there’s a bit more to be gleaned from looking at the raw help output - here’s what latest dev version of CmdStan says:
method=<list element>
Analysis method (Note that method= is optional)
Valid values: sample, optimize, variational, diagnose, generate_quantities
Defaults to sample
sample
Bayesian inference with Markov Chain Monte Carlo
Valid subarguments: num_samples, num_warmup, save_warmup, thin, adapt, algorithm
num_samples=<int>
Number of sampling iterations
Valid values: 0 <= num_samples
Defaults to 1000
num_warmup=<int>
Number of warmup iterations
Valid values: 0 <= warmup
Defaults to 1000
save_warmup=<boolean>
Stream warmup samples to output?
Valid values: [0, 1]
Defaults to 0
thin=<int>
Period between saved samples
Valid values: 0 < thin
Defaults to 1
adapt
Warmup Adaptation
Valid subarguments: engaged, gamma, delta, kappa, t0, init_buffer, term_buffer, window
engaged=<boolean>
Adaptation engaged?
Valid values: [0, 1]
Defaults to 1
gamma=<double>
Adaptation regularization scale
Valid values: 0 < gamma
Defaults to 0.05
delta=<double>
Adaptation target acceptance statistic
Valid values: 0 < delta < 1
Defaults to 0.8
kappa=<double>
Adaptation relaxation exponent
Valid values: 0 < kappa
Defaults to 0.75
t0=<double>
Adaptation iteration offset
Valid values: 0 < t0
Defaults to 10
init_buffer=<unsigned int>
Width of initial fast adaptation interval
Valid values: All
Defaults to 75
term_buffer=<unsigned int>
Width of final fast adaptation interval
Valid values: All
Defaults to 50
window=<unsigned int>
Initial width of slow adaptation interval
Valid values: All
Defaults to 25
algorithm=<list element>
Sampling algorithm
Valid values: hmc, fixed_param
Defaults to hmc
hmc
Hamiltonian Monte Carlo
Valid subarguments: engine, metric, metric_file, stepsize, stepsize_jitter
engine=<list element>
Engine for Hamiltonian Monte Carlo
Valid values: static, nuts
Defaults to nuts
static
Static integration time
Valid subarguments: int_time
int_time=<double>
Total integration time for Hamiltonian evolution
Valid values: 0 < int_time
Defaults to 2 * pi
nuts
The No-U-Turn Sampler
Valid subarguments: max_depth
max_depth=<int>
Maximum tree depth
Valid values: 0 < max_depth
Defaults to 10
metric=<list element>
Geometry of base manifold
Valid values: unit_e, diag_e, dense_e
Defaults to diag_e
unit_e
Euclidean manifold with unit metric
diag_e
Euclidean manifold with diag metric
dense_e
Euclidean manifold with dense metric
metric_file=<string>
Input file with precomputed Euclidean metric
Valid values: Path to existing file
Defaults to ""
stepsize=<double>
Step size for discrete evolution
Valid values: 0 < stepsize
Defaults to 1
stepsize_jitter=<double>
Uniformly random jitter of the stepsize, in percent
Valid values: 0 <= stepsize_jitter <= 1
Defaults to 0
fixed_param
Fixed Parameter Sampler
optimize
Point estimation
Valid subarguments: algorithm, iter, save_iterations
algorithm=<list element>
Optimization algorithm
Valid values: bfgs, lbfgs, newton
Defaults to lbfgs
bfgs
BFGS with linesearch
Valid subarguments: init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param
init_alpha=<double>
Line search step size for first iteration
Valid values: 0 < init_alpha
Defaults to 0.001
tol_obj=<double>
Convergence tolerance on absolute changes in objective function value
Valid values: 0 <= tol
Defaults to 9.9999999999999998e-13
tol_rel_obj=<double>
Convergence tolerance on relative changes in objective function value
Valid values: 0 <= tol
Defaults to 10000
tol_grad=<double>
Convergence tolerance on the norm of the gradient
Valid values: 0 <= tol
Defaults to 1e-08
tol_rel_grad=<double>
Convergence tolerance on the relative norm of the gradient
Valid values: 0 <= tol
Defaults to 10000000
tol_param=<double>
Convergence tolerance on changes in parameter value
Valid values: 0 <= tol
Defaults to 1e-08
lbfgs
LBFGS with linesearch
Valid subarguments: init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param, history_size
init_alpha=<double>
Line search step size for first iteration
Valid values: 0 < init_alpha
Defaults to 0.001
tol_obj=<double>
Convergence tolerance on absolute changes in objective function value
Valid values: 0 <= tol
Defaults to 9.9999999999999998e-13
tol_rel_obj=<double>
Convergence tolerance on relative changes in objective function value
Valid values: 0 <= tol
Defaults to 10000
tol_grad=<double>
Convergence tolerance on the norm of the gradient
Valid values: 0 <= tol
Defaults to 1e-08
tol_rel_grad=<double>
Convergence tolerance on the relative norm of the gradient
Valid values: 0 <= tol
Defaults to 10000000
tol_param=<double>
Convergence tolerance on changes in parameter value
Valid values: 0 <= tol
Defaults to 1e-08
history_size=<int>
Amount of history to keep for L-BFGS
Valid values: 0 < history_size
Defaults to 5
newton
Newton's method
iter=<int>
Total number of iterations
Valid values: 0 < iter
Defaults to 2000
save_iterations=<boolean>
Stream optimization progress to output?
Valid values: [0, 1]
Defaults to 0
variational
Variational inference
Valid subarguments: algorithm, iter, grad_samples, elbo_samples, eta, adapt, tol_rel_obj, eval_elbo, output_samples
algorithm=<list element>
Variational inference algorithm
Valid values: meanfield, fullrank
Defaults to meanfield
meanfield
mean-field approximation
fullrank
full-rank covariance
iter=<int>
Maximum number of ADVI iterations.
Valid values: 0 < iter
Defaults to 10000
grad_samples=<int>
Number of Monte Carlo draws for computing the gradient.
Valid values: 0 < num_samples
Defaults to 1
elbo_samples=<int>
Number of Monte Carlo draws for estimate of ELBO.
Valid values: 0 < num_samples
Defaults to 100
eta=<double>
Stepsize scaling parameter.
Valid values: 0 < eta
Defaults to 1
adapt
Eta Adaptation for Variational Inference
Valid subarguments: engaged, iter
engaged=<boolean>
Boolean flag for eta adaptation.
Valid values: [0, 1]
Defaults to 1
iter=<int>
Number of iterations for eta adaptation.
Valid values: 0 < iter
Defaults to 50
tol_rel_obj=<double>
Relative tolerance parameter for convergence.
Valid values: 0 <= tol
Defaults to 0.01
eval_elbo=<int>
Number of interations between ELBO evaluations
Valid values: 0 < eval_elbo
Defaults to 100
output_samples=<int>
Number of approximate posterior output draws to save.
Valid values: 0 < output_samples
Defaults to 1000
diagnose
Model diagnostics
Valid subarguments: test
test=<list element>
Diagnostic test
Valid values: gradient
Defaults to gradient
gradient
Check model gradient against finite differences
Valid subarguments: epsilon, error
epsilon=<double>
Finite difference step size
Valid values: 0 < epsilon
Defaults to 1e-6
error=<double>
Error threshold
Valid values: 0 < error
Defaults to 1e-6
generate_quantities
Generate quantities of interest
Valid subarguments: fitted_params
fitted_params=<string>
Input file of sample of fitted parameter values for model conditioned on data
Valid values: Path to existing file
Defaults to ""
id=<int>
Unique process identifier
Valid values: id > 0
Defaults to 0
data
Input data options
Valid subarguments: file
file=<string>
Input data file
Valid values: Path to existing file
Defaults to ""
init=<string>
Initialization method: "x" initializes randomly between [-x, x], "0" initializes to 0, anything else identifies a file of values
Valid values: All
Defaults to "2"
random
Random number configuration
Valid subarguments: seed
seed=<unsigned int>
Random number generator seed
Valid values: seed > 0, if negative seed is generated from time
Defaults to -1
output
File output options
Valid subarguments: file, diagnostic_file, refresh
file=<string>
Output file
Valid values: Path to existing file
Defaults to output.csv
diagnostic_file=<string>
Auxiliary output file for diagnostic information
Valid values: Path to existing file
Defaults to ""
refresh=<int>
Number of interations between screen updates
Valid values: 0 <= refresh
Defaults to 100