I’m trying to do what CmdStan is doing (minus writing and reading text files) using pure C++ code.

For example, using the generated CommandStan model executable, I would do this:

```
./model data file=data.R output file=sampleOutput.csv random seed=1234567890 sample num_samples=1000 num_warmup=1000
```

Digging through unit tests I have been able to figure out part of it (some code omitted for brevity):

```
model_namespace::model *myModel = new model_namespace::model(varContext, seed, &os);
stan::mcmc::unit_e_static_uniform<stan_model, rng_t>
sampler(*myModel, base_rng);
sampler.z() = z_init;
sampler.init_hamiltonian(logger);
sampler.set_nominal_stepsize(0.1);
sampler.set_stepsize_jitter(0);
sampler.sample_stepsize();
stan::mcmc::sample init_sample(z_init.q, 0, 0);
stan::mcmc::sample s = sampler.transition(init_sample, logger);
```

Ultimately I would like to be able to, through c++, get my parameter’s mean and standard deviation.

I’m by no means an expert in this field so it’s taking me a large amount of time to dig through the source code trying to find the proper procedure. I’ve found some documentation explaining the model concept, but I haven’t been able to find details on what I’m looking to do. So I was hoping for a nudge in the right direction from someone that understands this better than I.