Failing to compile stan model

I am using Rtools42, R 4.2.0 and Rstudio 2022.02.3.
I’ve followed all the procedures in teh github repo both for installing Stan and configuring rtools. Still when I try to compile the example model it gives me compile error:

> example(stan_model, package = "rstan", run.dontrun = TRUE)

ERROR(s) during compilation: source code errors or compiler configuration errors!

Program source:
  1: 
  2: // includes from the plugin
  3: // [[Rcpp::plugins(cpp14)]]
  4: 
  5: 
  6: // user includes
  7: #include <Rcpp.h>
  8: #include <rstan/io/rlist_ref_var_context.hpp>
  9: #include <rstan/io/r_ostream.hpp>
 10: #include <rstan/stan_args.hpp>
 11: #include <boost/integer/integer_log2.hpp>
 12: // Code generated by Stan version 2.21.0
 13: 
 14: #include <stan/model/model_header.hpp>
 15: 
 16: namespace model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1_namespace {
 17: 
 18: using std::istream;
 19: using std::string;
 20: using std::stringstream;
 21: using std::vector;
 22: using stan::io::dump;
 23: using stan::math::lgamma;
 24: using stan::model::prob_grad;
 25: using namespace stan::math;
 26: 
 27: static int current_statement_begin__;
 28: 
 29: stan::io::program_reader prog_reader__() {
 30:     stan::io::program_reader reader;
 31:     reader.add_event(0, 0, "start", "model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1");
 32:     reader.add_event(3, 1, "end", "model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1");
 33:     return reader;
 34: }
 35: 
 36: class model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1
 37:   : public stan::model::model_base_crtp<model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1> {
 38: private:
 39:         double y_mean;
 40: public:
 41:     model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1(rstan::io::rlist_ref_var_context& context__,
 42:         std::ostream* pstream__ = 0)
 43:         : model_base_crtp(0) {
 44:         ctor_body(context__, 0, pstream__);
 45:     }
 46: 
 47:     model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1(stan::io::var_context& context__,
 48:         unsigned int random_seed__,
 49:         std::ostream* pstream__ = 0)
 50:         : model_base_crtp(0) {
 51:         ctor_body(context__, random_seed__, pstream__);
 52:     }
 53: 
 54:     void ctor_body(stan::io::var_context& context__,
 55:                    unsigned int random_seed__,
 56:                    std::ostream* pstream__) {
 57:         typedef double local_scalar_t__;
 58: 
 59:         boost::ecuyer1988 base_rng__ =
 60:           stan::services::util::create_rng(random_seed__, 0);
 61:         (void) base_rng__;  // suppress unused var warning
 62: 
 63:         current_statement_begin__ = -1;
 64: 
 65:         static const char* function__ = "model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1_namespace::model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1";
 66:         (void) function__;  // dummy to suppress unused var warning
 67:         size_t pos__;
 68:         (void) pos__;  // dummy to suppress unused var warning
 69:         std::vector<int> vals_i__;
 70:         std::vector<double> vals_r__;
 71:         local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
 72:         (void) DUMMY_VAR__;  // suppress unused var warning
 73: 
 74:         try {
 75:             // initialize data block variables from context__
 76:             current_statement_begin__ = 1;
 77:             context__.validate_dims("data initialization", "y_mean", "double", context__.to_vec());
 78:             y_mean = double(0);
 79:             vals_r__ = context__.vals_r("y_mean");
 80:             pos__ = 0;
 81:             y_mean = vals_r__[pos__++];
 82: 
 83: 
 84:             // initialize transformed data variables
 85:             // execute transformed data statements
 86: 
 87:             // validate transformed data
 88: 
 89:             // validate, set parameter ranges
 90:             num_params_r__ = 0U;
 91:             param_ranges_i__.clear();
 92:             current_statement_begin__ = 1;
 93:             num_params_r__ += 1;
 94:         } catch (const std::exception& e) {
 95:             stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
 96:             // Next line prevents compiler griping about no return
 97:             throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
 98:         }
 99:     }
100: 
101:     ~model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1() { }
102: 
103: 
104:     void transform_inits(const stan::io::var_context& context__,
105:                          std::vector<int>& params_i__,
106:                          std::vector<double>& params_r__,
107:                          std::ostream* pstream__) const {
108:         typedef double local_scalar_t__;
109:         stan::io::writer<double> writer__(params_r__, params_i__);
110:         size_t pos__;
111:         (void) pos__; // dummy call to supress warning
112:         std::vector<double> vals_r__;
113:         std::vector<int> vals_i__;
114: 
115:         current_statement_begin__ = 1;
116:         if (!(context__.contains_r("y")))
117:             stan::lang::rethrow_located(std::runtime_error(std::string("Variable y missing")), current_statement_begin__, prog_reader__());
118:         vals_r__ = context__.vals_r("y");
119:         pos__ = 0U;
120:         context__.validate_dims("parameter initialization", "y", "double", context__.to_vec());
121:         double y(0);
122:         y = vals_r__[pos__++];
123:         try {
124:             writer__.scalar_unconstrain(y);
125:         } catch (const std::exception& e) {
126:             stan::lang::rethrow_located(std::runtime_error(std::string("Error transforming variable y: ") + e.what()), current_statement_begin__, prog_reader__());
127:         }
128: 
129:         params_r__ = writer__.data_r();
130:         params_i__ = writer__.data_i();
131:     }
132: 
133:     void transform_inits(const stan::io::var_context& context,
134:                          Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
135:                          std::ostream* pstream__) const {
136:       std::vector<double> params_r_vec;
137:       std::vector<int> params_i_vec;
138:       transform_inits(context, params_i_vec, params_r_vec, pstream__);
139:       params_r.resize(params_r_vec.size());
140:       for (int i = 0; i < params_r.size(); ++i)
141:         params_r(i) = params_r_vec[i];
142:     }
143: 
144: 
145:     template <bool propto__, bool jacobian__, typename T__>
146:     T__ log_prob(std::vector<T__>& params_r__,
147:                  std::vector<int>& params_i__,
148:                  std::ostream* pstream__ = 0) const {
149: 
150:         typedef T__ local_scalar_t__;
151: 
152:         local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
153:         (void) DUMMY_VAR__;  // dummy to suppress unused var warning
154: 
155:         T__ lp__(0.0);
156:         stan::math::accumulator<T__> lp_accum__;
157:         try {
158:             stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
159: 
160:             // model parameters
161:             current_statement_begin__ = 1;
162:             local_scalar_t__ y;
163:             (void) y;  // dummy to suppress unused var warning
164:             if (jacobian__)
165:                 y = in__.scalar_constrain(lp__);
166:             else
167:                 y = in__.scalar_constrain();
168: 
169:             // model body
170: 
171:             current_statement_begin__ = 1;
172:             lp_accum__.add(normal_log<propto__>(y, y_mean, 1));
173: 
174:         } catch (const std::exception& e) {
175:             stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
176:             // Next line prevents compiler griping about no return
177:             throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
178:         }
179: 
180:         lp_accum__.add(lp__);
181:         return lp_accum__.sum();
182: 
183:     } // log_prob()
184: 
185:     template <bool propto, bool jacobian, typename T_>
186:     T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
187:                std::ostream* pstream = 0) const {
188:       std::vector<T_> vec_params_r;
189:       vec_params_r.reserve(params_r.size());
190:       for (int i = 0; i < params_r.size(); ++i)
191:         vec_params_r.push_back(params_r(i));
192:       std::vector<int> vec_params_i;
193:       return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
194:     }
195: 
196: 
197:     void get_param_names(std::vector<std::string>& names__) const {
198:         names__.resize(0);
199:         names__.push_back("y");
200:     }
201: 
202: 
203:     void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
204:         dimss__.resize(0);
205:         std::vector<size_t> dims__;
206:         dims__.resize(0);
207:         dimss__.push_back(dims__);
208:     }
209: 
210:     template <typename RNG>
211:     void write_array(RNG& base_rng__,
212:                      std::vector<double>& params_r__,
213:                      std::vector<int>& params_i__,
214:                      std::vector<double>& vars__,
215:                      bool include_tparams__ = true,
216:                      bool include_gqs__ = true,
217:                      std::ostream* pstream__ = 0) const {
218:         typedef double local_scalar_t__;
219: 
220:         vars__.resize(0);
221:         stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
222:         static const char* function__ = "model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1_namespace::write_array";
223:         (void) function__;  // dummy to suppress unused var warning
224: 
225:         // read-transform, write parameters
226:         double y = in__.scalar_constrain();
227:         vars__.push_back(y);
228: 
229:         double lp__ = 0.0;
230:         (void) lp__;  // dummy to suppress unused var warning
231:         stan::math::accumulator<double> lp_accum__;
232: 
233:         local_scalar_t__ DUMMY_VAR__(std::numeric_limits<double>::quiet_NaN());
234:         (void) DUMMY_VAR__;  // suppress unused var warning
235: 
236:         if (!include_tparams__ && !include_gqs__) return;
237: 
238:         try {
239:             if (!include_gqs__ && !include_tparams__) return;
240:             if (!include_gqs__) return;
241:         } catch (const std::exception& e) {
242:             stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
243:             // Next line prevents compiler griping about no return
244:             throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ***");
245:         }
246:     }
247: 
248:     template <typename RNG>
249:     void write_array(RNG& base_rng,
250:                      Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
251:                      Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
252:                      bool include_tparams = true,
253:                      bool include_gqs = true,
254:                      std::ostream* pstream = 0) const {
255:       std::vector<double> params_r_vec(params_r.size());
256:       for (int i = 0; i < params_r.size(); ++i)
257:         params_r_vec[i] = params_r(i);
258:       std::vector<double> vars_vec;
259:       std::vector<int> params_i_vec;
260:       write_array(base_rng, params_r_vec, params_i_vec, vars_vec, include_tparams, include_gqs, pstream);
261:       vars.resize(vars_vec.size());
262:       for (int i = 0; i < vars.size(); ++i)
263:         vars(i) = vars_vec[i];
264:     }
265: 
266:     std::string model_name() const {
267:         return "model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1";
268:     }
269: 
270: 
271:     void constrained_param_names(std::vector<std::string>& param_names__,
272:                                  bool include_tparams__ = true,
273:                                  bool include_gqs__ = true) const {
274:         std::stringstream param_name_stream__;
275:         param_name_stream__.str(std::string());
276:         param_name_stream__ << "y";
277:         param_names__.push_back(param_name_stream__.str());
278: 
279:         if (!include_gqs__ && !include_tparams__) return;
280: 
281:         if (include_tparams__) {
282:         }
283: 
284:         if (!include_gqs__) return;
285:     }
286: 
287: 
288:     void unconstrained_param_names(std::vector<std::string>& param_names__,
289:                                    bool include_tparams__ = true,
290:                                    bool include_gqs__ = true) const {
291:         std::stringstream param_name_stream__;
292:         param_name_stream__.str(std::string());
293:         param_name_stream__ << "y";
294:         param_names__.push_back(param_name_stream__.str());
295: 
296:         if (!include_gqs__ && !include_tparams__) return;
297: 
298:         if (include_tparams__) {
299:         }
300: 
301:         if (!include_gqs__) return;
302:     }
303: 
304: }; // model
305: 
306: }  // namespace
307: 
308: typedef model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1_namespace::model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1 stan_model;
309: 
310: #ifndef USING_R
311: 
312: stan::model::model_base& new_model(
313:         stan::io::var_context& data_context,
314:         unsigned int seed,
315:         std::ostream* msg_stream) {
316:   stan_model* m = new stan_model(data_context, seed, msg_stream);
317:   return *m;
318: }
319: 
320: #endif
321: 
322: 
323: 
324: #include <rstan_next/stan_fit.hpp>
325: 
326: struct stan_model_holder {
327:     stan_model_holder(rstan::io::rlist_ref_var_context rcontext,
328:                       unsigned int random_seed)
329:     : rcontext_(rcontext), random_seed_(random_seed)
330:      {
331:      }
332: 
333:    //stan::math::ChainableStack ad_stack;
334:    rstan::io::rlist_ref_var_context rcontext_;
335:    unsigned int random_seed_;
336: };
337: 
338: Rcpp::XPtr<stan::model::model_base> model_ptr(stan_model_holder* smh) {
339:   Rcpp::XPtr<stan::model::model_base> model_instance(new stan_model(smh->rcontext_, smh->random_seed_), true);
340:   return model_instance;
341: }
342: 
343: Rcpp::XPtr<rstan::stan_fit_base> fit_ptr(stan_model_holder* smh) {
344:   return Rcpp::XPtr<rstan::stan_fit_base>(new rstan::stan_fit(model_ptr(smh), smh->random_seed_), true);
345: }
346: 
347: std::string model_name(stan_model_holder* smh) {
348:   return model_ptr(smh).get()->model_name();
349: }
350: 
351: RCPP_MODULE(stan_fit4model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1_mod){
352:   Rcpp::class_<stan_model_holder>("stan_fit4model204c2fd54aeb_73fc79f8b1915e8208c736914c86d1a1")
353:   .constructor<rstan::io::rlist_ref_var_context, unsigned int>()
354:   .method("model_ptr", &model_ptr)
355:   .method("fit_ptr", &fit_ptr)
356:   .method("model_name", &model_name)
357:   ;
358: }
359: 
360: 
361: // declarations
362: extern "C" {
363: SEXP file204c5c0910e2( ) ;
364: }
365: 
366: // definition
367: SEXP file204c5c0910e2() {
368:  return Rcpp::wrap("73fc79f8b1915e8208c736914c86d1a1");
369: }

Compilation ERROR, function(s)/method(s) not created!
Error in compileCode(f, code, language = language, verbose = verbose) : 
  C:\rtools42\x86_64-w64-mingw32.static.posix\bin/ld.exe: file204c5c0910e2.o:file204c5c0910e2.cpp:(.text$_ZN3tbb8internal26task_scheduler_observer_v3D0Ev[_ZN3tbb8internal26task_scheduler_observer_v3D0Ev]+0x1d): undefined reference to `tbb::internal::task_scheduler_observer_v3::observe(bool)'C:\rtools42\x86_64-w64-mingw32.static.posix\bin/ld.exe: file204c5c0910e2.o:file204c5c0910e2.cpp:(.text$_ZN3tbb10interface623task_scheduler_observerD1Ev[_ZN3tbb10interface623task_scheduler_observerD1Ev]+0x1d): undefined reference to `tbb::internal::task_scheduler_observer_v3::observe(bool)'C:\rtools42\x86_64-w64-mingw32.static.posix\bin/ld.exe: file204c5c0910e2.o:file204c5c0910e2.cpp:(.text$_ZN3tbb10interface623task_scheduler_observerD1Ev[_ZN3tbb10interface623task_scheduler_observerD1Ev]+0x3a): undefined reference to `tbb::internal::task_scheduler_observer_v3::observe(bool)'C:\rtools42\x86_64-w64-mingw32.static.posix\bin/ld.exe: file204c5c0910e2.o:file204c5c0910e2.cpp:(.text$_ZN3tbb10interface
> 

Can someone please help me? I’ve to deliver a project in one week.

We’re currently having compatibility issues with R4.2 and the CRAN rstan, can you restart R and install the preview of the next version using:

remove.packages(c("StanHeaders", "rstan"))
install.packages("StanHeaders", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
install.packages("rstan", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
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You made my day, it worked! Btw I’ve switched to Rtools40, should it work also with rtools42?

Yep, it’s compatible with both

1 Like