vignettes/cmdstanr.Rmd
cmdstanr.Rmd
# we recommend running this is a fresh R session or restarting your current session
-install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))We can now load the package like any other R package. We’ll also load the bayesplot and posterior packages to use later in examples.
@@ -277,30 +277,30 @@Running MCMC with 4 parallel chains...
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@@ -564,11 +564,11 @@ Optimization$optimize().
fit_mle <- mod$optimize(data = data_list, seed = 123)
-Initial log joint probability = -9.51104
- Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
- 6 -5.00402 0.000103557 2.55661e-07 1 1 9
-Optimization terminated normally:
- Convergence detected: relative gradient magnitude is below tolerance
+Initial log joint probability = -9.51104
+ Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
+ 6 -5.00402 0.000103557 2.55661e-07 1 1 9
+Optimization terminated normally:
+ Convergence detected: relative gradient magnitude is below tolerance
Finished in 0.2 seconds.
fit_mle$print() # includes lp__ (log prob calculated by Stan program)
@@ -598,11 +598,11 @@ Optimization= TRUE,
seed = 123
)
-Initial log joint probability = -11.0088
- Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
- 5 -6.74802 0.000938344 1.39149e-05 1 1 8
-Optimization terminated normally:
- Convergence detected: relative gradient magnitude is below tolerance
+Initial log joint probability = -11.0088
+ Iter log prob ||dx|| ||grad|| alpha alpha0 # evals Notes
+ 5 -6.74802 0.000938344 1.39149e-05 1 1 8
+Optimization terminated normally:
+ Convergence detected: relative gradient magnitude is below tolerance
Finished in 0.1 seconds.
@@ -629,13 +629,13 @@ Laplace Approximation seed = 123,
refresh = 1000
)
-Calculating Hessian
-Calculating inverse of Cholesky factor
-Generating draws
-iteration: 0
-iteration: 1000
-iteration: 2000
-iteration: 3000
+Calculating Hessian
+Calculating inverse of Cholesky factor
+Generating draws
+iteration: 0
+iteration: 1000
+iteration: 2000
+iteration: 3000
Finished in 0.1 seconds.
fit_laplace$print("theta")
@@ -658,28 +658,28 @@ Variational (ADVI)= 123,
draws = 4000
)
-------------------------------------------------------------
-EXPERIMENTAL ALGORITHM:
- This procedure has not been thoroughly tested and may be unstable
- or buggy. The interface is subject to change.
-------------------------------------------------------------
-Gradient evaluation took 5e-06 seconds
-1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
-Adjust your expectations accordingly!
-Begin eta adaptation.
-Iteration: 1 / 250 [ 0%] (Adaptation)
-Iteration: 50 / 250 [ 20%] (Adaptation)
-Iteration: 100 / 250 [ 40%] (Adaptation)
-Iteration: 150 / 250 [ 60%] (Adaptation)
-Iteration: 200 / 250 [ 80%] (Adaptation)
-Success! Found best value [eta = 1] earlier than expected.
-Begin stochastic gradient ascent.
- iter ELBO delta_ELBO_mean delta_ELBO_med notes
- 100 -6.262 1.000 1.000
- 200 -6.263 0.500 1.000
- 300 -6.307 0.336 0.007 MEDIAN ELBO CONVERGED
-Drawing a sample of size 4000 from the approximate posterior...
-COMPLETED.
+------------------------------------------------------------
+EXPERIMENTAL ALGORITHM:
+ This procedure has not been thoroughly tested and may be unstable
+ or buggy. The interface is subject to change.
+------------------------------------------------------------
+Gradient evaluation took 5e-06 seconds
+1000 transitions using 10 leapfrog steps per transition would take 0.05 seconds.
+Adjust your expectations accordingly!
+Begin eta adaptation.
+Iteration: 1 / 250 [ 0%] (Adaptation)
+Iteration: 50 / 250 [ 20%] (Adaptation)
+Iteration: 100 / 250 [ 40%] (Adaptation)
+Iteration: 150 / 250 [ 60%] (Adaptation)
+Iteration: 200 / 250 [ 80%] (Adaptation)
+Success! Found best value [eta = 1] earlier than expected.
+Begin stochastic gradient ascent.
+ iter ELBO delta_ELBO_mean delta_ELBO_med notes
+ 100 -6.262 1.000 1.000
+ 200 -6.263 0.500 1.000
+ 300 -6.307 0.336 0.007 MEDIAN ELBO CONVERGED
+Drawing a sample of size 4000 from the approximate posterior...
+COMPLETED.
Finished in 0.1 seconds.
fit_vb$print("theta")
@@ -703,23 +703,23 @@ Variational (Pathfinder) seed = 123,
draws = 4000
)
-Path [1] :Initial log joint density = -11.008832
-Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
- 5 -6.748e+00 9.383e-04 1.391e-05 1.000e+00 1.000e+00 126 -6.264e+00 -6.264e+00
-Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126)
-Path [2] :Initial log joint density = -7.318450
-Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
- 4 -6.748e+00 5.414e-03 1.618e-04 1.000e+00 1.000e+00 101 -6.251e+00 -6.251e+00
-Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101)
-Path [3] :Initial log joint density = -12.374612
-Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
- 5 -6.748e+00 1.419e-03 2.837e-05 1.000e+00 1.000e+00 126 -6.199e+00 -6.199e+00
-Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126)
-Path [4] :Initial log joint density = -13.009824
-Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
- 5 -6.748e+00 1.677e-03 3.885e-05 1.000e+00 1.000e+00 126 -6.173e+00 -6.173e+00
-Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126)
-Total log probability function evaluations:4379
+Path [1] :Initial log joint density = -11.008832
+Path [1] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+ 5 -6.748e+00 9.383e-04 1.391e-05 1.000e+00 1.000e+00 126 -6.264e+00 -6.264e+00
+Path [1] :Best Iter: [3] ELBO (-6.195408) evaluations: (126)
+Path [2] :Initial log joint density = -7.318450
+Path [2] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+ 4 -6.748e+00 5.414e-03 1.618e-04 1.000e+00 1.000e+00 101 -6.251e+00 -6.251e+00
+Path [2] :Best Iter: [3] ELBO (-6.229174) evaluations: (101)
+Path [3] :Initial log joint density = -12.374612
+Path [3] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+ 5 -6.748e+00 1.419e-03 2.837e-05 1.000e+00 1.000e+00 126 -6.199e+00 -6.199e+00
+Path [3] :Best Iter: [5] ELBO (-6.199185) evaluations: (126)
+Path [4] :Initial log joint density = -13.009824
+Path [4] : Iter log prob ||dx|| ||grad|| alpha alpha0 # evals ELBO Best ELBO Notes
+ 5 -6.748e+00 1.677e-03 3.885e-05 1.000e+00 1.000e+00 126 -6.173e+00 -6.173e+00
+Path [4] :Best Iter: [5] ELBO (-6.172860) evaluations: (126)
+Total log probability function evaluations:4379
Finished in 0.1 seconds.
fit_pf$print("theta")
@@ -902,10 +902,10 @@ Additional resources
-
+
-
+
@@ -168,7 +168,7 @@ Installing the R packageYou can install the latest beta release of the cmdstanr R package with
# we recommend running this is a fresh R session or restarting your current session
-install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
+install.packages("cmdstanr", repos = c("https://stan-dev.r-universe.dev", getOption("repos")))
This does not install the vignettes, which take a long time to build, but they are always available online at https://mc-stan.org/cmdstanr/articles/.
To instead install the latest development version of the package from GitHub use
@@ -259,10 +259,10 @@ Developers
-
-
+
+