin__ = 202;
stan::math::assign(logSobs_amhg, stan::math::log(Sobsvec_amhg));
current_statement_begin__ = 204;
stan::math::assign(sigmavec_man, ragged_vec(sigma_man, hasdat_man, pstream__));
current_statement_begin__ = 205;
stan::math::assign(sigmavec_amhg, ragged_vec(sigma_amhg, hasdat_amhg, pstream__));
current_statement_begin__ = 209;
stan::math::assign(maninds_amhg, commoninds(hasdat_amhg, hasdat_man, pstream__));
// validate transformed data
// validate, set parameter ranges
num_params_r__ = 0U;
param_ranges_i__.clear();
current_statement_begin__ = 213;
validate_non_negative_index("logQ", "nt", nt);
num_params_r__ += nt;