Error: cannot allocate vector of size 17348.7 Gb

Yes it is installed under C:\rtools40

What are

Sys.getenv("RTOOLS40_HOME")
Sys.getenv("PATH")

?

Sys.getenv(“RTOOLS40_HOME”)
[1] “C:\rtools40”
Sys.getenv(“PATH”)
[1] “C:\rtools40\usr\bin;C:\Program Files\R\R-4.0.0\bin\x64;C:\Program Files (x86)\Common Files\Oracle\Java\javapath;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\Program Files\Dell\Dell Data Protection\Encryption\;C:\Program Files\MiKTeX 2.9\miktex\bin\x64\;C:\Program Files\SASHome\SASFoundation\9.4\ets\sasexe;C:\Program Files\SASHome\Secure\ccme4;C:\Program Files (x86)\Webex\Plugins;C:\Users\fur\AppData\Local\Microsoft\WindowsApps;”

thanks for your continued attention on this!

OK, that looks right. Can you post the part of the output that involves calling g++ when you execute

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

?

It has a long output for something and here is the error message:

Compilation argument:
C:/Program Files/R/R-40~1.0/bin/x64/R CMD SHLIB file2a803b211e4f.cpp 2> file2a803b211e4f.cpp.err.txt
Error in file(con, “r”) : cannot open the connection
In addition: Warning message:
In file(con, “r”) :
cannot open file ‘file2a803b211e4f.cpp.err.txt’: No such file or directory

We need the part of the long output that says something like C:/rtools40/mingw_64/bin/g++ ....

Below is the whole thing. Rstudio prompted to install more things which I did. I also reinstalled Rtool40. Thanks!

example(stan_model, package = “rstan”, run.dontrun = TRUE)

stn_md> stancode <- ‘data {real y_mean;} parameters {real y;} model {y ~ normal(y_mean,1);}’

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
WARNING: Rtools is required to build R packages, but the version of Rtools previously installed in C:/rtools40 has been deleted.

Please download and install Rtools custom from https://cran.r-project.org/bin/windows/Rtools/.

setting environment variables:
PKG_LIBS = -L"C:/Users/fur/Documents/R/win-library/4.0/StanHeaders/libs/x64" -lStanHeaders
PKG_CPPFLAGS = -I"C:/Users/fur/Documents/R/win-library/4.0/Rcpp/include/" -I"C:/Users/fur/Documents/R/win-library/4.0/RcppEigen/include/" -I"C:/Users/fur/Documents/R/win-library/4.0/RcppEigen/include/unsupported" -I"C:/Users/fur/Documents/R/win-library/4.0/BH/include" -I"C:/Users/fur/Documents/R/win-library/4.0/StanHeaders/include/src/" -I"C:/Users/fur/Documents/R/win-library/4.0/StanHeaders/include/" -I"C:/Users/fur/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 2.19.1
9 :
10 : #include <stan/model/model_header.hpp>
11 :
12 : namespace model2a80454e5ad0_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”, “model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1”);
28 : reader.add_event(3, 1, “end”, “model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1”);
29 : return reader;
30 : }
31 :
32 : class model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1 : public prob_grad {
33 : private:
34 : double y_mean;
35 : public:
36 : model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1(stan::io::var_context& context__,
37 : std::ostream* pstream__ = 0)
38 : : prob_grad(0) {
39 : ctor_body(context__, 0, pstream__);
40 : }
41 :
42 : model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1(stan::io::var_context& context__,
43 : unsigned int random_seed__,
44 : std::ostream* pstream__ = 0)
45 : : prob_grad(0) {
46 : ctor_body(context__, random_seed__, pstream__);
47 : }
48 :
49 : void ctor_body(stan::io::var_context& context__,
50 : unsigned int random_seed__,
51 : std::ostream* pstream__) {
52 : typedef double local_scalar_t__;
53 :
54 : boost::ecuyer1988 base_rng__ =
55 : stan::services::util::create_rng(random_seed__, 0);
56 : (void) base_rng__; // suppress unused var warning
57 :
58 : current_statement_begin__ = -1;
59 :
60 : static const char* function__ = “model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1”;
61 : (void) function__; // dummy to suppress unused var warning
62 : size_t pos__;
63 : (void) pos__; // dummy to suppress unused var warning
64 : std::vector vals_i__;
65 : std::vector vals_r__;
66 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());
67 : (void) DUMMY_VAR__; // suppress unused var warning
68 :
69 : try {
70 : // initialize data block variables from context__
71 : current_statement_begin__ = 1;
72 : context__.validate_dims(“data initialization”, “y_mean”, “double”, context__.to_vec());
73 : y_mean = double(0);
74 : vals_r__ = context__.vals_r(“y_mean”);
75 : pos__ = 0;
76 : y_mean = vals_r__[pos__++];
77 :
78 :
79 : // initialize transformed data variables
80 : // execute transformed data statements
81 :
82 : // validate transformed data
83 :
84 : // validate, set parameter ranges
85 : num_params_r__ = 0U;
86 : param_ranges_i__.clear();
87 : current_statement_begin__ = 1;
88 : num_params_r__ += 1;
89 : } catch (const std::exception& e) {
90 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
91 : // Next line prevents compiler griping about no return
92 : throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ");
93 : }
94 : }
95 :
96 : ~model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1() { }
97 :
98 :
99 : void transform_inits(const stan::io::var_context& context__,
100 : std::vector& params_i__,
101 : std::vector& params_r__,
102 : std::ostream
pstream__) const {
103 : typedef double local_scalar_t__;
104 : stan::io::writer writer__(params_r__, params_i__);
105 : size_t pos__;
106 : (void) pos__; // dummy call to supress warning
107 : std::vector vals_r__;
108 : std::vector vals_i__;
109 :
110 : current_statement_begin__ = 1;
111 : if (!(context__.contains_r(“y”)))
112 : stan::lang::rethrow_located(std::runtime_error(std::string(“Variable y missing”)), current_statement_begin__, prog_reader__());
113 : vals_r__ = context__.vals_r(“y”);
114 : pos__ = 0U;
115 : context__.validate_dims(“parameter initialization”, “y”, “double”, context__.to_vec());
116 : double y(0);
117 : y = vals_r__[pos__++];
118 : try {
119 : writer__.scalar_unconstrain(y);
120 : } catch (const std::exception& e) {
121 : stan::lang::rethrow_located(std::runtime_error(std::string("Error transforming variable y: ") + e.what()), current_statement_begin__, prog_reader__());
122 : }
123 :
124 : params_r__ = writer__.data_r();
125 : params_i__ = writer__.data_i();
126 : }
127 :
128 : void transform_inits(const stan::io::var_context& context,
129 : Eigen::Matrix<double, Eigen::Dynamic, 1>& params_r,
130 : std::ostream
pstream__) const {
131 : std::vector params_r_vec;
132 : std::vector params_i_vec;
133 : transform_inits(context, params_i_vec, params_r_vec, pstream__);
134 : params_r.resize(params_r_vec.size());
135 : for (int i = 0; i < params_r.size(); ++i)
136 : params_r(i) = params_r_vec[i];
137 : }
138 :
139 :
140 : template <bool propto__, bool jacobian__, typename T__>
141 : T__ log_prob(std::vector<T__>& params_r__,
142 : std::vector& params_i__,
143 : std::ostream
pstream__ = 0) const {
144 :
145 : typedef T__ local_scalar_t__;
146 :
147 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());
148 : (void) DUMMY_VAR__; // dummy to suppress unused var warning
149 :
150 : T__ lp__(0.0);
151 : stan::math::accumulator<T__> lp_accum__;
152 : try {
153 : stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
154 :
155 : // model parameters
156 : current_statement_begin__ = 1;
157 : local_scalar_t__ y;
158 : (void) y; // dummy to suppress unused var warning
159 : if (jacobian__)
160 : y = in__.scalar_constrain(lp__);
161 : else
162 : y = in__.scalar_constrain();
163 :
164 : // model body
165 :
166 : current_statement_begin__ = 1;
167 : lp_accum__.add(normal_log<propto__>(y, y_mean, 1));
168 :
169 : } catch (const std::exception& e) {
170 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
171 : // Next line prevents compiler griping about no return
172 : throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ");
173 : }
174 :
175 : lp_accum__.add(lp__);
176 : return lp_accum__.sum();
177 :
178 : } // log_prob()
179 :
180 : template <bool propto, bool jacobian, typename T_>
181 : T_ log_prob(Eigen::Matrix<T_,Eigen::Dynamic,1>& params_r,
182 : std::ostream
pstream = 0) const {
183 : std::vector<T_> vec_params_r;
184 : vec_params_r.reserve(params_r.size());
185 : for (int i = 0; i < params_r.size(); ++i)
186 : vec_params_r.push_back(params_r(i));
187 : std::vector vec_params_i;
188 : return log_prob<propto,jacobian,T_>(vec_params_r, vec_params_i, pstream);
189 : }
190 :
191 :
192 : void get_param_names(std::vectorstd::string& names__) const {
193 : names__.resize(0);
194 : names__.push_back(“y”);
195 : }
196 :
197 :
198 : void get_dims(std::vector<std::vector<size_t> >& dimss__) const {
199 : dimss__.resize(0);
200 : std::vector<size_t> dims__;
201 : dims__.resize(0);
202 : dimss__.push_back(dims__);
203 : }
204 :
205 : template
206 : void write_array(RNG& base_rng__,
207 : std::vector& params_r__,
208 : std::vector& params_i__,
209 : std::vector& vars__,
210 : bool include_tparams__ = true,
211 : bool include_gqs__ = true,
212 : std::ostream
pstream__ = 0) const {
213 : typedef double local_scalar_t__;
214 :
215 : vars__.resize(0);
216 : stan::io::reader<local_scalar_t__> in__(params_r__, params_i__);
217 : static const char
function__ = “model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::write_array”;
218 : (void) function__; // dummy to suppress unused var warning
219 :
220 : // read-transform, write parameters
221 : double y = in__.scalar_constrain();
222 : vars__.push_back(y);
223 :
224 : double lp__ = 0.0;
225 : (void) lp__; // dummy to suppress unused var warning
226 : stan::math::accumulator lp_accum__;
227 :
228 : local_scalar_t__ DUMMY_VAR__(std::numeric_limits::quiet_NaN());
229 : (void) DUMMY_VAR__; // suppress unused var warning
230 :
231 : if (!include_tparams__ && !include_gqs__) return;
232 :
233 : try {
234 : if (!include_gqs__ && !include_tparams__) return;
235 : if (!include_gqs__) return;
236 : } catch (const std::exception& e) {
237 : stan::lang::rethrow_located(e, current_statement_begin__, prog_reader__());
238 : // Next line prevents compiler griping about no return
239 : throw std::runtime_error("*** IF YOU SEE THIS, PLEASE REPORT A BUG ");
240 : }
241 : }
242 :
243 : template
244 : void write_array(RNG& base_rng,
245 : Eigen::Matrix<double,Eigen::Dynamic,1>& params_r,
246 : Eigen::Matrix<double,Eigen::Dynamic,1>& vars,
247 : bool include_tparams = true,
248 : bool include_gqs = true,
249 : std::ostream
pstream = 0) const {
250 : std::vector params_r_vec(params_r.size());
251 : for (int i = 0; i < params_r.size(); ++i)
252 : params_r_vec[i] = params_r(i);
253 : std::vector vars_vec;
254 : std::vector params_i_vec;
255 : write_array(base_rng, params_r_vec, params_i_vec, vars_vec, include_tparams, include_gqs, pstream);
256 : vars.resize(vars_vec.size());
257 : for (int i = 0; i < vars.size(); ++i)
258 : vars(i) = vars_vec[i];
259 : }
260 :
261 : static std::string model_name() {
262 : return “model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1”;
263 : }
264 :
265 :
266 : void constrained_param_names(std::vectorstd::string& param_names__,
267 : bool include_tparams__ = true,
268 : bool include_gqs__ = true) const {
269 : std::stringstream param_name_stream__;
270 : param_name_stream__.str(std::string());
271 : param_name_stream__ << “y”;
272 : param_names__.push_back(param_name_stream__.str());
273 :
274 : if (!include_gqs__ && !include_tparams__) return;
275 :
276 : if (include_tparams__) {
277 : }
278 :
279 : if (!include_gqs__) return;
280 : }
281 :
282 :
283 : void unconstrained_param_names(std::vectorstd::string& param_names__,
284 : bool include_tparams__ = true,
285 : bool include_gqs__ = true) const {
286 : std::stringstream param_name_stream__;
287 : param_name_stream__.str(std::string());
288 : param_name_stream__ << “y”;
289 : param_names__.push_back(param_name_stream__.str());
290 :
291 : if (!include_gqs__ && !include_tparams__) return;
292 :
293 : if (include_tparams__) {
294 : }
295 :
296 : if (!include_gqs__) return;
297 : }
298 :
299 : }; // model
300 :
301 : } // namespace
302 :
303 : typedef model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1 stan_model;
304 :
305 : /

306 : * Define Rcpp Module to expose stan_fit’s functions to R.
307 : */
308 : RCPP_MODULE(stan_fit4model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_mod){
309 : Rcpp::class_<rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1,
310 : boost::random::ecuyer1988> >(“stan_fit4model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1”)
311 : // .constructorRcpp::List()
312 : .constructor<SEXP, SEXP, SEXP>()
313 : // .constructor<SEXP, SEXP>()
314 : .method(“call_sampler”,
315 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::call_sampler)
316 : .method(“param_names”,
317 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_names)
318 : .method(“param_names_oi”,
319 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_names_oi)
320 : .method(“param_fnames_oi”,
321 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_fnames_oi)
322 : .method(“param_dims”,
323 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_dims)
324 : .method(“param_dims_oi”,
325 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_dims_oi)
326 : .method(“update_param_oi”,
327 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::update_param_oi)
328 : .method(“param_oi_tidx”,
329 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::param_oi_tidx)
330 : .method(“grad_log_prob”,
331 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::grad_log_prob)
332 : .method(“log_prob”,
333 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::log_prob)
334 : .method(“unconstrain_pars”,
335 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::unconstrain_pars)
336 : .method(“constrain_pars”,
337 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::constrain_pars)
338 : .method(“num_pars_unconstrained”,
339 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::num_pars_unconstrained)
340 : .method(“unconstrained_param_names”,
341 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::unconstrained_param_names)
342 : .method(“constrained_param_names”,
343 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::constrained_param_names)
344 : .method(“standalone_gqs”,
345 : &rstan::stan_fit<model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1_namespace::model2a80454e5ad0_73fc79f8b1915e8208c736914c86d1a1, boost::random::ecuyer1988>::standalone_gqs)
346 : ;
347 : }
348 :
349 : // declarations
350 : extern “C” {
351 : SEXP file2a80750e7690( ) ;
352 : }
353 :
354 : // definition
355 :
356 : SEXP file2a80750e7690( ){
357 : return Rcpp::wrap(“73fc79f8b1915e8208c736914c86d1a1”);
358 : }
359 :
360 :
Compilation argument:
C:/Program Files/R/R-40~1.0/bin/x64/R CMD SHLIB file2a80750e7690.cpp 2> file2a80750e7690.cpp.err.txt
Error in file(con, “r”) : cannot open the connection
In addition: Warning message:
In file(con, “r”) :
cannot open file ‘file2a80750e7690.cpp.err.txt’: No such file or directory

Possibly relevant

Yes I noticed that so I reinstalled rtools40 again. Got the same error. Are you aware of any solution for this? Currently rtools40 is installed under C:/rtools40?

You may have some residuals of old Rtools left somewhere. Possible locations are you PATH and the .Rprofile file in your documents folder. I had a same problem at some point and removing traces from the old Rtools in these places helped.

Thanks for your tips.
For Path, the results are:
[1] “C:\rtools40\usr\bin;C:\Program Files\R\R-4.0.0\bin\x64;C:\Program Files (x86)\Common Files\Oracle\Java\javapath;C:\WINDOWS\system32;C:\WINDOWS;C:\WINDOWS\System32\Wbem;C:\WINDOWS\System32\WindowsPowerShell\v1.0\;C:\Program Files\Dell\Dell Data Protection\Encryption\;C:\Program Files\MiKTeX 2.9\miktex\bin\x64\;C:\Program Files\SASHome\SASFoundation\9.4\ets\sasexe;C:\Program Files\SASHome\Secure\ccme4;C:\Program Files (x86)\Webex\Plugins;C:\Users\fur\AppData\Local\Microsoft\WindowsApps;”

  • Anything abnormal? ( I don’t see any but my knowledge on this is very limited.)

And I run “file.edit(”~/.Rprofile")" , the file is completely empty, maybe because I just re-installed everything? Is it the correct way to edit ~/.Rprofile?

Thanks!

Try to remove the rtools40 stuff from your PATH. If you followed the native install instructions or rtools, this shouldn’t be required.

I had issues with R using the old Rtools because I had set the BINPREF environment variable to the old installation.

If you call Sys.getenv("BINPREF") and the output isn’t "", then that could be the issue

The rtools40 instructions include: writeLines('PATH="${RTOOLS40_HOME}\\usr\\bin;${PATH}"', con = "~/.Renviron")

I got the same ‘rtools has been deleted’ warning before I ran that.

I’d recommend just starting with a clean Renviron and Makevars.win, then run that line above, and see whether it works.

I had similar error messages after installing R 4.0. Installing Rcpp, RcppEigen and rstan together from source worked for me.
install.packages(c("Rcpp", "RcppEigen", "rstan"), type = "source")

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Got this same issue and

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

What should I do? Can you provide code to set the BINPREF environment variable to the new Rtools installation? Thanks so much!

In your R HOME directory (usually the Documents folder on Windows), you should have a .Rprofile file (you can check where this is using: file.path(Sys.getenv("HOME"), ".Rprofile")).

Open up that file with a text editor (like Notepad) and delete the line: Sys.setenv(BINPREF = "C:/Rtools/mingw_$(WIN)/bin/")

After that, restart R and run:

Sys.which("g++")

If that returns something with C:\\rtools40 at the start, you should be good to go

Hi all,

I had similar error after updating to R4.0. My model no longer run.

Turned out it was enough to remove compiled models and recompile with new RTools.

Hope someone will find this useful.

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I had the same issue and had to go into my R library and uninstall Rcpp and reinstall both it and rstan. I’m wondering if I updating packages prior to installing updated rtools was the culprit.

Thanks for all of your help!

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Yep, same here, reinstalling both rcpp and rstan did the trick.

1 Like