Installation Issue - Eigen.hpp missing

[maxwellpalmer]

I am trying to install birdie, and getting the following error:

In file included from stanExports_multinom.cc:5:
In file included from ./stanExports_multinom.h:6:
../inst/include/rstan/rstaninc.hpp:4:10: fatal error: 'stan/math/prim/mat/fun/Eigen.hpp' file not found
#include <stan/math/prim/mat/fun/Eigen.hpp>
         ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
1 error generated.
make: *** [stanExports_multinom.o] Error 1
ERROR: compilation failed for package ‘birdie’
* removing ‘/Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/library/birdie’

R version 4.3.2 on a mac (aarch64-apple-darwin20). I tried a fresh install of rstan and also tried using a new renv environment.

[CoryMcCartan]

Sorry about this! Looks like there's an issue with finding Stan libraries. Just to check, does rstan::stan_demo("eight_schools") run successfully? If not, then it's likely a Makevars configuration issue that the Stan forums might have some guidance on.

I can also try to pre-compile the package on my end for mac and send you the binary for installation

[maxwellpalmer]

rstan::stan_demo("eight_schools") runs without issues. Output below. I looked into Makevars on the stan forums, but as far as I can tell things are working properly.

> rstan::stan_demo("eight_schools")

> J <- 8

> y <- c(28,  8, -3,  7, -1,  1, 18, 12)

> sigma <- c(15, 10, 16, 11,  9, 11, 10, 18)

> tau <- 25

SAMPLING FOR MODEL 'eight_schools' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 2.3e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 1: 
Chain 1:  Elapsed Time: 0.121 seconds (Warm-up)
Chain 1:                0.082 seconds (Sampling)
Chain 1:                0.203 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL 'eight_schools' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 5e-06 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
Chain 2: 
Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 2: 
Chain 2:  Elapsed Time: 0.104 seconds (Warm-up)
Chain 2:                0.038 seconds (Sampling)
Chain 2:                0.142 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL 'eight_schools' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 6e-06 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3: 
Chain 3: 
Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 3: 
Chain 3:  Elapsed Time: 0.09 seconds (Warm-up)
Chain 3:                0.048 seconds (Sampling)
Chain 3:                0.138 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL 'eight_schools' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 5e-06 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
Chain 4: 
Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
Chain 4: 
Chain 4:  Elapsed Time: 0.088 seconds (Warm-up)
Chain 4:                0.053 seconds (Sampling)
Chain 4:                0.141 seconds (Total)
Chain 4: 
Inference for Stan model: eight_schools.
4 chains, each with iter=2000; warmup=1000; thin=1; 
post-warmup draws per chain=1000, total post-warmup draws=4000.

           mean se_mean   sd   2.5%    25%    50%    75% 97.5% n_eff Rhat
mu         7.71    0.21 5.33  -2.30   4.26   7.93  10.97 17.92   618 1.00
theta[1]  11.51    0.33 8.84  -3.09   5.78  10.49  15.81 32.30   718 1.00
theta[2]   7.61    0.21 6.52  -4.80   3.22   7.71  11.63 20.84   933 1.00
theta[3]   5.73    0.24 7.64 -10.79   1.36   6.23  10.48 19.71  1026 1.00
theta[4]   7.60    0.21 6.86  -6.37   3.25   7.77  11.77 21.18  1050 1.00
theta[5]   4.77    0.22 6.37  -8.57   0.84   5.16   9.00 15.94   803 1.00
theta[6]   5.98    0.23 6.67  -8.12   1.89   6.48  10.40 18.40   821 1.00
theta[7]  10.53    0.25 7.10  -1.52   5.74   9.99  14.54 26.70   825 1.00
theta[8]   8.14    0.25 8.41  -8.17   3.18   7.91  12.91 25.61  1147 1.00
tau        7.04    0.27 5.42   1.35   3.21   5.66   9.11 20.69   390 1.01
lp__     -18.16    0.36 5.12 -27.86 -21.79 -18.28 -14.59 -7.53   207 1.02

Samples were drawn using NUTS(diag_e) at Tue Jan 23 07:45:01 2024.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at 
convergence, Rhat=1).
Warning messages:
1: There were 81 divergent transitions after warmup. See
https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
to find out why this is a problem and how to eliminate them. 
2: Examine the pairs() plot to diagnose sampling problems

3: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#bulk-ess 
4: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#tail-ess

[CoryMcCartan]

Thanks for this. The problem ended up being related to rstan's recent updates.

Everything should be working now. You can also try downloading the attached tarball and installing with remotes::install_local("<path/to/birdie_0.5.0.tgz"), which should let you skip the compilation step completely

birdie_0.5.0.tgz

[maxwellpalmer]

That worked perfectly. I was able to build from source. Thank you!