Rstan connection not establishing with rtools

Modeling
1

I created a modelString and running stan_model but getting errors. The stan_model function was working well till yesterday. Due to some error I reinstalled Rtools multiple times. And now the error is persistent.
My modelString:
modelString<-"
data {
int<lower=1> Ntotal;
real y[Ntotal];
int<lower=2> NxLvl;
int<lower=1, upper=NxLvl> x[Ntotal];
real<lower=0> agammaShRa[2];
}
transformed data {
real meanY;
real sdY;
meanY = mean(y);
sdY = sd(y);
}
parameters {
real a0;
real<lower=0> aSigma;
vector[NxLvl] a;
real<lower=0> ySigma;
}
model {
a0 ~ normal(meanY, 5sdY);
aSigma ~ gamma(agammaShRa[1], agammaShRa[2]);
a ~ normal(0, aSigma);
ySigma ~ uniform(sdY/100, sdY10);
for ( i in 1:Ntotal ) {
y[i] ~ normal(a0 + a[x[i]], ySigma);
}
}
generated quantities {
// Convert a0,a[] to sum-to-zero b0,b[] :
real b0;
vector[NxLvl] b;
b0 = a0 + mean(a);
b = a - mean(a);
}"

stanDso <- stan_model(model_code = modelString)

Errors
Error in file(con, “r”) : cannot open the connection
9.
file(con, “r”)
8.
readLines(errfile)
7.
compileCode(f, code, language = language, verbose = verbose)
6.
cxxfunction(sig = sig, body = body, plugin = plugin, includes = includes, settings = settings, …, verbose = verbose)
5.
force(code)
4.
withr::with_path(rtools_path(), code)
3.
pkgbuild::with_build_tools(cxxfunction(sig = sig, body = body, plugin = plugin, includes = includes, settings = settings, …, verbose = verbose), required = rstan_options(“required”) && !identical(Sys.getenv(“WINDOWS”), “TRUE”) && !identical(Sys.getenv(“R_PACKAGE_SOURCE”), …
2.
cxxfunctionplus(signature(), body = paste(" return Rcpp::wrap("", model_name, “”);", sep = “”), includes = inc, plugin = “rstan”, save_dso = save_dso | auto_write, module_name = paste(“stan_fit4”, model_cppname, “_mod”, sep = “”), verbose = verbose)
1.
stan_model(model_code = modelString)

Error in sink(type = “output”) : invalid connection
3.
sink(type = “output”)
2.
cxxfunctionplus(signature(), body = paste(" return Rcpp::wrap("", model_name, “”);", sep = “”), includes = inc, plugin = “rstan”, save_dso = save_dso | auto_write, module_name = paste(“stan_fit4”, model_cppname, “_mod”, sep = “”), verbose = verbose)
1.
stan_model(model_code = modelString)

2

What version of R and RTools are you running?

Can you run the test model:

example(stan_model, run.dontrun = TRUE, verbose=TRUE)

And post the line that starts with error:

3

Thank you Andrew. I am using R 4.0.1, RStudio 1.3.959 and Rtools40.

I ran the example(stan_model, run.dontrun = TRUE, verbose=TRUE) and got the below error…

Found file = ‘C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/rstan/help/stan_model’
‘envir’ chosen:<environment: R_GlobalEnv>
encoding = “UTF-8” chosen
→ parsed 4 expressions; now eval(.)ing them:
has srcrefs:
List of 4
: 'srcref' int [1:8] 8 1 8 84 1 84 8 8 ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x0000025abb510ca8> : ‘srcref’ int [1:8] 9 1 9 56 1 56 9 9
…- attr(, “srcfile”)=Classes ‘srcfilecopy’, ‘srcfile’ <environment: 0x0000025abb510ca8>
: 'srcref' int [1:8] 10 1 10 45 1 45 10 10 ..- attr(*, "srcfile")=Classes 'srcfilecopy', 'srcfile' <environment: 0x0000025abb510ca8> : ‘srcref’ int [1:8] 11 1 11 46 1 46 11 11
…- attr(, “srcfile”)=Classes ‘srcfilecopy’, ‘srcfile’ <environment: 0x0000025abb510ca8>

eval(expression_nr. 1 )
=================

stn_md> stancode ← ‘data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}’
curr.fun: symbol ←
… after ‘expression(stancode ← ‘data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}’)’

eval(expression_nr. 2 )
=================

stn_md> mod ← stan_model(model_code = stancode, verbose = TRUE)

TRANSLATING MODEL ‘73fc79f8b1915e8208c736914c86d1a1’ FROM Stan CODE TO C++ CODE NOW.
successful in parsing the Stan model ‘73fc79f8b1915e8208c736914c86d1a1’.
COMPILING THE C++ CODE FOR MODEL ‘73fc79f8b1915e8208c736914c86d1a1’ NOW.
OS: x86_64, mingw32; rstan: 2.19.3; Rcpp: 1.0.4.6; inline: 0.3.15

setting environment variables:
PKG_LIBS = -L"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/StanHeaders/libs/x64" -lStanHeaders
PKG_CPPFLAGS = -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/Rcpp/include/" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/RcppEigen/include/" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/RcppEigen/include/unsupported" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/BH/include" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/StanHeaders/include/src/" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/StanHeaders/include/" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/rstan/include" -DEIGEN_NO_DEBUG -D_REENTRANT -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -include stan/math/prim/mat/fun/Eigen.hpp -std=c++1y
Program source :

1 :
2 : // includes from the plugin
3 : // [[Rcpp::plugins(cpp14)]]
4 :
5 : // user includes
6 : #define STAN__SERVICES__COMMAND_HPP#include <boost/integer/integer_log2.hpp>
7 : #include <rstan/rstaninc.hpp>
8 : // Code generated by Stan version 2.21.0
9 :
10 : #include <stan/model/model_header.hpp>
11 :
12 : namespace model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace {
13 :
14 : using std::istream;
15 : using std::string;
16 : using std::stringstream;
17 : using std::vector;
18 : using stan::io::dump;
19 : using stan::math::lgamma;
20 : using stan::model::prob_grad;
21 : using namespace stan::math;
22 :
23 : static int current_statement_begin__;
24 :
25 : stan::io::program_reader prog_reader__() {
26 : stan::io::program_reader reader;
27 : reader.add_event(0, 0, “start”, “model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1”);
28 : reader.add_event(3, 1, “end”, “model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1”);
29 : return reader;
30 : }
31 :
32 : class model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1
33 : : public stan::model::model_base_crtp<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1> {
34 : private:
35 : double y_mean;
36 : public:
37 : model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1(stan::io::var_context& context__,
38 : std::ostream* pstream__ = 0)
39 : : model_base_crtp(0) {
40 : ctor_body(context__, 0, pstream__);
41 : }
42 :
43 : model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1(stan::io::var_context& context__,
44 : unsigned int random_seed__,
45 : std::ostream* pstream__ = 0)
46 : : model_base_crtp(0) {
47 : ctor_body(context__, random_seed__, pstream__);
48 : }
49 :
50 : void ctor_body(stan::io::var_context& context__,
51 : unsigned int random_seed__,
52 : std::ostream* pstream__) {
53 : typedef double local_scalar_t__;
54 :
55 : boost::ecuyer1988 base_rng__ =
56 : stan::services::util::create_rng(random_seed__, 0);
57 : (void) base_rng__; // suppress unused var warning
58 :
59 : current_statement_begin__ = -1;
60 :
61 : static const char* function__ = “model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1”;
62 : (void) function__; // dummy to suppress unused var warning
63 : size_t pos__;
64 : (void) pos__; // dummy to suppress unused var warning
65 : std::vector vals_i__;
66 : std::vector vals_r__;
67 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());
68 : (void) DUMMY_VAR__; // suppress unused var warning
69 :
70 : try {
71 : // initialize data block variables from context__
72 : current_statement_begin__ = 1;
73 : context__.validate_dims(“data initialization”, “y_mean”, “double”, context__.to_vec());
74 : y_mean = double(0);
75 : vals_r__ = context__.vals_r(“y_mean”);
76 : pos__ = 0;
77 : y_mean = vals_r__[pos__++];
78 :
79 :
80 : // initialize transformed data variables
81 : // execute transformed data statements
82 :
83 : // validate transformed data
84 :
85 : // validate, set parameter ranges
86 : num_params_r__ = 0U;
87 : param_ranges_i__.clear();
88 : current_statement_begin__ = 1;
89 : num_params_r__ += 1;
90 : } catch (const std::exception& e) {
91 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
92 : // Next line prevents compiler griping about no return
93 : throw std::runtime_error(“*** IF YOU SEE THIS, PLEASE REPORT A BUG ");
94 : }
95 : }
96 :
97 : ~model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1() { }
98 :
99 :
100 : void transform_inits(const stan::io::var_context& context__,
101 : std::vector& params_i__,
102 : std::vector& params_r__,
103 : std::ostream pstream__) const {
104 : typedef double local_scalar_t__;
105 : stan::io::writer writer__(params_r__, params_i__);
106 : size_t pos__;
107 : (void) pos__; // dummy call to supress warning
108 : std::vector vals_r__;
109 : std::vector vals_i__;
110 :
111 : current_statement_begin__ = 1;
112 : if (!(context__.contains_r(“y”)))
113 : stan::lang::rethrow_located(std::runtime_error(std::string(“Variable y missing”)), current_statement_begin__, prog_reader__());
114 : vals_r__ = context__.vals_r(“y”);
115 : pos__ = 0U;
116 : context__.validate_dims(“parameter initialization”, “y”, “double”, context__.to_vec());
117 : double y(0);
118 : y = vals_r__[pos__++];
119 : try {
120 : writer__.scalar_unconstrain(y);
121 : } catch (const std::exception& e) {
122 : stan::lang::rethrow_located(std::runtime_error(std::string("Error transforming variable y: ") + e.what()), current_statement_begin__, prog_reader__());
123 : }
124 :
125 : params_r__ = writer__.data_r();
126 : params_i__ = writer__.data_i();
127 : }
128 :
129 : void transform_inits(const stan::io::var_context& context,
130 : Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
131 : std::ostream pstream__) const {
132 : std::vector params_r_vec;
133 : std::vector params_i_vec;
134 : transform_inits(context, params_i_vec, params_r_vec, pstream__);
135 : params_r.resize(params_r_vec.size());
136 : for (int i = 0; i < params_r.size(); ++i)
137 : params_r(i) = params_r_vec[i];
138 : }
139 :
140 :
141 : template <bool propto__, bool jacobian__, typename T__>
142 : T__ log_prob(std::vector<T__>& params_r__,
143 : std::vector& params_i__,
144 : std::ostream pstream__ = 0) const {
145 :
146 : typedef T__ local_scalar_t__;
147 :
148 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());
149 : (void) DUMMY_VAR__; // dummy to suppress unused var warning
150 :
151 : T__ lp__(0.0);
152 : stan::math::accumulator<T__> lp_accum__;
153 : try {
154 : stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
155 :
156 : // model parameters
157 : current_statement_begin__ = 1;
158 : local_scalar_t__ y;
159 : (void) y; // dummy to suppress unused var warning
160 : if (jacobian__)
161 : y = in__.scalar_constrain(lp__);
162 : else
163 : y = in__.scalar_constrain();
164 :
165 : // model body
166 :
167 : current_statement_begin__ = 1;
168 : lp_accum__.add(normal_log<propto__>(y, y_mean, 1));
169 :
170 : } catch (const std::exception& e) {
171 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
172 : // Next line prevents compiler griping about no return
173 : throw std::runtime_error(”*** IF YOU SEE THIS, PLEASE REPORT A BUG ");
174 : }
175 :
176 : lp_accum__.add(lp__);
177 : return lp_accum__.sum();
178 :
179 : } // log_prob()
180 :
181 : template <bool propto, bool jacobian, typename T_>
182 : T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
183 : std::ostream pstream = 0) const {
184 : std::vector<T_> vec_params_r;
185 : vec_params_r.reserve(params_r.size());
186 : for (int i = 0; i < params_r.size(); ++i)
187 : vec_params_r.push_back(params_r(i));
188 : std::vector vec_params_i;
189 : return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
190 : }
191 :
192 :
193 : void get_param_names(std::vectorstd::string& names__) const {
194 : names__.resize(0);
195 : names__.push_back(“y”);
196 : }
197 :
198 :
199 : void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
200 : dimss__.resize(0);
201 : std::vector<size_t> dims__;
202 : dims__.resize(0);
203 : dimss__.push_back(dims__);
204 : }
205 :
206 : template
207 : void write_array(RNG& base_rng__,
208 : std::vector& params_r__,
209 : std::vector& params_i__,
210 : std::vector& vars__,
211 : bool include_tparams__ = true,
212 : bool include_gqs__ = true,
213 : std::ostream pstream__ = 0) const {
214 : typedef double local_scalar_t__;
215 :
216 : vars__.resize(0);
217 : stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
218 : static const char function__ = “model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::write_array”;
219 : (void) function__; // dummy to suppress unused var warning
220 :
221 : // read-transform, write parameters
222 : double y = in__.scalar_constrain();
223 : vars__.push_back(y);
224 :
225 : double lp__ = 0.0;
226 : (void) lp__; // dummy to suppress unused var warning
227 : stan::math::accumulator lp_accum__;
228 :
229 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());
230 : (void) DUMMY_VAR__; // suppress unused var warning
231 :
232 : if (!include_tparams__ && !include_gqs__) return;
233 :
234 : try {
235 : if (!include_gqs__ && !include_tparams__) return;
236 : if (!include_gqs__) return;
237 : } catch (const std::exception& e) {
238 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
239 : // Next line prevents compiler griping about no return
240 : throw std::runtime_error(“*** IF YOU SEE THIS, PLEASE REPORT A BUG ");
241 : }
242 : }
243 :
244 : template
245 : void write_array(RNG& base_rng,
246 : Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
247 : Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
248 : bool include_tparams = true,
249 : bool include_gqs = true,
250 : std::ostream pstream = 0) const {
251 : std::vector params_r_vec(params_r.size());
252 : for (int i = 0; i < params_r.size(); ++i)
253 : params_r_vec[i] = params_r(i);
254 : std::vector vars_vec;
255 : std::vector params_i_vec;
256 : write_array(base_rng, params_r_vec, params_i_vec, vars_vec, include_tparams, include_gqs, pstream);
257 : vars.resize(vars_vec.size());
258 : for (int i = 0; i < vars.size(); ++i)
259 : vars(i) = vars_vec[i];
260 : }
261 :
262 : std::string model_name() const {
263 : return “model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1”;
264 : }
265 :
266 :
267 : void constrained_param_names(std::vectorstd::string& param_names__,
268 : bool include_tparams__ = true,
269 : bool include_gqs__ = true) const {
270 : std::stringstream param_name_stream__;
271 : param_name_stream__.str(std::string());
272 : param_name_stream__ << “y”;
273 : param_names__.push_back(param_name_stream__.str());
274 :
275 : if (!include_gqs__ && !include_tparams__) return;
276 :
277 : if (include_tparams__) {
278 : }
279 :
280 : if (!include_gqs__) return;
281 : }
282 :
283 :
284 : void unconstrained_param_names(std::vectorstd::string& param_names__,
285 : bool include_tparams__ = true,
286 : bool include_gqs__ = true) const {
287 : std::stringstream param_name_stream__;
288 : param_name_stream__.str(std::string());
289 : param_name_stream__ << “y”;
290 : param_names__.push_back(param_name_stream__.str());
291 :
292 : if (!include_gqs__ && !include_tparams__) return;
293 :
294 : if (include_tparams__) {
295 : }
296 :
297 : if (!include_gqs__) return;
298 : }
299 :
300 : }; // model
301 :
302 : } // namespace
303 :
304 : typedef model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1 stan_model;
305 :
306 : #ifndef USING_R
307 :
308 : stan::model::model_base& new_model(
309 : stan::io::var_context& data_context,
310 : unsigned int seed,
311 : std::ostream msg_stream) {
312 : stan_model m = new stan_model(data_context, seed, msg_stream);
313 : return m;
314 : }
315 :
316 : #endif
317 :
318 : /*
319 : * Define Rcpp Module to expose stan_fit’s functions to R.
320 : */
321 : RCPP_MODULE(stan_fit4model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_mod){
322 : Rcpp::class_<rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1,
323 : boost::random::ecuyer1988> >(“stan_fit4model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1”)
324 : // .constructorRcpp::List()
325 : .constructor<SEXP, SEXP, SEXP>()
326 : // .constructor<SEXP, SEXP>()
327 : .method(“call_sampler”,
328 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::call_sampler)
329 : .method(“param_names”,
330 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_names)
331 : .method(“param_names_oi”,
332 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_names_oi)
333 : .method(“param_fnames_oi”,
334 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_fnames_oi)
335 : .method(“param_dims”,
336 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_dims)
337 : .method(“param_dims_oi”,
338 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_dims_oi)
339 : .method(“update_param_oi”,
340 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::update_param_oi)
341 : .method(“param_oi_tidx”,
342 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_oi_tidx)
343 : .method(“grad_log_prob”,
344 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::grad_log_prob)
345 : .method(“log_prob”,
346 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::log_prob)
347 : .method(“unconstrain_pars”,
348 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::unconstrain_pars)
349 : .method(“constrain_pars”,
350 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::constrain_pars)
351 : .method(“num_pars_unconstrained”,
352 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::num_pars_unconstrained)
353 : .method(“unconstrained_param_names”,
354 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::unconstrained_param_names)
355 : .method(“constrained_param_names”,
356 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::constrained_param_names)
357 : .method(“standalone_gqs”,
358 : &rstan::stan_fit<model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1_namespace::model9ddc23367098_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::standalone_gqs)
359 : ;
360 : }
361 :
362 : // declarations
363 : extern “C” {
364 : SEXP file9ddc4e4d213b( ) ;
365 : }
366 :
367 : // definition
368 :
369 : SEXP file9ddc4e4d213b( ){
370 : return Rcpp::wrap(“73fc79f8b1915e8208c736914c86d1a1”);
371 : }
372 :
373 :
Compilation argument:
C:/PROGRA~1/R/R-40~1.1/bin/x64/R CMD SHLIB file9ddc4e4d213b.cpp 2> file9ddc4e4d213b.cpp.err.txt
C:/Rtools/mingw_64/bin/g++ -m64 -std=c++1y -std=gnu++14 -I"C:/PROGRA~1/R/R-40~1.1/include” -DNDEBUG -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/Rcpp/include/" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/RcppEigen/include/" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/RcppEigen/include/unsupported" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/BH/include" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/StanHeaders/include/src/" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/StanHeaders/include/" -I"C:/Users/ranja/OneDrive/Documents/R/win-library/4.0/rstan/include" -DEIGEN_NO_DEBUG -D_REENTRANT -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -include stan/math/prim/mat/fun/Eigen.hpp -std=c++1y -march=core2 -O3 -march=native -mtune=native -c file9ddc4e4d213b.cpp -o file9ddc4e4d213b.o
sh: C:/Rtools/mingw_64/bin/g++: No such file or directory
make: *** [C:/PROGRA~1/R/R-40~1.1/etc/x64/Makeconf:229: file9ddc4e4d213b.o] Error 127
Error in file(con, “r”) : cannot open the connection
In addition: Warning messages:
1: In find.package(package, lib.loc, verbose = verbose) :
package ‘base’ found more than once, using the first from
“C:/PROGRA~1/R/R-40~1.1/library/base”,
“C:/Program Files/R/R-4.0.1/library/base”
2: In file(con, “r”) :
cannot open file ‘file9ddc4e4d213b.cpp.err.txt’: No such file or directory

4

Thanks! The key error here is:

C:/Rtools/mingw_64/bin/g++: No such file or directory

This indicates that R is looking for the old version of RTools. There are some configurations that we can check to see what’s causing this.

Can you run the following commands and post the output:

Sys.getenv("BINPREF")
Sys.getenv("PATH")
readLines("~/.R/Makevars.win")
readLines("~/Rprofile.site")
5

Thanks !! Please find the output

Sys.getenv(“BINPREF”)
[1] “C:/Rtools/mingw_(WIN)/bin/" Sys.getenv("PATH") [1] "C:\\Program Files\\rtools40\\usr\\bin;C:\\Program Files\\R\\R-4.0.1\\bin\\x64;C:\\Program Files (x86)\\Common Files\\Oracle\\Java\\javapath_target_300653500;C:\\Windows\\System32;C:\\Windows;C:\\Windows\\System32\\wbem;C:\\Windows\\System32\\WindowsPowerShell\\v1.0;C:\\Windows\\System32\\OpenSSH;C:\\Users\\ranja\\AppData\\Local\\Microsoft\\WindowsApps;C:\\Users\\ranja\\AppData\\Local\\atom\\bin;C:\\Users\\ranja\\AppData\\Local\\GitHubDesktop\\bin" readLines("~/.R/Makevars.win") [1] "" [2] "CXX14FLAGS=-O3 -march=native -mtune=native" [3] "CXX11FLAGS=-O3 -march=corei7 -mtune=corei7" [4] "" [5] "CXX14FLAGS=-O3 -march=native -mtune=native" [6] "CXX11FLAGS=-O3 -march=corei7 -mtune=corei7" [7] "" [8] "CXX14FLAGS=-O3 -march=native -mtune=native" [9] "CXX11FLAGS=-O3 -march=corei7 -mtune=corei7" [10] "" [11] "CXX14FLAGS=-O3 -march=corei7 -mtune=corei7" [12] "CXX14 = (BINPREF)g++ -m$(WIN) -std=c++1y”
[13] “CXX11FLAGS=-O3 -march=corei7 -mtune=corei7”
[14] “”
[15] “CXX14FLAGS=-O3 -march=corei7 -mtune=corei7”
[16] “CXX14 = (BINPREF)g++ -m(WIN) -std=c++1y”
[17] “CXX11FLAGS=-O3 -march=corei7 -mtune=corei7”
[18] “”
[19] “CXX14FLAGS=-O3 -march=corei7 -mtune=corei7”
[20] “CXX14 = (BINPREF)g++ -m(WIN) -std=c++1y”
[21] “CXX11FLAGS=-O3 -march=corei7 -mtune=corei7”
[22] “”
[23] “CXX14FLAGS=-O3 -march=native -mtune=native”
[24] “CXX11FLAGS=-O3 -march=corei7 -mtune=corei7”
[25] “”
[26] “CXX14FLAGS=-O3 -march=native -mtune=native”
[27] “CXX11FLAGS=-O3 -march=corei7 -mtune=corei7”
[28] “”
[29] “CXX14=$(BINPREF)g++ -O2 -march=native -mtune=native”
[30] “CXX11FLAGS=-O3 -march=corei7 -mtune=corei7”
[31] “”
[32] “CXX14FLAGS=-O3 -march=native -mtune=native”
[33] “CXX11FLAGS=-O3 -march=corei7 -mtune=corei7”
readLines(“~/Rprofile.site”)
Error in file(con, “r”) : cannot open the connection
In addition: Warning message:
In file(con, “r”) :
cannot open file ‘C:/Users/ranja/OneDrive/Documents/Rprofile.site’: No such file or directory

6

The cause of the issue is here:

> Sys.getenv("BINPREF")
[1] "C:/Rtools/mingw_$(WIN)/bin/"

This is telling R to keep looking for the old version of RTools. The first step to fixing this is to delete the file that’s setting this variable:

unlink("~/.Rprofile")

You can also get rid of the majority of the Makevars.win file, since these aren’t needed anymore with RTools4:

cat("CXX14FLAGS=-O3 -march=native -mtune=native",
    file = "~/.R/Makevars.win", 
    sep = "\n", append = FALSE)

Try running those commands, then restarting R and running the example model again:

example(stan_model, run.dontrun = TRUE, verbose=TRUE)

If that works fine, then you should be able to run your Stan models as normal again

7

I ran the above suggestions and then restarted R and tries my STan models. But it is not working. Then I re installed rstan and ran the below:

unlink(“~/.Rprofile”)
Sys.getenv(“BINPREF”)
[1] “”
Sys.getenv(“PATH”)
[1] “C:\Program Files\rtools40\usr\bin;C:\Program Files\R\R-4.0.1\bin\x64;C:\Program Files (x86)\Common Files\Oracle\Java\javapath_target_300653500;C:\Windows\System32;C:\Windows;C:\Windows\System32\wbem;C:\Windows\System32\WindowsPowerShell\v1.0;C:\Windows\System32\OpenSSH;C:\Users\ranja\AppData\Local\Microsoft\WindowsApps;C:\Users\ranja\AppData\Local\atom\bin;C:\Users\ranja\AppData\Local\GitHubDesktop\bin”
readLines(“~/.R/Makevars.win”)
[1] “CXX14FLAGS=-O3 -march=native -mtune=native”
readLines(“~/Rprofile.site”)
Error in file(con, “r”) : cannot open the connection
In addition: Warning message:
In file(con, “r”) :
cannot open file ‘C:/Users/ranja/OneDrive/Documents/Rprofile.site’: No such file or directory

8

So if you run the example model:

example(stan_model, run.dontrun = TRUE, verbose=TRUE)

What is the line that starts with error:?

9

Error in file(con, “r”) : cannot open the connection
In addition: Warning messages:
1: In find.package(package, lib.loc, verbose = verbose) :
package ‘base’ found more than once, using the first from
“C:/PROGRA~1/R/R-40~1.1/library/base”,
“C:/Program Files/R/R-4.0.1/library/base”
2: In file(con, “r”) :
cannot open file ‘file14c1030bd5233.cpp.err.txt’: No such file or directory

10

If you restart R and run:

library(rstan)

stancode = "data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}"
fit = stan(model_code=stancode,data=list(y_mean=0))

What happens?

11

stancode = “data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}”
fit = stan(model_code=stancode,data=list(y_mean=0))
I am getting 2 errors.

Error 1:

Error in file(con, “r”) : cannot open the connection
10.
file(con, “r”)
9.
readLines(errfile)
8.
compileCode(f, code, language = language, verbose = verbose)
7.
cxxfunction(sig = sig, body = body, plugin = plugin, includes = includes, settings = settings, …, verbose = verbose)
6.
force(code)
5.
withr::with_path(rtools_path(), code)
4.
pkgbuild::with_build_tools(cxxfunction(sig = sig, body = body, plugin = plugin, includes = includes, settings = settings, …, verbose = verbose), required = rstan_options(“required”) && !identical(Sys.getenv(“WINDOWS”), “TRUE”) && !identical(Sys.getenv(“R_PACKAGE_SOURCE”), …
3.
cxxfunctionplus(signature(), body = paste(" return Rcpp::wrap("", model_name, “");”, sep = “”), includes = inc, plugin = “rstan”, save_dso = save_dso | auto_write, module_name = paste(“stan_fit4”, model_cppname, “_mod”, sep = “”), verbose = verbose)
2.
stan_model(file, model_name = model_name, model_code = model_code, stanc_ret = NULL, boost_lib = boost_lib, eigen_lib = eigen_lib, save_dso = save_dso, verbose = verbose)
1.
stan(model_code = stancode, data = list(y_mean = 0))

Error 2:
Error in sink(type = “output”) : invalid connection
4.
sink(type = “output”)
3.
cxxfunctionplus(signature(), body = paste(" return Rcpp::wrap("", model_name, “");”, sep = “”), includes = inc, plugin = “rstan”, save_dso = save_dso | auto_write, module_name = paste(“stan_fit4”, model_cppname, “_mod”, sep = “”), verbose = verbose)
2.
stan_model(file, model_name = model_name, model_code = model_code, stanc_ret = NULL, boost_lib = boost_lib, eigen_lib = eigen_lib, save_dso = save_dso, verbose = verbose)
1.
stan(model_code = stancode, data = list(y_mean = 0))

12

I had a similar problem once, and for me moving the R-folder from “C:/Program Files/R” to “C:/R” solved the problem. Back then I thought that maybe the space in the path is the problem, because I remember somewhere in the error message i saw that the file path was not parsed correctly (was interrupted after the space)… Not sure whether this is also the case here…

13

I see you’re working out of a OneDrive folder, is the result any different if you pause the synchronisation and try again?

14

Thank you. But there was issue in the Onedrive and it got fixed.

15

Thank you so much Andrew. Totally appreciate your time and effort for helping me. Onedrive was the issue and I fixed that. Now the stan models are working

Stay safe and healthy.