From 77dd46e779272d97340ac1adbcb85ab4dcd3432d Mon Sep 17 00:00:00 2001 From: jamesaazam Date: Thu, 6 Jul 2023 11:43:50 -0500 Subject: [PATCH] Renamed extdata folder to testdata (Closes #87) --- .../constant_foi_sim_data_grouped.csv | 0 .../constant_foi_sim_data_no_grouped.csv | 0 .../{extdata => testdata}/haiti_ssa_sample.RDS | Bin .../{extdata => testdata}/model_constant.RDS | Bin tests/testthat/testdata/model_constant.json | 1 + .../{extdata => testdata}/model_tv_normal.RDS | Bin tests/testthat/testdata/model_tv_normal.json | 1 + .../{extdata => testdata}/model_tv_normal_log.RDS | Bin tests/testthat/testdata/model_tv_normal_log.json | 1 + .../prev_expanded_constant.RDS | Bin .../prev_expanded_tv_normal.RDS | Bin .../prev_expanded_tv_normal_log.RDS | Bin .../sw_dec_foi_sim_data_grouped.csv | 0 .../sw_dec_foi_sim_data_no_grouped.csv | 0 14 files changed, 3 insertions(+) rename tests/testthat/{extdata => testdata}/constant_foi_sim_data_grouped.csv (100%) rename tests/testthat/{extdata => testdata}/constant_foi_sim_data_no_grouped.csv (100%) rename tests/testthat/{extdata => testdata}/haiti_ssa_sample.RDS (100%) rename tests/testthat/{extdata => testdata}/model_constant.RDS (100%) create mode 100644 tests/testthat/testdata/model_constant.json rename tests/testthat/{extdata => testdata}/model_tv_normal.RDS (100%) create mode 100644 tests/testthat/testdata/model_tv_normal.json rename tests/testthat/{extdata => testdata}/model_tv_normal_log.RDS (100%) create mode 100644 tests/testthat/testdata/model_tv_normal_log.json rename tests/testthat/{extdata => testdata}/prev_expanded_constant.RDS (100%) rename tests/testthat/{extdata => testdata}/prev_expanded_tv_normal.RDS (100%) rename tests/testthat/{extdata => testdata}/prev_expanded_tv_normal_log.RDS (100%) rename tests/testthat/{extdata => testdata}/sw_dec_foi_sim_data_grouped.csv (100%) rename tests/testthat/{extdata => testdata}/sw_dec_foi_sim_data_no_grouped.csv (100%) diff --git a/tests/testthat/extdata/constant_foi_sim_data_grouped.csv b/tests/testthat/testdata/constant_foi_sim_data_grouped.csv similarity index 100% rename from tests/testthat/extdata/constant_foi_sim_data_grouped.csv rename to tests/testthat/testdata/constant_foi_sim_data_grouped.csv diff --git a/tests/testthat/extdata/constant_foi_sim_data_no_grouped.csv b/tests/testthat/testdata/constant_foi_sim_data_no_grouped.csv similarity index 100% rename from tests/testthat/extdata/constant_foi_sim_data_no_grouped.csv rename to tests/testthat/testdata/constant_foi_sim_data_no_grouped.csv diff --git a/tests/testthat/extdata/haiti_ssa_sample.RDS b/tests/testthat/testdata/haiti_ssa_sample.RDS similarity index 100% rename from tests/testthat/extdata/haiti_ssa_sample.RDS rename to tests/testthat/testdata/haiti_ssa_sample.RDS diff --git a/tests/testthat/extdata/model_constant.RDS b/tests/testthat/testdata/model_constant.RDS similarity index 100% rename from tests/testthat/extdata/model_constant.RDS rename 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{\\n int Nobs;\\n int Npos[Nobs];\\n int Ntotal[Nobs];\\n int Age[Nobs];\\n int Ymax;\\n matrix[Nobs, Ymax] AgeExpoMatrix;\\n}\\nparameters {\\n real lambda0;\\n}\\ntransformed parameters {\\n real P[Nobs];\\n real ScalerDotProduct[Nobs];\\n row_vector[Ymax] foi;\\n\\n for (j in 1:Ymax) {\\n foi[j] = lambda0;\\n }\\n\\n for (i in 1:Nobs){\\n ScalerDotProduct[i] = dot_product(AgeExpoMatrix[i,], foi);\\n P[i] = 1 - exp(-ScalerDotProduct[i]);\\n }\\n}\\nmodel {\\n for (i in 1:Nobs)\\n Npos[i] ~ binomial(Ntotal[i], P[i]) ;\\n\\n lambda0 ~ uniform (0, 2);\\n}\\ngenerated quantities{\\n vector[Nobs] Npos_sim;\\n vector[Nobs] P_sim;\\n vector[Nobs] logLikelihood;\\n for(i in 1:Nobs){\\n Npos_sim[i] = binomial_rng(Ntotal[i], P[i]);\\n P_sim[i] = Npos_sim[i] / Ntotal[i];\\n logLikelihood[i] = binomial_lpmf(Npos[i] | Ntotal[i], P[i]);\\n }\\n}\"]},\"model_cpp\":{\"type\":\"list\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"model_cppname\",\"model_cppcode\"]}},\"value\":[{\"type\":\"character\",\"attributes\":{},\"value\":[\"model100b163de462a_\"]},{\"type\":\"character\",\"attributes\":{},\"value\":[\"#ifndef MODELS_HPP\\n#define MODELS_HPP\\n#define STAN__SERVICES__COMMAND_HPP\\n#include \\n#ifndef USE_STANC3\\n#define USE_STANC3\\n#endif\\n// Code generated by %%NAME%% %%VERSION%%\\n#include \\nnamespace model100b163de462a__namespace {\\ninline void validate_positive_index(const char* var_name, const char* expr,\\n int val) {\\n if (val < 1) {\\n std::stringstream msg;\\n msg << \\\"Found dimension size less than one in simplex declaration\\\"\\n << \\\"; variable=\\\" << var_name << \\\"; dimension size expression=\\\" << expr\\n << \\\"; expression value=\\\" << val;\\n std::string msg_str(msg.str());\\n throw std::invalid_argument(msg_str.c_str());\\n }\\n}\\ninline void validate_unit_vector_index(const char* var_name, const char* expr,\\n int val) {\\n if (val <= 1) {\\n std::stringstream msg;\\n if (val == 1) {\\n msg << \\\"Found dimension size one in unit vector declaration.\\\"\\n << \\\" One-dimensional unit vector is discrete\\\"\\n << \\\" but the target distribution must be continuous.\\\"\\n << \\\" variable=\\\" << var_name << \\\"; dimension size expression=\\\" << expr;\\n } else {\\n msg << \\\"Found dimension size less than one in unit vector declaration\\\"\\n << \\\"; variable=\\\" << var_name << \\\"; dimension size expression=\\\" << expr\\n << \\\"; expression value=\\\" << val;\\n }\\n std::string msg_str(msg.str());\\n throw std::invalid_argument(msg_str.c_str());\\n }\\n}\\nusing std::istream;\\nusing std::string;\\nusing std::stringstream;\\nusing std::vector;\\nusing std::pow;\\nusing stan::io::dump;\\nusing stan::math::lgamma;\\nusing stan::model::model_base_crtp;\\nusing stan::model::rvalue;\\nusing stan::model::cons_list;\\nusing stan::model::index_uni;\\nusing stan::model::index_max;\\nusing stan::model::index_min;\\nusing stan::model::index_min_max;\\nusing stan::model::index_multi;\\nusing stan::model::index_omni;\\nusing stan::model::nil_index_list;\\nusing namespace stan::math;\\nusing stan::math::pow; \\nstan::math::profile_map profiles__;\\nstatic int current_statement__= 0;\\nstatic const std::vector locations_array__ = {\\\" (found before start of program)\\\",\\n \\\" (in 'anon_model', line 10, column 3 to column 33)\\\",\\n \\\" (in 'anon_model', line 13, column 2 to column 15)\\\",\\n \\\" (in 'anon_model', line 14, column 2 to column 30)\\\",\\n \\\" (in 'anon_model', line 15, column 2 to column 23)\\\",\\n \\\" (in 'anon_model', line 18, column 3 to column 20)\\\",\\n \\\" (in 'anon_model', line 17, column 19 to line 19, column 4)\\\",\\n \\\" (in 'anon_model', line 17, column 1 to line 19, column 4)\\\",\\n \\\" (in 'anon_model', line 22, column 3 to column 61)\\\",\\n \\\" (in 'anon_model', line 23, column 3 to column 40)\\\",\\n \\\" (in 'anon_model', line 21, column 18 to line 24, column 2)\\\",\\n \\\" (in 'anon_model', line 21, column 1 to line 24, column 2)\\\",\\n \\\" (in 'anon_model', line 33, column 2 to column 24)\\\",\\n \\\" (in 'anon_model', line 34, column 2 to column 21)\\\",\\n \\\" (in 'anon_model', line 35, column 2 to column 29)\\\",\\n \\\" (in 'anon_model', line 37, column 4 to column 48)\\\",\\n \\\" (in 'anon_model', line 38, column 4 to column 39)\\\",\\n \\\" (in 'anon_model', line 39, column 4 to column 64)\\\",\\n \\\" (in 'anon_model', line 36, column 18 to line 40, column 3)\\\",\\n \\\" (in 'anon_model', line 36, column 2 to line 40, column 3)\\\",\\n \\\" (in 'anon_model', line 28, column 3 to column 40)\\\",\\n \\\" (in 'anon_model', line 27, column 2 to line 28, column 40)\\\",\\n \\\" (in 'anon_model', line 30, column 3 to column 28)\\\",\\n \\\" (in 'anon_model', line 2, column 5 to column 23)\\\",\\n \\\" (in 'anon_model', line 3, column 14 to column 18)\\\",\\n \\\" (in 'anon_model', line 3, column 5 to column 20)\\\",\\n \\\" (in 'anon_model', line 4, column 16 to column 20)\\\",\\n \\\" (in 'anon_model', line 4, column 5 to column 22)\\\",\\n \\\" (in 'anon_model', line 5, column 13 to column 17)\\\",\\n \\\" (in 'anon_model', line 5, column 5 to column 19)\\\",\\n \\\" (in 'anon_model', line 6, column 5 to column 23)\\\",\\n \\\" (in 'anon_model', line 7, column 12 to column 16)\\\",\\n \\\" (in 'anon_model', line 7, column 18 to column 22)\\\",\\n \\\" (in 'anon_model', line 7, column 5 to column 38)\\\",\\n \\\" (in 'anon_model', line 13, column 9 to column 13)\\\",\\n \\\" (in 'anon_model', line 14, column 24 to column 28)\\\",\\n \\\" (in 'anon_model', line 15, column 13 to column 17)\\\",\\n \\\" (in 'anon_model', line 33, column 9 to column 13)\\\",\\n \\\" (in 'anon_model', line 34, column 9 to column 13)\\\",\\n \\\" (in 'anon_model', line 35, column 9 to column 13)\\\"};\\nclass model100b163de462a_ final : public model_base_crtp {\\nprivate:\\n int Nobs;\\n std::vector Npos;\\n std::vector Ntotal;\\n std::vector Age;\\n int Ymax;\\n Eigen::Matrix AgeExpoMatrix;\\n \\npublic:\\n ~model100b163de462a_() { }\\n \\n inline std::string model_name() const final { return \\\"model100b163de462a_\\\"; }\\n inline std::vector model_compile_info() const noexcept {\\n return std::vector{\\\"stanc_version = %%NAME%%3 %%VERSION%%\\\", \\\"stancflags = \\\"};\\n }\\n \\n \\n model100b163de462a_(stan::io::var_context& context__,\\n unsigned int random_seed__ = 0,\\n std::ostream* pstream__ = nullptr) : model_base_crtp(0) {\\n using local_scalar_t__ = double ;\\n boost::ecuyer1988 base_rng__ = \\n stan::services::util::create_rng(random_seed__, 0);\\n (void) base_rng__; // suppress unused var warning\\n static const char* function__ = \\\"model100b163de462a__namespace::model100b163de462a_\\\";\\n (void) function__; // suppress unused var warning\\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());\\n (void) DUMMY_VAR__; // suppress unused var warning\\n \\n try {\\n int pos__;\\n pos__ = std::numeric_limits::min();\\n \\n pos__ = 1;\\n current_statement__ = 23;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Nobs\\\",\\\"int\\\",\\n context__.to_vec());\\n Nobs = std::numeric_limits::min();\\n \\n current_statement__ = 23;\\n Nobs = context__.vals_i(\\\"Nobs\\\")[(1 - 1)];\\n current_statement__ = 23;\\n current_statement__ = 23;\\n check_greater_or_equal(function__, \\\"Nobs\\\", Nobs, 0);\\n current_statement__ = 24;\\n validate_non_negative_index(\\\"Npos\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 25;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Npos\\\",\\\"int\\\",\\n context__.to_vec(Nobs));\\n Npos = std::vector(Nobs, std::numeric_limits::min());\\n \\n current_statement__ = 25;\\n assign(Npos, nil_index_list(), context__.vals_i(\\\"Npos\\\"),\\n \\\"assigning variable Npos\\\");\\n current_statement__ = 26;\\n validate_non_negative_index(\\\"Ntotal\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 27;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Ntotal\\\",\\\"int\\\",\\n context__.to_vec(Nobs));\\n Ntotal = std::vector(Nobs, std::numeric_limits::min());\\n \\n current_statement__ = 27;\\n assign(Ntotal, nil_index_list(), context__.vals_i(\\\"Ntotal\\\"),\\n \\\"assigning variable Ntotal\\\");\\n current_statement__ = 28;\\n validate_non_negative_index(\\\"Age\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 29;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Age\\\",\\\"int\\\",\\n context__.to_vec(Nobs));\\n Age = std::vector(Nobs, std::numeric_limits::min());\\n \\n current_statement__ = 29;\\n assign(Age, nil_index_list(), context__.vals_i(\\\"Age\\\"),\\n \\\"assigning variable Age\\\");\\n current_statement__ = 30;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Ymax\\\",\\\"int\\\",\\n context__.to_vec());\\n Ymax = std::numeric_limits::min();\\n \\n current_statement__ = 30;\\n Ymax = context__.vals_i(\\\"Ymax\\\")[(1 - 1)];\\n current_statement__ = 30;\\n current_statement__ = 30;\\n check_greater_or_equal(function__, \\\"Ymax\\\", Ymax, 1);\\n current_statement__ = 31;\\n validate_non_negative_index(\\\"AgeExpoMatrix\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 32;\\n validate_non_negative_index(\\\"AgeExpoMatrix\\\", \\\"Ymax\\\", Ymax);\\n current_statement__ = 33;\\n context__.validate_dims(\\\"data initialization\\\",\\\"AgeExpoMatrix\\\",\\\"double\\\",\\n context__.to_vec(Nobs, Ymax));\\n AgeExpoMatrix = Eigen::Matrix(Nobs, Ymax);\\n stan::math::fill(AgeExpoMatrix, std::numeric_limits::quiet_NaN());\\n \\n {\\n std::vector AgeExpoMatrix_flat__;\\n current_statement__ = 33;\\n assign(AgeExpoMatrix_flat__, nil_index_list(),\\n context__.vals_r(\\\"AgeExpoMatrix\\\"),\\n \\\"assigning variable AgeExpoMatrix_flat__\\\");\\n current_statement__ = 33;\\n pos__ = 1;\\n current_statement__ = 33;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 33;\\n for (int sym2__ = 1; sym2__ <= Nobs; ++sym2__) {\\n current_statement__ = 33;\\n assign(AgeExpoMatrix,\\n cons_list(index_uni(sym2__),\\n cons_list(index_uni(sym1__), nil_index_list())),\\n AgeExpoMatrix_flat__[(pos__ - 1)],\\n \\\"assigning variable AgeExpoMatrix\\\");\\n current_statement__ = 33;\\n pos__ = (pos__ + 1);}}\\n }\\n current_statement__ = 34;\\n validate_non_negative_index(\\\"P\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 35;\\n validate_non_negative_index(\\\"ScalerDotProduct\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 36;\\n validate_non_negative_index(\\\"foi\\\", \\\"Ymax\\\", Ymax);\\n current_statement__ = 37;\\n validate_non_negative_index(\\\"Npos_sim\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 38;\\n validate_non_negative_index(\\\"P_sim\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 39;\\n validate_non_negative_index(\\\"logLikelihood\\\", \\\"Nobs\\\", Nobs);\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n num_params_r__ = 0U;\\n \\n try {\\n num_params_r__ += 1;\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n }\\n template * = nullptr, stan::require_vector_like_vt* = nullptr>\\n inline stan::scalar_type_t log_prob_impl(VecR& params_r__,\\n VecI& params_i__,\\n std::ostream* pstream__ = nullptr) const {\\n using T__ = stan::scalar_type_t;\\n using local_scalar_t__ = T__;\\n T__ lp__(0.0);\\n stan::math::accumulator lp_accum__;\\n static const char* function__ = \\\"model100b163de462a__namespace::log_prob\\\";\\n(void) function__; // suppress unused var warning\\n stan::io::reader in__(params_r__, params_i__);\\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());\\n (void) DUMMY_VAR__; // suppress unused var warning\\n \\n try {\\n local_scalar_t__ lambda0;\\n lambda0 = DUMMY_VAR__;\\n \\n current_statement__ = 1;\\n lambda0 = in__.scalar();\\n current_statement__ = 1;\\n if (jacobian__) {\\n current_statement__ = 1;\\n lambda0 = stan::math::lub_constrain(lambda0, 0, 2, lp__);\\n } else {\\n current_statement__ = 1;\\n lambda0 = stan::math::lub_constrain(lambda0, 0, 2);\\n }\\n std::vector P;\\n P = std::vector(Nobs, DUMMY_VAR__);\\n \\n std::vector ScalerDotProduct;\\n ScalerDotProduct = std::vector(Nobs, DUMMY_VAR__);\\n \\n Eigen::Matrix foi;\\n foi = Eigen::Matrix(Ymax);\\n stan::math::fill(foi, DUMMY_VAR__);\\n \\n current_statement__ = 7;\\n for (int j = 1; j <= Ymax; ++j) {\\n current_statement__ = 5;\\n assign(foi, cons_list(index_uni(j), nil_index_list()), lambda0,\\n \\\"assigning variable foi\\\");}\\n current_statement__ = 11;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 8;\\n assign(ScalerDotProduct, cons_list(index_uni(i), nil_index_list()),\\n dot_product(\\n rvalue(AgeExpoMatrix,\\n cons_list(index_uni(i),\\n cons_list(index_omni(), nil_index_list())), \\\"AgeExpoMatrix\\\"),\\n foi), \\\"assigning variable ScalerDotProduct\\\");\\n current_statement__ = 9;\\n assign(P, cons_list(index_uni(i), nil_index_list()),\\n (1 - stan::math::exp(-ScalerDotProduct[(i - 1)])),\\n \\\"assigning variable P\\\");}\\n {\\n current_statement__ = 21;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 20;\\n lp_accum__.add(\\n binomial_lpmf(Npos[(i - 1)], Ntotal[(i - 1)],\\n P[(i - 1)]));}\\n current_statement__ = 22;\\n lp_accum__.add(uniform_lpdf(lambda0, 0, 2));\\n }\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n lp_accum__.add(lp__);\\n return lp_accum__.sum();\\n } // log_prob_impl() \\n \\n template * = nullptr, stan::require_vector_like_vt* = nullptr, stan::require_std_vector_vt* = nullptr>\\n inline void write_array_impl(RNG& base_rng__, VecR& params_r__,\\n VecI& params_i__, VecVar& vars__,\\n const bool emit_transformed_parameters__ = true,\\n const bool emit_generated_quantities__ = true,\\n std::ostream* pstream__ = nullptr) const {\\n using local_scalar_t__ = double;\\n vars__.resize(0);\\n stan::io::reader in__(params_r__, params_i__);\\n static const char* function__ = \\\"model100b163de462a__namespace::write_array\\\";\\n(void) function__; // suppress unused var warning\\n (void) function__; // suppress unused var warning\\n double lp__ = 0.0;\\n (void) lp__; // dummy to suppress unused var warning\\n stan::math::accumulator lp_accum__;\\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());\\n (void) DUMMY_VAR__; // suppress unused var warning\\n \\n try {\\n double lambda0;\\n lambda0 = std::numeric_limits::quiet_NaN();\\n \\n current_statement__ = 1;\\n lambda0 = in__.scalar();\\n current_statement__ = 1;\\n lambda0 = stan::math::lub_constrain(lambda0, 0, 2);\\n std::vector P;\\n P = std::vector(Nobs, std::numeric_limits::quiet_NaN());\\n \\n std::vector ScalerDotProduct;\\n ScalerDotProduct = std::vector(Nobs, std::numeric_limits::quiet_NaN());\\n \\n Eigen::Matrix foi;\\n foi = Eigen::Matrix(Ymax);\\n stan::math::fill(foi, std::numeric_limits::quiet_NaN());\\n \\n vars__.emplace_back(lambda0);\\n if (logical_negation((primitive_value(emit_transformed_parameters__) ||\\n primitive_value(emit_generated_quantities__)))) {\\n return ;\\n } \\n current_statement__ = 7;\\n for (int j = 1; j <= Ymax; ++j) {\\n current_statement__ = 5;\\n assign(foi, cons_list(index_uni(j), nil_index_list()), lambda0,\\n \\\"assigning variable foi\\\");}\\n current_statement__ = 11;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 8;\\n assign(ScalerDotProduct, cons_list(index_uni(i), nil_index_list()),\\n dot_product(\\n rvalue(AgeExpoMatrix,\\n cons_list(index_uni(i),\\n cons_list(index_omni(), nil_index_list())), \\\"AgeExpoMatrix\\\"),\\n foi), \\\"assigning variable ScalerDotProduct\\\");\\n current_statement__ = 9;\\n assign(P, cons_list(index_uni(i), nil_index_list()),\\n (1 - stan::math::exp(-ScalerDotProduct[(i - 1)])),\\n \\\"assigning variable P\\\");}\\n if (emit_transformed_parameters__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(P[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(ScalerDotProduct[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n vars__.emplace_back(foi[(sym1__ - 1)]);}\\n } \\n if (logical_negation(emit_generated_quantities__)) {\\n return ;\\n } \\n Eigen::Matrix Npos_sim;\\n Npos_sim = Eigen::Matrix(Nobs);\\n stan::math::fill(Npos_sim, std::numeric_limits::quiet_NaN());\\n \\n Eigen::Matrix P_sim;\\n P_sim = Eigen::Matrix(Nobs);\\n stan::math::fill(P_sim, std::numeric_limits::quiet_NaN());\\n \\n Eigen::Matrix logLikelihood;\\n logLikelihood = Eigen::Matrix(Nobs);\\n stan::math::fill(logLikelihood, std::numeric_limits::quiet_NaN());\\n \\n current_statement__ = 19;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 15;\\n assign(Npos_sim, cons_list(index_uni(i), nil_index_list()),\\n binomial_rng(Ntotal[(i - 1)], P[(i - 1)], base_rng__),\\n \\\"assigning variable Npos_sim\\\");\\n current_statement__ = 16;\\n assign(P_sim, cons_list(index_uni(i), nil_index_list()),\\n (Npos_sim[(i - 1)] / Ntotal[(i - 1)]), \\\"assigning variable P_sim\\\");\\n current_statement__ = 17;\\n assign(logLikelihood, cons_list(index_uni(i), nil_index_list()),\\n binomial_lpmf(Npos[(i - 1)], Ntotal[(i - 1)], P[(i - 1)]),\\n \\\"assigning variable logLikelihood\\\");}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(Npos_sim[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(P_sim[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(logLikelihood[(sym1__ - 1)]);}\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n } // write_array_impl() \\n \\n template * = nullptr, stan::require_vector_like_vt* = nullptr>\\n inline void transform_inits_impl(const stan::io::var_context& context__,\\n VecI& params_i__, VecVar& vars__,\\n std::ostream* pstream__ = nullptr) const {\\n using local_scalar_t__ = double;\\n vars__.clear();\\n vars__.reserve(num_params_r__);\\n \\n try {\\n int pos__;\\n pos__ = std::numeric_limits::min();\\n \\n pos__ = 1;\\n double lambda0;\\n lambda0 = std::numeric_limits::quiet_NaN();\\n \\n current_statement__ = 1;\\n lambda0 = context__.vals_r(\\\"lambda0\\\")[(1 - 1)];\\n double lambda0_free__;\\n lambda0_free__ = std::numeric_limits::quiet_NaN();\\n \\n current_statement__ = 1;\\n lambda0_free__ = stan::math::lub_free(lambda0, 0, 2);\\n vars__.emplace_back(lambda0_free__);\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n } // transform_inits_impl() \\n \\n inline void get_param_names(std::vector& names__) const {\\n \\n names__.clear();\\n names__.emplace_back(\\\"lambda0\\\");\\n names__.emplace_back(\\\"P\\\");\\n names__.emplace_back(\\\"ScalerDotProduct\\\");\\n names__.emplace_back(\\\"foi\\\");\\n names__.emplace_back(\\\"Npos_sim\\\");\\n names__.emplace_back(\\\"P_sim\\\");\\n names__.emplace_back(\\\"logLikelihood\\\");\\n } // get_param_names() \\n \\n inline void get_dims(std::vector>& dimss__) const {\\n dimss__.clear();\\n dimss__.emplace_back(std::vector{});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Ymax)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n } // get_dims() \\n \\n inline void constrained_param_names(\\n std::vector& param_names__,\\n bool emit_transformed_parameters__ = true,\\n bool emit_generated_quantities__ = true) const\\n final {\\n \\n param_names__.emplace_back(std::string() + \\\"lambda0\\\");\\n if (emit_transformed_parameters__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"ScalerDotProduct\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"foi\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n if (emit_generated_quantities__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"Npos_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"logLikelihood\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n } // constrained_param_names() \\n \\n inline void unconstrained_param_names(\\n std::vector& param_names__,\\n bool emit_transformed_parameters__ = true,\\n bool emit_generated_quantities__ = true) const\\n final {\\n \\n param_names__.emplace_back(std::string() + \\\"lambda0\\\");\\n if (emit_transformed_parameters__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"ScalerDotProduct\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"foi\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n if (emit_generated_quantities__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"Npos_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"logLikelihood\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n } // unconstrained_param_names() \\n \\n inline std::string get_constrained_sizedtypes() const {\\n stringstream s__;\\n s__ << \\\"[{\\\\\\\"name\\\\\\\":\\\\\\\"lambda0\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"ScalerDotProduct\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"foi\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Ymax << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"Npos_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"logLikelihood\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"}]\\\";\\n return s__.str();\\n } // get_constrained_sizedtypes() \\n \\n inline std::string get_unconstrained_sizedtypes() const {\\n stringstream s__;\\n s__ << \\\"[{\\\\\\\"name\\\\\\\":\\\\\\\"lambda0\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"ScalerDotProduct\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"foi\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Ymax << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"Npos_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"logLikelihood\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"}]\\\";\\n return s__.str();\\n } // get_unconstrained_sizedtypes() \\n \\n \\n // Begin method overload boilerplate\\n template \\n inline void write_array(RNG& base_rng,\\n Eigen::Matrix& params_r,\\n Eigen::Matrix& vars,\\n const bool emit_transformed_parameters = true,\\n const bool emit_generated_quantities = true,\\n std::ostream* pstream = nullptr) const {\\n std::vector vars_vec(vars.size());\\n std::vector params_i;\\n write_array_impl(base_rng, params_r, params_i, vars_vec,\\n emit_transformed_parameters, emit_generated_quantities, pstream);\\n vars.resize(vars_vec.size());\\n for (int i = 0; i < vars.size(); ++i) {\\n vars.coeffRef(i) = vars_vec[i];\\n }\\n }\\n template \\n inline void write_array(RNG& base_rng, std::vector& params_r,\\n std::vector& params_i,\\n std::vector& vars,\\n bool emit_transformed_parameters = true,\\n bool emit_generated_quantities = true,\\n std::ostream* pstream = nullptr) const {\\n write_array_impl(base_rng, params_r, params_i, vars, emit_transformed_parameters, emit_generated_quantities, pstream);\\n }\\n template \\n inline T_ log_prob(Eigen::Matrix& params_r,\\n std::ostream* pstream = nullptr) const {\\n Eigen::Matrix params_i;\\n return log_prob_impl(params_r, params_i, pstream);\\n }\\n template \\n inline T__ log_prob(std::vector& params_r,\\n std::vector& params_i,\\n std::ostream* pstream = nullptr) const {\\n return log_prob_impl(params_r, params_i, pstream);\\n }\\n \\n inline void transform_inits(const stan::io::var_context& context,\\n Eigen::Matrix& params_r,\\n std::ostream* pstream = nullptr) const final {\\n std::vector params_r_vec(params_r.size());\\n std::vector params_i;\\n transform_inits_impl(context, params_i, params_r_vec, pstream);\\n params_r.resize(params_r_vec.size());\\n for (int i = 0; i < params_r.size(); ++i) {\\n params_r.coeffRef(i) = params_r_vec[i];\\n }\\n }\\n inline void transform_inits(const stan::io::var_context& context,\\n std::vector& params_i,\\n std::vector& vars,\\n std::ostream* pstream = nullptr) const final {\\n transform_inits_impl(context, params_i, vars, pstream);\\n } \\n};\\n}\\nusing stan_model = model100b163de462a__namespace::model100b163de462a_;\\n#ifndef USING_R\\n// Boilerplate\\nstan::model::model_base& new_model(\\n stan::io::var_context& data_context,\\n unsigned int seed,\\n std::ostream* msg_stream) {\\n stan_model* m = new stan_model(data_context, seed, msg_stream);\\n return *m;\\n}\\nstan::math::profile_map& get_stan_profile_data() {\\n return model100b163de462a__namespace::profiles__;\\n}\\n#endif\\n#endif\"]}]},\"mk_cppmodule\":{\"type\":\"closure\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"object\",\"\"]}},\"value\":[{\"type\":\"symbol\",\"attributes\":{},\"value\":[\"\"]},{\"type\":\"language\",\"attributes\":{},\"value\":[\"{\",\" prep_call_sampler(object)\",\" model_cppname <- object@model_cpp$model_cppname\",\" mod <- get(\\\"module\\\", envir = object@dso@.CXXDSOMISC, inherits = FALSE)\",\" eval(call(\\\"$\\\", mod, paste(\\\"stan_fit4\\\", model_cppname, sep = \\\"\\\")))\",\"}\"]}]},\"dso\":{\"type\":\"S4\",\"attributes\":{\"sig\":{\"type\":\"list\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"file100b14986f75f\"]}},\"value\":[{\"type\":\"character\",\"attributes\":{},\"value\":[]}]},\"dso_saved\":{\"type\":\"logical\",\"attributes\":{},\"value\":[true]},\"dso_filename\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"file100b14986f75f\"]},\"modulename\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"stan_fit4model100b163de462a__mod\"]},\"system\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"x86_64, linux-gnu\"]},\"cxxflags\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"CXXFLAGS = -g -O2 -ffile-prefix-map=/build/r-base-LhKvHL/r-base-4.2.3=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 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diff --git a/tests/testthat/extdata/model_tv_normal.RDS b/tests/testthat/testdata/model_tv_normal.RDS similarity index 100% rename from tests/testthat/extdata/model_tv_normal.RDS rename to tests/testthat/testdata/model_tv_normal.RDS diff --git a/tests/testthat/testdata/model_tv_normal.json b/tests/testthat/testdata/model_tv_normal.json new file mode 100644 index 00000000..9229f167 --- /dev/null +++ b/tests/testthat/testdata/model_tv_normal.json @@ -0,0 +1 @@ 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sampling\"]},{\"type\":\"list\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"adapt_delta\",\"max_treedepth\"]}},\"value\":[{\"type\":\"double\",\"attributes\":{},\"value\":[0.9]},{\"type\":\"double\",\"attributes\":{},\"value\":[10]}]}]}]},\"stanmodel\":{\"type\":\"S4\",\"attributes\":{\"model_name\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"anon_model\"]},\"model_code\":{\"type\":\"character\",\"attributes\":{\"model_name2\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"tv_normal\"]}},\"value\":[\"data {\\n int Nobs;\\n int Npos[Nobs];\\n int Ntotal[Nobs];\\n int Age[Nobs];\\n int Ymax;\\n matrix[Nobs, Ymax] AgeExpoMatrix;\\n}\\nparameters {\\n row_vector[Ymax] foi;\\n real sigma;\\n}\\ntransformed parameters {\\n real P[Nobs];\\n real ScalerDotProduct[Nobs];\\n\\n for (i in 1:Nobs){\\n ScalerDotProduct[i] = dot_product(AgeExpoMatrix[i,], foi);\\n P[i] = 1 - exp(-ScalerDotProduct[i]);\\n }\\n}\\nmodel {\\n for (i in 1:Nobs)\\n Npos[i] ~ binomial(Ntotal[i], P[i]) ;\\n sigma ~ cauchy(0, 1);\\n\\n for(i in 2:Ymax)\\n foi[i] ~ normal(foi[i - 1], sigma);\\n foi[1] ~ normal(0, 1);\\n}\\ngenerated quantities{\\n vector[Nobs] Npos_sim;\\n vector[Nobs] P_sim;\\n vector[Nobs] logLikelihood;\\n for(i in 1:Nobs){\\n Npos_sim[i] = binomial_rng(Ntotal[i], P[i]);\\n P_sim[i] = Npos_sim[i] / Ntotal[i];\\n logLikelihood[i] = binomial_lpmf(Npos[i] | Ntotal[i], P[i]);\\n }\\n}\"]},\"model_cpp\":{\"type\":\"list\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"model_cppname\",\"model_cppcode\"]}},\"value\":[{\"type\":\"character\",\"attributes\":{},\"value\":[\"model4d28376ce589_\"]},{\"type\":\"character\",\"attributes\":{},\"value\":[\"#ifndef MODELS_HPP\\n#define MODELS_HPP\\n#define STAN__SERVICES__COMMAND_HPP\\n#include \\n#ifndef USE_STANC3\\n#define USE_STANC3\\n#endif\\n// Code generated by %%NAME%% %%VERSION%%\\n#include \\nnamespace model4d28376ce589__namespace {\\ninline void validate_positive_index(const char* var_name, const char* expr,\\n int val) {\\n if (val < 1) {\\n std::stringstream msg;\\n msg << \\\"Found dimension size less than one in simplex declaration\\\"\\n << \\\"; variable=\\\" << var_name << \\\"; dimension size expression=\\\" << expr\\n << \\\"; expression value=\\\" << val;\\n std::string msg_str(msg.str());\\n throw std::invalid_argument(msg_str.c_str());\\n }\\n}\\ninline void validate_unit_vector_index(const char* var_name, const char* expr,\\n int val) {\\n if (val <= 1) {\\n std::stringstream msg;\\n if (val == 1) {\\n msg << \\\"Found dimension size one in unit vector declaration.\\\"\\n << \\\" One-dimensional unit vector is discrete\\\"\\n << \\\" but the target distribution must be continuous.\\\"\\n << \\\" variable=\\\" << var_name << \\\"; dimension size expression=\\\" << expr;\\n } else {\\n msg << \\\"Found dimension size less than one in unit vector declaration\\\"\\n << \\\"; variable=\\\" << var_name << \\\"; dimension size expression=\\\" << expr\\n << \\\"; expression value=\\\" << val;\\n }\\n std::string msg_str(msg.str());\\n throw std::invalid_argument(msg_str.c_str());\\n }\\n}\\nusing std::istream;\\nusing std::string;\\nusing std::stringstream;\\nusing std::vector;\\nusing std::pow;\\nusing stan::io::dump;\\nusing stan::math::lgamma;\\nusing stan::model::model_base_crtp;\\nusing stan::model::rvalue;\\nusing stan::model::cons_list;\\nusing stan::model::index_uni;\\nusing stan::model::index_max;\\nusing stan::model::index_min;\\nusing stan::model::index_min_max;\\nusing stan::model::index_multi;\\nusing stan::model::index_omni;\\nusing stan::model::nil_index_list;\\nusing namespace stan::math;\\nusing stan::math::pow; \\nstan::math::profile_map profiles__;\\nstatic int current_statement__= 0;\\nstatic const std::vector locations_array__ = {\\\" (found before start of program)\\\",\\n \\\" (in 'anon_model', line 10, column 3 to column 33)\\\",\\n \\\" (in 'anon_model', line 11, column 3 to column 23)\\\",\\n \\\" (in 'anon_model', line 14, column 2 to column 15)\\\",\\n \\\" (in 'anon_model', line 15, column 2 to column 30)\\\",\\n \\\" (in 'anon_model', line 18, column 3 to column 61)\\\",\\n \\\" (in 'anon_model', line 19, column 3 to column 40)\\\",\\n \\\" (in 'anon_model', line 17, column 18 to line 20, column 2)\\\",\\n \\\" (in 'anon_model', line 17, column 1 to line 20, column 2)\\\",\\n \\\" (in 'anon_model', line 32, column 2 to column 24)\\\",\\n \\\" (in 'anon_model', line 33, column 2 to column 21)\\\",\\n \\\" (in 'anon_model', line 34, column 2 to column 29)\\\",\\n \\\" (in 'anon_model', line 36, column 4 to column 48)\\\",\\n \\\" (in 'anon_model', line 37, column 4 to column 39)\\\",\\n \\\" (in 'anon_model', line 38, column 4 to column 64)\\\",\\n \\\" (in 'anon_model', line 35, column 18 to line 39, column 3)\\\",\\n \\\" (in 'anon_model', line 35, column 2 to line 39, column 3)\\\",\\n \\\" (in 'anon_model', line 24, column 4 to column 41)\\\",\\n \\\" (in 'anon_model', line 23, column 2 to line 24, column 41)\\\",\\n \\\" (in 'anon_model', line 25, column 4 to column 25)\\\",\\n \\\" (in 'anon_model', line 28, column 4 to column 39)\\\",\\n \\\" (in 'anon_model', line 27, column 2 to line 28, column 39)\\\",\\n \\\" (in 'anon_model', line 29, column 2 to column 24)\\\",\\n \\\" (in 'anon_model', line 2, column 5 to column 23)\\\",\\n \\\" (in 'anon_model', line 3, column 14 to column 18)\\\",\\n \\\" (in 'anon_model', line 3, column 5 to column 20)\\\",\\n \\\" (in 'anon_model', line 4, column 16 to column 20)\\\",\\n \\\" (in 'anon_model', line 4, column 5 to column 22)\\\",\\n \\\" (in 'anon_model', line 5, column 13 to column 17)\\\",\\n \\\" (in 'anon_model', line 5, column 5 to column 19)\\\",\\n \\\" (in 'anon_model', line 6, column 5 to column 23)\\\",\\n \\\" (in 'anon_model', line 7, column 12 to column 16)\\\",\\n \\\" (in 'anon_model', line 7, column 18 to column 22)\\\",\\n \\\" (in 'anon_model', line 7, column 5 to column 38)\\\",\\n \\\" (in 'anon_model', line 10, column 23 to column 27)\\\",\\n \\\" (in 'anon_model', line 14, column 9 to column 13)\\\",\\n \\\" (in 'anon_model', line 15, column 24 to column 28)\\\",\\n \\\" (in 'anon_model', line 32, column 9 to column 13)\\\",\\n \\\" (in 'anon_model', line 33, column 9 to column 13)\\\",\\n \\\" (in 'anon_model', line 34, column 9 to column 13)\\\"};\\nclass model4d28376ce589_ final : public model_base_crtp {\\nprivate:\\n int Nobs;\\n std::vector Npos;\\n std::vector Ntotal;\\n std::vector Age;\\n int Ymax;\\n Eigen::Matrix AgeExpoMatrix;\\n \\npublic:\\n ~model4d28376ce589_() { }\\n \\n inline std::string model_name() const final { return \\\"model4d28376ce589_\\\"; }\\n inline std::vector model_compile_info() const noexcept {\\n return std::vector{\\\"stanc_version = %%NAME%%3 %%VERSION%%\\\", \\\"stancflags = \\\"};\\n }\\n \\n \\n model4d28376ce589_(stan::io::var_context& context__,\\n unsigned int random_seed__ = 0,\\n std::ostream* pstream__ = nullptr) : model_base_crtp(0) {\\n using local_scalar_t__ = double ;\\n boost::ecuyer1988 base_rng__ = \\n stan::services::util::create_rng(random_seed__, 0);\\n (void) base_rng__; // suppress unused var warning\\n static const char* function__ = \\\"model4d28376ce589__namespace::model4d28376ce589_\\\";\\n (void) function__; // suppress unused var warning\\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());\\n (void) DUMMY_VAR__; // suppress unused var warning\\n \\n try {\\n int pos__;\\n pos__ = std::numeric_limits::min();\\n \\n pos__ = 1;\\n current_statement__ = 23;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Nobs\\\",\\\"int\\\",\\n context__.to_vec());\\n Nobs = std::numeric_limits::min();\\n \\n current_statement__ = 23;\\n Nobs = context__.vals_i(\\\"Nobs\\\")[(1 - 1)];\\n current_statement__ = 23;\\n current_statement__ = 23;\\n check_greater_or_equal(function__, \\\"Nobs\\\", Nobs, 0);\\n current_statement__ = 24;\\n validate_non_negative_index(\\\"Npos\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 25;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Npos\\\",\\\"int\\\",\\n context__.to_vec(Nobs));\\n Npos = std::vector(Nobs, std::numeric_limits::min());\\n \\n current_statement__ = 25;\\n assign(Npos, nil_index_list(), context__.vals_i(\\\"Npos\\\"),\\n \\\"assigning variable Npos\\\");\\n current_statement__ = 26;\\n validate_non_negative_index(\\\"Ntotal\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 27;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Ntotal\\\",\\\"int\\\",\\n context__.to_vec(Nobs));\\n Ntotal = std::vector(Nobs, std::numeric_limits::min());\\n \\n current_statement__ = 27;\\n assign(Ntotal, nil_index_list(), context__.vals_i(\\\"Ntotal\\\"),\\n \\\"assigning variable Ntotal\\\");\\n current_statement__ = 28;\\n validate_non_negative_index(\\\"Age\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 29;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Age\\\",\\\"int\\\",\\n context__.to_vec(Nobs));\\n Age = std::vector(Nobs, std::numeric_limits::min());\\n \\n current_statement__ = 29;\\n assign(Age, nil_index_list(), context__.vals_i(\\\"Age\\\"),\\n \\\"assigning variable Age\\\");\\n current_statement__ = 30;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Ymax\\\",\\\"int\\\",\\n context__.to_vec());\\n Ymax = std::numeric_limits::min();\\n \\n current_statement__ = 30;\\n Ymax = context__.vals_i(\\\"Ymax\\\")[(1 - 1)];\\n current_statement__ = 30;\\n current_statement__ = 30;\\n check_greater_or_equal(function__, \\\"Ymax\\\", Ymax, 1);\\n current_statement__ = 31;\\n validate_non_negative_index(\\\"AgeExpoMatrix\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 32;\\n validate_non_negative_index(\\\"AgeExpoMatrix\\\", \\\"Ymax\\\", Ymax);\\n current_statement__ = 33;\\n context__.validate_dims(\\\"data initialization\\\",\\\"AgeExpoMatrix\\\",\\\"double\\\",\\n context__.to_vec(Nobs, Ymax));\\n AgeExpoMatrix = Eigen::Matrix(Nobs, Ymax);\\n stan::math::fill(AgeExpoMatrix, std::numeric_limits::quiet_NaN());\\n \\n {\\n std::vector AgeExpoMatrix_flat__;\\n current_statement__ = 33;\\n assign(AgeExpoMatrix_flat__, nil_index_list(),\\n context__.vals_r(\\\"AgeExpoMatrix\\\"),\\n \\\"assigning variable AgeExpoMatrix_flat__\\\");\\n current_statement__ = 33;\\n pos__ = 1;\\n current_statement__ = 33;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 33;\\n for (int sym2__ = 1; sym2__ <= Nobs; ++sym2__) {\\n current_statement__ = 33;\\n assign(AgeExpoMatrix,\\n cons_list(index_uni(sym2__),\\n cons_list(index_uni(sym1__), nil_index_list())),\\n AgeExpoMatrix_flat__[(pos__ - 1)],\\n \\\"assigning variable AgeExpoMatrix\\\");\\n current_statement__ = 33;\\n pos__ = (pos__ + 1);}}\\n }\\n current_statement__ = 34;\\n validate_non_negative_index(\\\"foi\\\", \\\"Ymax\\\", Ymax);\\n current_statement__ = 35;\\n validate_non_negative_index(\\\"P\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 36;\\n validate_non_negative_index(\\\"ScalerDotProduct\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 37;\\n validate_non_negative_index(\\\"Npos_sim\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 38;\\n validate_non_negative_index(\\\"P_sim\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 39;\\n validate_non_negative_index(\\\"logLikelihood\\\", \\\"Nobs\\\", Nobs);\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n num_params_r__ = 0U;\\n \\n try {\\n num_params_r__ += Ymax;\\n num_params_r__ += 1;\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n }\\n template * = nullptr, stan::require_vector_like_vt* = nullptr>\\n inline stan::scalar_type_t log_prob_impl(VecR& params_r__,\\n VecI& params_i__,\\n std::ostream* pstream__ = nullptr) const {\\n using T__ = stan::scalar_type_t;\\n using local_scalar_t__ = T__;\\n T__ lp__(0.0);\\n stan::math::accumulator lp_accum__;\\n static const char* function__ = \\\"model4d28376ce589__namespace::log_prob\\\";\\n(void) function__; // suppress unused var warning\\n stan::io::reader in__(params_r__, params_i__);\\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());\\n (void) DUMMY_VAR__; // suppress unused var warning\\n \\n try {\\n Eigen::Matrix foi;\\n foi = Eigen::Matrix(Ymax);\\n stan::math::fill(foi, DUMMY_VAR__);\\n \\n current_statement__ = 1;\\n foi = in__.row_vector(Ymax);\\n current_statement__ = 1;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 1;\\n if (jacobian__) {\\n current_statement__ = 1;\\n assign(foi, cons_list(index_uni(sym1__), nil_index_list()),\\n stan::math::lb_constrain(foi[(sym1__ - 1)], 0, lp__),\\n \\\"assigning variable foi\\\");\\n } else {\\n current_statement__ = 1;\\n assign(foi, cons_list(index_uni(sym1__), nil_index_list()),\\n stan::math::lb_constrain(foi[(sym1__ - 1)], 0),\\n \\\"assigning variable foi\\\");\\n }}\\n local_scalar_t__ sigma;\\n sigma = DUMMY_VAR__;\\n \\n current_statement__ = 2;\\n sigma = in__.scalar();\\n current_statement__ = 2;\\n if (jacobian__) {\\n current_statement__ = 2;\\n sigma = stan::math::lb_constrain(sigma, 0, lp__);\\n } else {\\n current_statement__ = 2;\\n sigma = stan::math::lb_constrain(sigma, 0);\\n }\\n std::vector P;\\n P = std::vector(Nobs, DUMMY_VAR__);\\n \\n std::vector ScalerDotProduct;\\n ScalerDotProduct = std::vector(Nobs, DUMMY_VAR__);\\n \\n current_statement__ = 8;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 5;\\n assign(ScalerDotProduct, cons_list(index_uni(i), nil_index_list()),\\n dot_product(\\n rvalue(AgeExpoMatrix,\\n cons_list(index_uni(i),\\n cons_list(index_omni(), nil_index_list())), \\\"AgeExpoMatrix\\\"),\\n foi), \\\"assigning variable ScalerDotProduct\\\");\\n current_statement__ = 6;\\n assign(P, cons_list(index_uni(i), nil_index_list()),\\n (1 - stan::math::exp(-ScalerDotProduct[(i - 1)])),\\n \\\"assigning variable P\\\");}\\n {\\n current_statement__ = 18;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 17;\\n lp_accum__.add(\\n binomial_lpmf(Npos[(i - 1)], Ntotal[(i - 1)],\\n P[(i - 1)]));}\\n current_statement__ = 19;\\n lp_accum__.add(cauchy_lpdf(sigma, 0, 1));\\n current_statement__ = 21;\\n for (int i = 2; i <= Ymax; ++i) {\\n current_statement__ = 20;\\n lp_accum__.add(\\n normal_lpdf(foi[(i - 1)], foi[((i - 1) - 1)], sigma));}\\n current_statement__ = 22;\\n lp_accum__.add(normal_lpdf(foi[(1 - 1)], 0, 1));\\n }\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n lp_accum__.add(lp__);\\n return lp_accum__.sum();\\n } // log_prob_impl() \\n \\n template * = nullptr, stan::require_vector_like_vt* = nullptr, stan::require_std_vector_vt* = nullptr>\\n inline void write_array_impl(RNG& base_rng__, VecR& params_r__,\\n VecI& params_i__, VecVar& vars__,\\n const bool emit_transformed_parameters__ = true,\\n const bool emit_generated_quantities__ = true,\\n std::ostream* pstream__ = nullptr) const {\\n using local_scalar_t__ = double;\\n vars__.resize(0);\\n stan::io::reader in__(params_r__, params_i__);\\n static const char* function__ = \\\"model4d28376ce589__namespace::write_array\\\";\\n(void) function__; // suppress unused var warning\\n (void) function__; // suppress unused var warning\\n double lp__ = 0.0;\\n (void) lp__; // dummy to suppress unused var warning\\n stan::math::accumulator lp_accum__;\\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());\\n (void) DUMMY_VAR__; // suppress unused var warning\\n \\n try {\\n Eigen::Matrix foi;\\n foi = Eigen::Matrix(Ymax);\\n stan::math::fill(foi, std::numeric_limits::quiet_NaN());\\n \\n current_statement__ = 1;\\n foi = in__.row_vector(Ymax);\\n current_statement__ = 1;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 1;\\n assign(foi, cons_list(index_uni(sym1__), nil_index_list()),\\n stan::math::lb_constrain(foi[(sym1__ - 1)], 0),\\n \\\"assigning variable foi\\\");}\\n double sigma;\\n sigma = std::numeric_limits::quiet_NaN();\\n \\n current_statement__ = 2;\\n sigma = in__.scalar();\\n current_statement__ = 2;\\n sigma = stan::math::lb_constrain(sigma, 0);\\n std::vector P;\\n P = std::vector(Nobs, std::numeric_limits::quiet_NaN());\\n \\n std::vector ScalerDotProduct;\\n ScalerDotProduct = std::vector(Nobs, std::numeric_limits::quiet_NaN());\\n \\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n vars__.emplace_back(foi[(sym1__ - 1)]);}\\n vars__.emplace_back(sigma);\\n if (logical_negation((primitive_value(emit_transformed_parameters__) ||\\n primitive_value(emit_generated_quantities__)))) {\\n return ;\\n } \\n current_statement__ = 8;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 5;\\n assign(ScalerDotProduct, cons_list(index_uni(i), nil_index_list()),\\n dot_product(\\n rvalue(AgeExpoMatrix,\\n cons_list(index_uni(i),\\n cons_list(index_omni(), nil_index_list())), \\\"AgeExpoMatrix\\\"),\\n foi), \\\"assigning variable ScalerDotProduct\\\");\\n current_statement__ = 6;\\n assign(P, cons_list(index_uni(i), nil_index_list()),\\n (1 - stan::math::exp(-ScalerDotProduct[(i - 1)])),\\n \\\"assigning variable P\\\");}\\n if (emit_transformed_parameters__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(P[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(ScalerDotProduct[(sym1__ - 1)]);}\\n } \\n if (logical_negation(emit_generated_quantities__)) {\\n return ;\\n } \\n Eigen::Matrix Npos_sim;\\n Npos_sim = Eigen::Matrix(Nobs);\\n stan::math::fill(Npos_sim, std::numeric_limits::quiet_NaN());\\n \\n Eigen::Matrix P_sim;\\n P_sim = Eigen::Matrix(Nobs);\\n stan::math::fill(P_sim, std::numeric_limits::quiet_NaN());\\n \\n Eigen::Matrix logLikelihood;\\n logLikelihood = Eigen::Matrix(Nobs);\\n stan::math::fill(logLikelihood, std::numeric_limits::quiet_NaN());\\n \\n current_statement__ = 16;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 12;\\n assign(Npos_sim, cons_list(index_uni(i), nil_index_list()),\\n binomial_rng(Ntotal[(i - 1)], P[(i - 1)], base_rng__),\\n \\\"assigning variable Npos_sim\\\");\\n current_statement__ = 13;\\n assign(P_sim, cons_list(index_uni(i), nil_index_list()),\\n (Npos_sim[(i - 1)] / Ntotal[(i - 1)]), \\\"assigning variable P_sim\\\");\\n current_statement__ = 14;\\n assign(logLikelihood, cons_list(index_uni(i), nil_index_list()),\\n binomial_lpmf(Npos[(i - 1)], Ntotal[(i - 1)], P[(i - 1)]),\\n \\\"assigning variable logLikelihood\\\");}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(Npos_sim[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(P_sim[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(logLikelihood[(sym1__ - 1)]);}\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n } // write_array_impl() \\n \\n template * = nullptr, stan::require_vector_like_vt* = nullptr>\\n inline void transform_inits_impl(const stan::io::var_context& context__,\\n VecI& params_i__, VecVar& vars__,\\n std::ostream* pstream__ = nullptr) const {\\n using local_scalar_t__ = double;\\n vars__.clear();\\n vars__.reserve(num_params_r__);\\n \\n try {\\n int pos__;\\n pos__ = std::numeric_limits::min();\\n \\n pos__ = 1;\\n Eigen::Matrix foi;\\n foi = Eigen::Matrix(Ymax);\\n stan::math::fill(foi, std::numeric_limits::quiet_NaN());\\n \\n {\\n std::vector foi_flat__;\\n current_statement__ = 1;\\n assign(foi_flat__, nil_index_list(), context__.vals_r(\\\"foi\\\"),\\n \\\"assigning variable foi_flat__\\\");\\n current_statement__ = 1;\\n pos__ = 1;\\n current_statement__ = 1;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 1;\\n assign(foi, cons_list(index_uni(sym1__), nil_index_list()),\\n foi_flat__[(pos__ - 1)], \\\"assigning variable foi\\\");\\n current_statement__ = 1;\\n pos__ = (pos__ + 1);}\\n }\\n Eigen::Matrix foi_free__;\\n foi_free__ = Eigen::Matrix(Ymax);\\n stan::math::fill(foi_free__, std::numeric_limits::quiet_NaN());\\n \\n current_statement__ = 1;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 1;\\n assign(foi_free__, cons_list(index_uni(sym1__), nil_index_list()),\\n stan::math::lb_free(foi[(sym1__ - 1)], 0),\\n \\\"assigning variable foi_free__\\\");}\\n double sigma;\\n sigma = std::numeric_limits::quiet_NaN();\\n \\n current_statement__ = 2;\\n sigma = context__.vals_r(\\\"sigma\\\")[(1 - 1)];\\n double sigma_free__;\\n sigma_free__ = std::numeric_limits::quiet_NaN();\\n \\n current_statement__ = 2;\\n sigma_free__ = stan::math::lb_free(sigma, 0);\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n vars__.emplace_back(foi_free__[(sym1__ - 1)]);}\\n vars__.emplace_back(sigma_free__);\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n } // transform_inits_impl() \\n \\n inline void get_param_names(std::vector& names__) const {\\n \\n names__.clear();\\n names__.emplace_back(\\\"foi\\\");\\n names__.emplace_back(\\\"sigma\\\");\\n names__.emplace_back(\\\"P\\\");\\n names__.emplace_back(\\\"ScalerDotProduct\\\");\\n names__.emplace_back(\\\"Npos_sim\\\");\\n names__.emplace_back(\\\"P_sim\\\");\\n names__.emplace_back(\\\"logLikelihood\\\");\\n } // get_param_names() \\n \\n inline void get_dims(std::vector>& dimss__) const {\\n dimss__.clear();\\n dimss__.emplace_back(std::vector{static_cast(Ymax)});\\n \\n dimss__.emplace_back(std::vector{});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n } // get_dims() \\n \\n inline void constrained_param_names(\\n std::vector& param_names__,\\n bool emit_transformed_parameters__ = true,\\n bool emit_generated_quantities__ = true) const\\n final {\\n \\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"foi\\\" + '.' + std::to_string(sym1__));\\n }}\\n param_names__.emplace_back(std::string() + \\\"sigma\\\");\\n if (emit_transformed_parameters__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"ScalerDotProduct\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n if (emit_generated_quantities__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"Npos_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"logLikelihood\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n } // constrained_param_names() \\n \\n inline void unconstrained_param_names(\\n std::vector& param_names__,\\n bool emit_transformed_parameters__ = true,\\n bool emit_generated_quantities__ = true) const\\n final {\\n \\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"foi\\\" + '.' + std::to_string(sym1__));\\n }}\\n param_names__.emplace_back(std::string() + \\\"sigma\\\");\\n if (emit_transformed_parameters__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"ScalerDotProduct\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n if (emit_generated_quantities__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"Npos_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"logLikelihood\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n } // unconstrained_param_names() \\n \\n inline std::string get_constrained_sizedtypes() const {\\n stringstream s__;\\n s__ << \\\"[{\\\\\\\"name\\\\\\\":\\\\\\\"foi\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Ymax << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"sigma\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"ScalerDotProduct\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"Npos_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"logLikelihood\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"}]\\\";\\n return s__.str();\\n } // get_constrained_sizedtypes() \\n \\n inline std::string get_unconstrained_sizedtypes() const {\\n stringstream s__;\\n s__ << \\\"[{\\\\\\\"name\\\\\\\":\\\\\\\"foi\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Ymax << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"sigma\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"ScalerDotProduct\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"Npos_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"logLikelihood\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"}]\\\";\\n return s__.str();\\n } // get_unconstrained_sizedtypes() \\n \\n \\n // Begin method overload boilerplate\\n template \\n inline void write_array(RNG& base_rng,\\n Eigen::Matrix& params_r,\\n Eigen::Matrix& vars,\\n const bool emit_transformed_parameters = true,\\n const bool emit_generated_quantities = true,\\n std::ostream* pstream = nullptr) const {\\n std::vector vars_vec(vars.size());\\n std::vector params_i;\\n write_array_impl(base_rng, params_r, params_i, vars_vec,\\n emit_transformed_parameters, emit_generated_quantities, pstream);\\n vars.resize(vars_vec.size());\\n for (int i = 0; i < vars.size(); ++i) {\\n vars.coeffRef(i) = vars_vec[i];\\n }\\n }\\n template \\n inline void write_array(RNG& base_rng, std::vector& params_r,\\n std::vector& params_i,\\n std::vector& vars,\\n bool emit_transformed_parameters = true,\\n bool emit_generated_quantities = true,\\n std::ostream* pstream = nullptr) const {\\n write_array_impl(base_rng, params_r, params_i, vars, emit_transformed_parameters, emit_generated_quantities, pstream);\\n }\\n template \\n inline T_ log_prob(Eigen::Matrix& params_r,\\n std::ostream* pstream = nullptr) const {\\n Eigen::Matrix params_i;\\n return log_prob_impl(params_r, params_i, pstream);\\n }\\n template \\n inline T__ log_prob(std::vector& params_r,\\n std::vector& params_i,\\n std::ostream* pstream = nullptr) const {\\n return log_prob_impl(params_r, params_i, pstream);\\n }\\n \\n inline void transform_inits(const stan::io::var_context& context,\\n Eigen::Matrix& params_r,\\n std::ostream* pstream = nullptr) const final {\\n std::vector params_r_vec(params_r.size());\\n std::vector params_i;\\n transform_inits_impl(context, params_i, params_r_vec, pstream);\\n params_r.resize(params_r_vec.size());\\n for (int i = 0; i < params_r.size(); ++i) {\\n params_r.coeffRef(i) = params_r_vec[i];\\n }\\n }\\n inline void transform_inits(const stan::io::var_context& context,\\n std::vector& params_i,\\n std::vector& vars,\\n std::ostream* pstream = nullptr) const final {\\n transform_inits_impl(context, params_i, vars, pstream);\\n } \\n};\\n}\\nusing stan_model = model4d28376ce589__namespace::model4d28376ce589_;\\n#ifndef USING_R\\n// Boilerplate\\nstan::model::model_base& new_model(\\n stan::io::var_context& data_context,\\n unsigned int seed,\\n std::ostream* msg_stream) {\\n stan_model* m = new stan_model(data_context, seed, msg_stream);\\n return *m;\\n}\\nstan::math::profile_map& get_stan_profile_data() {\\n return model4d28376ce589__namespace::profiles__;\\n}\\n#endif\\n#endif\"]}]},\"mk_cppmodule\":{\"type\":\"closure\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"object\",\"\"]}},\"value\":[{\"type\":\"symbol\",\"attributes\":{},\"value\":[\"\"]},{\"type\":\"language\",\"attributes\":{},\"value\":[\"{\",\" prep_call_sampler(object)\",\" model_cppname <- object@model_cpp$model_cppname\",\" mod <- get(\\\"module\\\", envir = object@dso@.CXXDSOMISC, inherits = FALSE)\",\" eval(call(\\\"$\\\", mod, paste(\\\"stan_fit4\\\", model_cppname, sep = \\\"\\\")))\",\"}\"]}]},\"dso\":{\"type\":\"S4\",\"attributes\":{\"sig\":{\"type\":\"list\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"file4d285836cfc9\"]}},\"value\":[{\"type\":\"character\",\"attributes\":{},\"value\":[]}]},\"dso_saved\":{\"type\":\"logical\",\"attributes\":{},\"value\":[true]},\"dso_filename\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"file4d285836cfc9\"]},\"modulename\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"stan_fit4model4d28376ce589__mod\"]},\"system\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"x86_64, linux-gnu\"]},\"cxxflags\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"CXXFLAGS = -g -O2 -ffile-prefix-map=/build/r-base-LhKvHL/r-base-4.2.3=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 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diff --git a/tests/testthat/extdata/model_tv_normal_log.RDS b/tests/testthat/testdata/model_tv_normal_log.RDS similarity index 100% rename from tests/testthat/extdata/model_tv_normal_log.RDS rename to tests/testthat/testdata/model_tv_normal_log.RDS diff --git a/tests/testthat/testdata/model_tv_normal_log.json b/tests/testthat/testdata/model_tv_normal_log.json new file mode 100644 index 00000000..9229f167 --- /dev/null +++ b/tests/testthat/testdata/model_tv_normal_log.json @@ -0,0 +1 @@ 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sampling\"]},{\"type\":\"list\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"adapt_delta\",\"max_treedepth\"]}},\"value\":[{\"type\":\"double\",\"attributes\":{},\"value\":[0.9]},{\"type\":\"double\",\"attributes\":{},\"value\":[10]}]}]}]},\"stanmodel\":{\"type\":\"S4\",\"attributes\":{\"model_name\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"anon_model\"]},\"model_code\":{\"type\":\"character\",\"attributes\":{\"model_name2\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"tv_normal\"]}},\"value\":[\"data {\\n int Nobs;\\n int Npos[Nobs];\\n int Ntotal[Nobs];\\n int Age[Nobs];\\n int Ymax;\\n matrix[Nobs, Ymax] AgeExpoMatrix;\\n}\\nparameters {\\n row_vector[Ymax] foi;\\n real sigma;\\n}\\ntransformed parameters {\\n real P[Nobs];\\n real ScalerDotProduct[Nobs];\\n\\n for (i in 1:Nobs){\\n ScalerDotProduct[i] = dot_product(AgeExpoMatrix[i,], foi);\\n P[i] = 1 - exp(-ScalerDotProduct[i]);\\n }\\n}\\nmodel {\\n for (i in 1:Nobs)\\n Npos[i] ~ binomial(Ntotal[i], P[i]) ;\\n sigma ~ cauchy(0, 1);\\n\\n for(i in 2:Ymax)\\n foi[i] ~ normal(foi[i - 1], sigma);\\n foi[1] ~ normal(0, 1);\\n}\\ngenerated quantities{\\n vector[Nobs] Npos_sim;\\n vector[Nobs] P_sim;\\n vector[Nobs] logLikelihood;\\n for(i in 1:Nobs){\\n Npos_sim[i] = binomial_rng(Ntotal[i], P[i]);\\n P_sim[i] = Npos_sim[i] / Ntotal[i];\\n logLikelihood[i] = binomial_lpmf(Npos[i] | Ntotal[i], P[i]);\\n }\\n}\"]},\"model_cpp\":{\"type\":\"list\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"model_cppname\",\"model_cppcode\"]}},\"value\":[{\"type\":\"character\",\"attributes\":{},\"value\":[\"model4d28376ce589_\"]},{\"type\":\"character\",\"attributes\":{},\"value\":[\"#ifndef MODELS_HPP\\n#define MODELS_HPP\\n#define STAN__SERVICES__COMMAND_HPP\\n#include \\n#ifndef USE_STANC3\\n#define USE_STANC3\\n#endif\\n// Code generated by %%NAME%% %%VERSION%%\\n#include \\nnamespace model4d28376ce589__namespace {\\ninline void validate_positive_index(const char* var_name, const char* expr,\\n int val) {\\n if (val < 1) {\\n std::stringstream msg;\\n msg << \\\"Found dimension size less than one in simplex declaration\\\"\\n << \\\"; variable=\\\" << var_name << \\\"; dimension size expression=\\\" << expr\\n << \\\"; expression value=\\\" << val;\\n std::string msg_str(msg.str());\\n throw std::invalid_argument(msg_str.c_str());\\n }\\n}\\ninline void validate_unit_vector_index(const char* var_name, const char* expr,\\n int val) {\\n if (val <= 1) {\\n std::stringstream msg;\\n if (val == 1) {\\n msg << \\\"Found dimension size one in unit vector declaration.\\\"\\n << \\\" One-dimensional unit vector is discrete\\\"\\n << \\\" but the target distribution must be continuous.\\\"\\n << \\\" variable=\\\" << var_name << \\\"; dimension size expression=\\\" << expr;\\n } else {\\n msg << \\\"Found dimension size less than one in unit vector declaration\\\"\\n << \\\"; variable=\\\" << var_name << \\\"; dimension size expression=\\\" << expr\\n << \\\"; expression value=\\\" << val;\\n }\\n std::string msg_str(msg.str());\\n throw std::invalid_argument(msg_str.c_str());\\n }\\n}\\nusing std::istream;\\nusing std::string;\\nusing std::stringstream;\\nusing std::vector;\\nusing std::pow;\\nusing stan::io::dump;\\nusing stan::math::lgamma;\\nusing stan::model::model_base_crtp;\\nusing stan::model::rvalue;\\nusing stan::model::cons_list;\\nusing stan::model::index_uni;\\nusing stan::model::index_max;\\nusing stan::model::index_min;\\nusing stan::model::index_min_max;\\nusing stan::model::index_multi;\\nusing stan::model::index_omni;\\nusing stan::model::nil_index_list;\\nusing namespace stan::math;\\nusing stan::math::pow; \\nstan::math::profile_map profiles__;\\nstatic int current_statement__= 0;\\nstatic const std::vector locations_array__ = {\\\" (found before start of program)\\\",\\n \\\" (in 'anon_model', line 10, column 3 to column 33)\\\",\\n \\\" (in 'anon_model', line 11, column 3 to column 23)\\\",\\n \\\" (in 'anon_model', line 14, column 2 to column 15)\\\",\\n \\\" (in 'anon_model', line 15, column 2 to column 30)\\\",\\n \\\" (in 'anon_model', line 18, column 3 to column 61)\\\",\\n \\\" (in 'anon_model', line 19, column 3 to column 40)\\\",\\n \\\" (in 'anon_model', line 17, column 18 to line 20, column 2)\\\",\\n \\\" (in 'anon_model', line 17, column 1 to line 20, column 2)\\\",\\n \\\" (in 'anon_model', line 32, column 2 to column 24)\\\",\\n \\\" (in 'anon_model', line 33, column 2 to column 21)\\\",\\n \\\" (in 'anon_model', line 34, column 2 to column 29)\\\",\\n \\\" (in 'anon_model', line 36, column 4 to column 48)\\\",\\n \\\" (in 'anon_model', line 37, column 4 to column 39)\\\",\\n \\\" (in 'anon_model', line 38, column 4 to column 64)\\\",\\n \\\" (in 'anon_model', line 35, column 18 to line 39, column 3)\\\",\\n \\\" (in 'anon_model', line 35, column 2 to line 39, column 3)\\\",\\n \\\" (in 'anon_model', line 24, column 4 to column 41)\\\",\\n \\\" (in 'anon_model', line 23, column 2 to line 24, column 41)\\\",\\n \\\" (in 'anon_model', line 25, column 4 to column 25)\\\",\\n \\\" (in 'anon_model', line 28, column 4 to column 39)\\\",\\n \\\" (in 'anon_model', line 27, column 2 to line 28, column 39)\\\",\\n \\\" (in 'anon_model', line 29, column 2 to column 24)\\\",\\n \\\" (in 'anon_model', line 2, column 5 to column 23)\\\",\\n \\\" (in 'anon_model', line 3, column 14 to column 18)\\\",\\n \\\" (in 'anon_model', line 3, column 5 to column 20)\\\",\\n \\\" (in 'anon_model', line 4, column 16 to column 20)\\\",\\n \\\" (in 'anon_model', line 4, column 5 to column 22)\\\",\\n \\\" (in 'anon_model', line 5, column 13 to column 17)\\\",\\n \\\" (in 'anon_model', line 5, column 5 to column 19)\\\",\\n \\\" (in 'anon_model', line 6, column 5 to column 23)\\\",\\n \\\" (in 'anon_model', line 7, column 12 to column 16)\\\",\\n \\\" (in 'anon_model', line 7, column 18 to column 22)\\\",\\n \\\" (in 'anon_model', line 7, column 5 to column 38)\\\",\\n \\\" (in 'anon_model', line 10, column 23 to column 27)\\\",\\n \\\" (in 'anon_model', line 14, column 9 to column 13)\\\",\\n \\\" (in 'anon_model', line 15, column 24 to column 28)\\\",\\n \\\" (in 'anon_model', line 32, column 9 to column 13)\\\",\\n \\\" (in 'anon_model', line 33, column 9 to column 13)\\\",\\n \\\" (in 'anon_model', line 34, column 9 to column 13)\\\"};\\nclass model4d28376ce589_ final : public model_base_crtp {\\nprivate:\\n int Nobs;\\n std::vector Npos;\\n std::vector Ntotal;\\n std::vector Age;\\n int Ymax;\\n Eigen::Matrix AgeExpoMatrix;\\n \\npublic:\\n ~model4d28376ce589_() { }\\n \\n inline std::string model_name() const final { return \\\"model4d28376ce589_\\\"; }\\n inline std::vector model_compile_info() const noexcept {\\n return std::vector{\\\"stanc_version = %%NAME%%3 %%VERSION%%\\\", \\\"stancflags = \\\"};\\n }\\n \\n \\n model4d28376ce589_(stan::io::var_context& context__,\\n unsigned int random_seed__ = 0,\\n std::ostream* pstream__ = nullptr) : model_base_crtp(0) {\\n using local_scalar_t__ = double ;\\n boost::ecuyer1988 base_rng__ = \\n stan::services::util::create_rng(random_seed__, 0);\\n (void) base_rng__; // suppress unused var warning\\n static const char* function__ = \\\"model4d28376ce589__namespace::model4d28376ce589_\\\";\\n (void) function__; // suppress unused var warning\\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());\\n (void) DUMMY_VAR__; // suppress unused var warning\\n \\n try {\\n int pos__;\\n pos__ = std::numeric_limits::min();\\n \\n pos__ = 1;\\n current_statement__ = 23;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Nobs\\\",\\\"int\\\",\\n context__.to_vec());\\n Nobs = std::numeric_limits::min();\\n \\n current_statement__ = 23;\\n Nobs = context__.vals_i(\\\"Nobs\\\")[(1 - 1)];\\n current_statement__ = 23;\\n current_statement__ = 23;\\n check_greater_or_equal(function__, \\\"Nobs\\\", Nobs, 0);\\n current_statement__ = 24;\\n validate_non_negative_index(\\\"Npos\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 25;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Npos\\\",\\\"int\\\",\\n context__.to_vec(Nobs));\\n Npos = std::vector(Nobs, std::numeric_limits::min());\\n \\n current_statement__ = 25;\\n assign(Npos, nil_index_list(), context__.vals_i(\\\"Npos\\\"),\\n \\\"assigning variable Npos\\\");\\n current_statement__ = 26;\\n validate_non_negative_index(\\\"Ntotal\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 27;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Ntotal\\\",\\\"int\\\",\\n context__.to_vec(Nobs));\\n Ntotal = std::vector(Nobs, std::numeric_limits::min());\\n \\n current_statement__ = 27;\\n assign(Ntotal, nil_index_list(), context__.vals_i(\\\"Ntotal\\\"),\\n \\\"assigning variable Ntotal\\\");\\n current_statement__ = 28;\\n validate_non_negative_index(\\\"Age\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 29;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Age\\\",\\\"int\\\",\\n context__.to_vec(Nobs));\\n Age = std::vector(Nobs, std::numeric_limits::min());\\n \\n current_statement__ = 29;\\n assign(Age, nil_index_list(), context__.vals_i(\\\"Age\\\"),\\n \\\"assigning variable Age\\\");\\n current_statement__ = 30;\\n context__.validate_dims(\\\"data initialization\\\",\\\"Ymax\\\",\\\"int\\\",\\n context__.to_vec());\\n Ymax = std::numeric_limits::min();\\n \\n current_statement__ = 30;\\n Ymax = context__.vals_i(\\\"Ymax\\\")[(1 - 1)];\\n current_statement__ = 30;\\n current_statement__ = 30;\\n check_greater_or_equal(function__, \\\"Ymax\\\", Ymax, 1);\\n current_statement__ = 31;\\n validate_non_negative_index(\\\"AgeExpoMatrix\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 32;\\n validate_non_negative_index(\\\"AgeExpoMatrix\\\", \\\"Ymax\\\", Ymax);\\n current_statement__ = 33;\\n context__.validate_dims(\\\"data initialization\\\",\\\"AgeExpoMatrix\\\",\\\"double\\\",\\n context__.to_vec(Nobs, Ymax));\\n AgeExpoMatrix = Eigen::Matrix(Nobs, Ymax);\\n stan::math::fill(AgeExpoMatrix, std::numeric_limits::quiet_NaN());\\n \\n {\\n std::vector AgeExpoMatrix_flat__;\\n current_statement__ = 33;\\n assign(AgeExpoMatrix_flat__, nil_index_list(),\\n context__.vals_r(\\\"AgeExpoMatrix\\\"),\\n \\\"assigning variable AgeExpoMatrix_flat__\\\");\\n current_statement__ = 33;\\n pos__ = 1;\\n current_statement__ = 33;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 33;\\n for (int sym2__ = 1; sym2__ <= Nobs; ++sym2__) {\\n current_statement__ = 33;\\n assign(AgeExpoMatrix,\\n cons_list(index_uni(sym2__),\\n cons_list(index_uni(sym1__), nil_index_list())),\\n AgeExpoMatrix_flat__[(pos__ - 1)],\\n \\\"assigning variable AgeExpoMatrix\\\");\\n current_statement__ = 33;\\n pos__ = (pos__ + 1);}}\\n }\\n current_statement__ = 34;\\n validate_non_negative_index(\\\"foi\\\", \\\"Ymax\\\", Ymax);\\n current_statement__ = 35;\\n validate_non_negative_index(\\\"P\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 36;\\n validate_non_negative_index(\\\"ScalerDotProduct\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 37;\\n validate_non_negative_index(\\\"Npos_sim\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 38;\\n validate_non_negative_index(\\\"P_sim\\\", \\\"Nobs\\\", Nobs);\\n current_statement__ = 39;\\n validate_non_negative_index(\\\"logLikelihood\\\", \\\"Nobs\\\", Nobs);\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n num_params_r__ = 0U;\\n \\n try {\\n num_params_r__ += Ymax;\\n num_params_r__ += 1;\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n }\\n template * = nullptr, stan::require_vector_like_vt* = nullptr>\\n inline stan::scalar_type_t log_prob_impl(VecR& params_r__,\\n VecI& params_i__,\\n std::ostream* pstream__ = nullptr) const {\\n using T__ = stan::scalar_type_t;\\n using local_scalar_t__ = T__;\\n T__ lp__(0.0);\\n stan::math::accumulator lp_accum__;\\n static const char* function__ = \\\"model4d28376ce589__namespace::log_prob\\\";\\n(void) function__; // suppress unused var warning\\n stan::io::reader in__(params_r__, params_i__);\\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());\\n (void) DUMMY_VAR__; // suppress unused var warning\\n \\n try {\\n Eigen::Matrix foi;\\n foi = Eigen::Matrix(Ymax);\\n stan::math::fill(foi, DUMMY_VAR__);\\n \\n current_statement__ = 1;\\n foi = in__.row_vector(Ymax);\\n current_statement__ = 1;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 1;\\n if (jacobian__) {\\n current_statement__ = 1;\\n assign(foi, cons_list(index_uni(sym1__), nil_index_list()),\\n stan::math::lb_constrain(foi[(sym1__ - 1)], 0, lp__),\\n \\\"assigning variable foi\\\");\\n } else {\\n current_statement__ = 1;\\n assign(foi, cons_list(index_uni(sym1__), nil_index_list()),\\n stan::math::lb_constrain(foi[(sym1__ - 1)], 0),\\n \\\"assigning variable foi\\\");\\n }}\\n local_scalar_t__ sigma;\\n sigma = DUMMY_VAR__;\\n \\n current_statement__ = 2;\\n sigma = in__.scalar();\\n current_statement__ = 2;\\n if (jacobian__) {\\n current_statement__ = 2;\\n sigma = stan::math::lb_constrain(sigma, 0, lp__);\\n } else {\\n current_statement__ = 2;\\n sigma = stan::math::lb_constrain(sigma, 0);\\n }\\n std::vector P;\\n P = std::vector(Nobs, DUMMY_VAR__);\\n \\n std::vector ScalerDotProduct;\\n ScalerDotProduct = std::vector(Nobs, DUMMY_VAR__);\\n \\n current_statement__ = 8;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 5;\\n assign(ScalerDotProduct, cons_list(index_uni(i), nil_index_list()),\\n dot_product(\\n rvalue(AgeExpoMatrix,\\n cons_list(index_uni(i),\\n cons_list(index_omni(), nil_index_list())), \\\"AgeExpoMatrix\\\"),\\n foi), \\\"assigning variable ScalerDotProduct\\\");\\n current_statement__ = 6;\\n assign(P, cons_list(index_uni(i), nil_index_list()),\\n (1 - stan::math::exp(-ScalerDotProduct[(i - 1)])),\\n \\\"assigning variable P\\\");}\\n {\\n current_statement__ = 18;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 17;\\n lp_accum__.add(\\n binomial_lpmf(Npos[(i - 1)], Ntotal[(i - 1)],\\n P[(i - 1)]));}\\n current_statement__ = 19;\\n lp_accum__.add(cauchy_lpdf(sigma, 0, 1));\\n current_statement__ = 21;\\n for (int i = 2; i <= Ymax; ++i) {\\n current_statement__ = 20;\\n lp_accum__.add(\\n normal_lpdf(foi[(i - 1)], foi[((i - 1) - 1)], sigma));}\\n current_statement__ = 22;\\n lp_accum__.add(normal_lpdf(foi[(1 - 1)], 0, 1));\\n }\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n lp_accum__.add(lp__);\\n return lp_accum__.sum();\\n } // log_prob_impl() \\n \\n template * = nullptr, stan::require_vector_like_vt* = nullptr, stan::require_std_vector_vt* = nullptr>\\n inline void write_array_impl(RNG& base_rng__, VecR& params_r__,\\n VecI& params_i__, VecVar& vars__,\\n const bool emit_transformed_parameters__ = true,\\n const bool emit_generated_quantities__ = true,\\n std::ostream* pstream__ = nullptr) const {\\n using local_scalar_t__ = double;\\n vars__.resize(0);\\n stan::io::reader in__(params_r__, params_i__);\\n static const char* function__ = \\\"model4d28376ce589__namespace::write_array\\\";\\n(void) function__; // suppress unused var warning\\n (void) function__; // suppress unused var warning\\n double lp__ = 0.0;\\n (void) lp__; // dummy to suppress unused var warning\\n stan::math::accumulator lp_accum__;\\n local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());\\n (void) DUMMY_VAR__; // suppress unused var warning\\n \\n try {\\n Eigen::Matrix foi;\\n foi = Eigen::Matrix(Ymax);\\n stan::math::fill(foi, std::numeric_limits::quiet_NaN());\\n \\n current_statement__ = 1;\\n foi = in__.row_vector(Ymax);\\n current_statement__ = 1;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 1;\\n assign(foi, cons_list(index_uni(sym1__), nil_index_list()),\\n stan::math::lb_constrain(foi[(sym1__ - 1)], 0),\\n \\\"assigning variable foi\\\");}\\n double sigma;\\n sigma = std::numeric_limits::quiet_NaN();\\n \\n current_statement__ = 2;\\n sigma = in__.scalar();\\n current_statement__ = 2;\\n sigma = stan::math::lb_constrain(sigma, 0);\\n std::vector P;\\n P = std::vector(Nobs, std::numeric_limits::quiet_NaN());\\n \\n std::vector ScalerDotProduct;\\n ScalerDotProduct = std::vector(Nobs, std::numeric_limits::quiet_NaN());\\n \\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n vars__.emplace_back(foi[(sym1__ - 1)]);}\\n vars__.emplace_back(sigma);\\n if (logical_negation((primitive_value(emit_transformed_parameters__) ||\\n primitive_value(emit_generated_quantities__)))) {\\n return ;\\n } \\n current_statement__ = 8;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 5;\\n assign(ScalerDotProduct, cons_list(index_uni(i), nil_index_list()),\\n dot_product(\\n rvalue(AgeExpoMatrix,\\n cons_list(index_uni(i),\\n cons_list(index_omni(), nil_index_list())), \\\"AgeExpoMatrix\\\"),\\n foi), \\\"assigning variable ScalerDotProduct\\\");\\n current_statement__ = 6;\\n assign(P, cons_list(index_uni(i), nil_index_list()),\\n (1 - stan::math::exp(-ScalerDotProduct[(i - 1)])),\\n \\\"assigning variable P\\\");}\\n if (emit_transformed_parameters__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(P[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(ScalerDotProduct[(sym1__ - 1)]);}\\n } \\n if (logical_negation(emit_generated_quantities__)) {\\n return ;\\n } \\n Eigen::Matrix Npos_sim;\\n Npos_sim = Eigen::Matrix(Nobs);\\n stan::math::fill(Npos_sim, std::numeric_limits::quiet_NaN());\\n \\n Eigen::Matrix P_sim;\\n P_sim = Eigen::Matrix(Nobs);\\n stan::math::fill(P_sim, std::numeric_limits::quiet_NaN());\\n \\n Eigen::Matrix logLikelihood;\\n logLikelihood = Eigen::Matrix(Nobs);\\n stan::math::fill(logLikelihood, std::numeric_limits::quiet_NaN());\\n \\n current_statement__ = 16;\\n for (int i = 1; i <= Nobs; ++i) {\\n current_statement__ = 12;\\n assign(Npos_sim, cons_list(index_uni(i), nil_index_list()),\\n binomial_rng(Ntotal[(i - 1)], P[(i - 1)], base_rng__),\\n \\\"assigning variable Npos_sim\\\");\\n current_statement__ = 13;\\n assign(P_sim, cons_list(index_uni(i), nil_index_list()),\\n (Npos_sim[(i - 1)] / Ntotal[(i - 1)]), \\\"assigning variable P_sim\\\");\\n current_statement__ = 14;\\n assign(logLikelihood, cons_list(index_uni(i), nil_index_list()),\\n binomial_lpmf(Npos[(i - 1)], Ntotal[(i - 1)], P[(i - 1)]),\\n \\\"assigning variable logLikelihood\\\");}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(Npos_sim[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(P_sim[(sym1__ - 1)]);}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n vars__.emplace_back(logLikelihood[(sym1__ - 1)]);}\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n } // write_array_impl() \\n \\n template * = nullptr, stan::require_vector_like_vt* = nullptr>\\n inline void transform_inits_impl(const stan::io::var_context& context__,\\n VecI& params_i__, VecVar& vars__,\\n std::ostream* pstream__ = nullptr) const {\\n using local_scalar_t__ = double;\\n vars__.clear();\\n vars__.reserve(num_params_r__);\\n \\n try {\\n int pos__;\\n pos__ = std::numeric_limits::min();\\n \\n pos__ = 1;\\n Eigen::Matrix foi;\\n foi = Eigen::Matrix(Ymax);\\n stan::math::fill(foi, std::numeric_limits::quiet_NaN());\\n \\n {\\n std::vector foi_flat__;\\n current_statement__ = 1;\\n assign(foi_flat__, nil_index_list(), context__.vals_r(\\\"foi\\\"),\\n \\\"assigning variable foi_flat__\\\");\\n current_statement__ = 1;\\n pos__ = 1;\\n current_statement__ = 1;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 1;\\n assign(foi, cons_list(index_uni(sym1__), nil_index_list()),\\n foi_flat__[(pos__ - 1)], \\\"assigning variable foi\\\");\\n current_statement__ = 1;\\n pos__ = (pos__ + 1);}\\n }\\n Eigen::Matrix foi_free__;\\n foi_free__ = Eigen::Matrix(Ymax);\\n stan::math::fill(foi_free__, std::numeric_limits::quiet_NaN());\\n \\n current_statement__ = 1;\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n current_statement__ = 1;\\n assign(foi_free__, cons_list(index_uni(sym1__), nil_index_list()),\\n stan::math::lb_free(foi[(sym1__ - 1)], 0),\\n \\\"assigning variable foi_free__\\\");}\\n double sigma;\\n sigma = std::numeric_limits::quiet_NaN();\\n \\n current_statement__ = 2;\\n sigma = context__.vals_r(\\\"sigma\\\")[(1 - 1)];\\n double sigma_free__;\\n sigma_free__ = std::numeric_limits::quiet_NaN();\\n \\n current_statement__ = 2;\\n sigma_free__ = stan::math::lb_free(sigma, 0);\\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n vars__.emplace_back(foi_free__[(sym1__ - 1)]);}\\n vars__.emplace_back(sigma_free__);\\n } catch (const std::exception& e) {\\n stan::lang::rethrow_located(e, locations_array__[current_statement__]);\\n // Next line prevents compiler griping about no return\\n throw std::runtime_error(\\\"*** IF YOU SEE THIS, PLEASE REPORT A BUG ***\\\"); \\n }\\n } // transform_inits_impl() \\n \\n inline void get_param_names(std::vector& names__) const {\\n \\n names__.clear();\\n names__.emplace_back(\\\"foi\\\");\\n names__.emplace_back(\\\"sigma\\\");\\n names__.emplace_back(\\\"P\\\");\\n names__.emplace_back(\\\"ScalerDotProduct\\\");\\n names__.emplace_back(\\\"Npos_sim\\\");\\n names__.emplace_back(\\\"P_sim\\\");\\n names__.emplace_back(\\\"logLikelihood\\\");\\n } // get_param_names() \\n \\n inline void get_dims(std::vector>& dimss__) const {\\n dimss__.clear();\\n dimss__.emplace_back(std::vector{static_cast(Ymax)});\\n \\n dimss__.emplace_back(std::vector{});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n dimss__.emplace_back(std::vector{static_cast(Nobs)});\\n \\n } // get_dims() \\n \\n inline void constrained_param_names(\\n std::vector& param_names__,\\n bool emit_transformed_parameters__ = true,\\n bool emit_generated_quantities__ = true) const\\n final {\\n \\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"foi\\\" + '.' + std::to_string(sym1__));\\n }}\\n param_names__.emplace_back(std::string() + \\\"sigma\\\");\\n if (emit_transformed_parameters__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"ScalerDotProduct\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n if (emit_generated_quantities__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"Npos_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"logLikelihood\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n } // constrained_param_names() \\n \\n inline void unconstrained_param_names(\\n std::vector& param_names__,\\n bool emit_transformed_parameters__ = true,\\n bool emit_generated_quantities__ = true) const\\n final {\\n \\n for (int sym1__ = 1; sym1__ <= Ymax; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"foi\\\" + '.' + std::to_string(sym1__));\\n }}\\n param_names__.emplace_back(std::string() + \\\"sigma\\\");\\n if (emit_transformed_parameters__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"ScalerDotProduct\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n if (emit_generated_quantities__) {\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"Npos_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"P_sim\\\" + '.' + std::to_string(sym1__));\\n }}\\n for (int sym1__ = 1; sym1__ <= Nobs; ++sym1__) {\\n {\\n param_names__.emplace_back(std::string() + \\\"logLikelihood\\\" + '.' + std::to_string(sym1__));\\n }}\\n }\\n \\n } // unconstrained_param_names() \\n \\n inline std::string get_constrained_sizedtypes() const {\\n stringstream s__;\\n s__ << \\\"[{\\\\\\\"name\\\\\\\":\\\\\\\"foi\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Ymax << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"sigma\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"ScalerDotProduct\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"Npos_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"logLikelihood\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"}]\\\";\\n return s__.str();\\n } // get_constrained_sizedtypes() \\n \\n inline std::string get_unconstrained_sizedtypes() const {\\n stringstream s__;\\n s__ << \\\"[{\\\\\\\"name\\\\\\\":\\\\\\\"foi\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Ymax << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"sigma\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"},\\\\\\\"block\\\\\\\":\\\\\\\"parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"ScalerDotProduct\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"array\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\",\\\\\\\"element_type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"real\\\\\\\"}},\\\\\\\"block\\\\\\\":\\\\\\\"transformed_parameters\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"Npos_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"P_sim\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"},{\\\\\\\"name\\\\\\\":\\\\\\\"logLikelihood\\\\\\\",\\\\\\\"type\\\\\\\":{\\\\\\\"name\\\\\\\":\\\\\\\"vector\\\\\\\",\\\\\\\"length\\\\\\\":\\\" << Nobs << \\\"},\\\\\\\"block\\\\\\\":\\\\\\\"generated_quantities\\\\\\\"}]\\\";\\n return s__.str();\\n } // get_unconstrained_sizedtypes() \\n \\n \\n // Begin method overload boilerplate\\n template \\n inline void write_array(RNG& base_rng,\\n Eigen::Matrix& params_r,\\n Eigen::Matrix& vars,\\n const bool emit_transformed_parameters = true,\\n const bool emit_generated_quantities = true,\\n std::ostream* pstream = nullptr) const {\\n std::vector vars_vec(vars.size());\\n std::vector params_i;\\n write_array_impl(base_rng, params_r, params_i, vars_vec,\\n emit_transformed_parameters, emit_generated_quantities, pstream);\\n vars.resize(vars_vec.size());\\n for (int i = 0; i < vars.size(); ++i) {\\n vars.coeffRef(i) = vars_vec[i];\\n }\\n }\\n template \\n inline void write_array(RNG& base_rng, std::vector& params_r,\\n std::vector& params_i,\\n std::vector& vars,\\n bool emit_transformed_parameters = true,\\n bool emit_generated_quantities = true,\\n std::ostream* pstream = nullptr) const {\\n write_array_impl(base_rng, params_r, params_i, vars, emit_transformed_parameters, emit_generated_quantities, pstream);\\n }\\n template \\n inline T_ log_prob(Eigen::Matrix& params_r,\\n std::ostream* pstream = nullptr) const {\\n Eigen::Matrix params_i;\\n return log_prob_impl(params_r, params_i, pstream);\\n }\\n template \\n inline T__ log_prob(std::vector& params_r,\\n std::vector& params_i,\\n std::ostream* pstream = nullptr) const {\\n return log_prob_impl(params_r, params_i, pstream);\\n }\\n \\n inline void transform_inits(const stan::io::var_context& context,\\n Eigen::Matrix& params_r,\\n std::ostream* pstream = nullptr) const final {\\n std::vector params_r_vec(params_r.size());\\n std::vector params_i;\\n transform_inits_impl(context, params_i, params_r_vec, pstream);\\n params_r.resize(params_r_vec.size());\\n for (int i = 0; i < params_r.size(); ++i) {\\n params_r.coeffRef(i) = params_r_vec[i];\\n }\\n }\\n inline void transform_inits(const stan::io::var_context& context,\\n std::vector& params_i,\\n std::vector& vars,\\n std::ostream* pstream = nullptr) const final {\\n transform_inits_impl(context, params_i, vars, pstream);\\n } \\n};\\n}\\nusing stan_model = model4d28376ce589__namespace::model4d28376ce589_;\\n#ifndef USING_R\\n// Boilerplate\\nstan::model::model_base& new_model(\\n stan::io::var_context& data_context,\\n unsigned int seed,\\n std::ostream* msg_stream) {\\n stan_model* m = new stan_model(data_context, seed, msg_stream);\\n return *m;\\n}\\nstan::math::profile_map& get_stan_profile_data() {\\n return model4d28376ce589__namespace::profiles__;\\n}\\n#endif\\n#endif\"]}]},\"mk_cppmodule\":{\"type\":\"closure\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"object\",\"\"]}},\"value\":[{\"type\":\"symbol\",\"attributes\":{},\"value\":[\"\"]},{\"type\":\"language\",\"attributes\":{},\"value\":[\"{\",\" prep_call_sampler(object)\",\" model_cppname <- object@model_cpp$model_cppname\",\" mod <- get(\\\"module\\\", envir = object@dso@.CXXDSOMISC, inherits = FALSE)\",\" eval(call(\\\"$\\\", mod, paste(\\\"stan_fit4\\\", model_cppname, sep = \\\"\\\")))\",\"}\"]}]},\"dso\":{\"type\":\"S4\",\"attributes\":{\"sig\":{\"type\":\"list\",\"attributes\":{\"names\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"file4d285836cfc9\"]}},\"value\":[{\"type\":\"character\",\"attributes\":{},\"value\":[]}]},\"dso_saved\":{\"type\":\"logical\",\"attributes\":{},\"value\":[true]},\"dso_filename\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"file4d285836cfc9\"]},\"modulename\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"stan_fit4model4d28376ce589__mod\"]},\"system\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"x86_64, linux-gnu\"]},\"cxxflags\":{\"type\":\"character\",\"attributes\":{},\"value\":[\"CXXFLAGS = -g -O2 -ffile-prefix-map=/build/r-base-LhKvHL/r-base-4.2.3=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 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diff --git a/tests/testthat/extdata/prev_expanded_constant.RDS b/tests/testthat/testdata/prev_expanded_constant.RDS similarity index 100% rename from tests/testthat/extdata/prev_expanded_constant.RDS rename to tests/testthat/testdata/prev_expanded_constant.RDS diff --git a/tests/testthat/extdata/prev_expanded_tv_normal.RDS b/tests/testthat/testdata/prev_expanded_tv_normal.RDS similarity index 100% rename from tests/testthat/extdata/prev_expanded_tv_normal.RDS rename to tests/testthat/testdata/prev_expanded_tv_normal.RDS diff --git a/tests/testthat/extdata/prev_expanded_tv_normal_log.RDS b/tests/testthat/testdata/prev_expanded_tv_normal_log.RDS similarity index 100% rename from tests/testthat/extdata/prev_expanded_tv_normal_log.RDS rename to tests/testthat/testdata/prev_expanded_tv_normal_log.RDS diff --git a/tests/testthat/extdata/sw_dec_foi_sim_data_grouped.csv b/tests/testthat/testdata/sw_dec_foi_sim_data_grouped.csv similarity index 100% rename from tests/testthat/extdata/sw_dec_foi_sim_data_grouped.csv rename to tests/testthat/testdata/sw_dec_foi_sim_data_grouped.csv diff --git a/tests/testthat/extdata/sw_dec_foi_sim_data_no_grouped.csv b/tests/testthat/testdata/sw_dec_foi_sim_data_no_grouped.csv similarity index 100% rename from tests/testthat/extdata/sw_dec_foi_sim_data_no_grouped.csv rename to tests/testthat/testdata/sw_dec_foi_sim_data_no_grouped.csv