• CmdStan User's Guide
  • Introduction
  • QuickStart Guide
  • 1 CmdStan Installation
    • 1.1 Installation via conda
      • 1.1.1 CmdStan install location under conda
    • 1.2 Installation from GitHub
      • 1.2.1 Downloading the source code
      • 1.2.2 Building CmdStan
    • 1.3 Checking the Stan compiler
    • 1.4 Troubleshooting the installation
      • 1.4.1 Common problems
    • 1.5 C++ Toolchain
    • 1.6 Using GNU Make
  • 2 Example Model and Data
  • 3 Compiling a Stan Program
    • 3.1 Invoking the Make utility
    • 3.2 Dependencies
    • 3.3 Compiler errors
    • 3.4 Troubleshooting C++ compiler or linker errors
    • 3.5 C++ compilation and linking flags
      • 3.5.1 Optimizing by ignoring range checks
  • 4 MCMC Sampling
    • 4.1 Running the sampler
    • 4.2 Running multiple chains
      • 4.2.1 Using the num_chains argument to run multiple chains
      • 4.2.2 Using shell for running multiple chains
    • 4.3 Stan CSV output file
    • 4.4 Summarizing sampler output(s) with stansummary
  • 5 Optimization
  • 6 Variational Inference using Pathfinder
  • 7 Variational Inference using ADVI
  • 8 Generating Quantities of Interest from a Fitted Model
  • Reference Manual
  • 9 Command-Line Interface Overview
    • 9.1 Input data argument
    • 9.2 Output control arguments
    • 9.3 Initialize model parameters argument
    • 9.4 Random number generator arguments
    • 9.5 Chain identifier argument: id
    • 9.6 Command line help
    • 9.7 Error messages and return codes
  • 10 MCMC Sampling using Hamiltonian Monte Carlo
    • 10.1 Iterations
    • 10.2 Adaptation
      • 10.2.1 Step size optimization configuration
      • 10.2.2 Warmup schedule configuration
    • 10.3 Algorithm
      • 10.3.1 Samples from a set of fixed parameters
      • 10.3.2 HMC samplers
    • 10.4 Sampler diagnostic file
    • 10.5 Multiple chains in one executable
    • 10.6 Examples - older parallelism
      • 10.6.1 Running multiple chains with a specified RNG seed
      • 10.6.2 Changing the default warmup and sampling iterations
      • 10.6.3 Saving warmup draws
      • 10.6.4 Initializing parameters
      • 10.6.5 Specifying the metric and stepsize
      • 10.6.6 Changing the NUTS-HMC adaptation parameters
      • 10.6.7 Increasing the tree-depth
      • 10.6.8 Capturing Hamiltonian diagnostics and gradients
      • 10.6.9 Suppressing progress updates to the console
      • 10.6.10 Everything example
  • 11 Maximum Likelihood Estimation
    • 11.1 Jacobian adjustments
    • 11.2 Optimization algorithms
    • 11.3 The quasi-Newton optimizers
    • 11.4 The Newton optimizer
  • 12 Pathfinder Method for Approximate Bayesian Inference
    • 12.1 Pathfinder Configuration
    • 12.2 L-BFGS Configuration
    • 12.3 Multi-path Pathfinder CSV files
    • 12.4 Single-path Pathfinder Outputs.
  • 13 Variational Inference Algorithm: ADVI
    • 13.1 Variational algorithms
    • 13.2 Configuration
    • 13.3 CSV output
  • 14 Standalone Generate Quantities
  • 15 Laplace sampling
    • 15.1 Configuration
    • 15.2 CSV output
    • 15.3 Example
  • 16 Extracting log probabilities and gradients for diagnostics
    • 16.1 Configuration
    • 16.2 CSV output
  • 17 Diagnosing HMC by Comparison of Gradients
  • 18 Parallelization
    • 18.1 Multi-threading with TBB
      • 18.1.1 Compiling
      • 18.1.2 Running
    • 18.2 Multi-processing with MPI
      • 18.2.1 Dependencies
      • 18.2.2 Compiling
      • 18.2.3 Running
    • 18.3 OpenCL
      • 18.3.1 Dependencies
      • 18.3.2 Compiling
      • 18.3.3 Running
  • CmdStan Tools
  • 19 stanc: Translating Stan to C++
    • 19.1 Instantiating the stanc binary
    • 19.2 The Stan compiler program
  • 20 stansummary: MCMC Output Analysis
    • 20.1 Building the stansummary command
    • 20.2 Running the stansummary program
      • 20.2.1 Sampler parameters
      • 20.2.2 Model parameters and quantities of interest
    • 20.3 Command-line options
  • 21 diagnose: Diagnosing Biased Hamiltonian Monte Carlo Inferences
    • 21.1 Building the diagnose command
    • 21.2 Running the diagnose command
    • 21.3 diagnose warnings and recommendations
      • 21.3.1 Divergent transitions after warmup
      • 21.3.2 Maximum treedepth exceeded
      • 21.3.3 Low E-BFMI values - sampler transitions HMC potential energy.
      • 21.3.4 Low effective sample sizes
      • 21.3.5 High \(\hat{R}\)
  • 22 print (deprecated): MCMC Output Analysis
  • Appendices
  • 23 Stan CSV File Format
    • 23.1 CSV column names and order
    • 23.2 MCMC sampler CSV output
      • 23.2.1 Sampler Stan CSV output file
      • 23.2.2 Diagnostic CSV output file
      • 23.2.3 Profiling CSV output file
    • 23.3 Optimization output
    • 23.4 Variational inference output
    • 23.5 Generate quantities outputs
    • 23.6 Diagnose method outputs
  • 24 JSON Format for CmdStan
    • 24.1 Creating JSON files
    • 24.2 JSON syntax summary
    • 24.3 Stan data types in JSON notation
      • 24.3.1 Empty arrays in JSON
  • 25 RDump Format for CmdStan
    • 25.1 Creating dump files
    • 25.2 Scalar variables
    • 25.3 Sequence variables
    • 25.4 Array variables
    • 25.5 Matrix- and vector-valued variables
      • 25.5.1 Vector dump format
      • 25.5.2 Matrix dump format
      • 25.5.3 Arrays of vectors and matrices
    • 25.6 Complex-valued variables
    • 25.7 Integer- and real-valued variables
      • 25.7.1 Scientific notation
      • 25.7.2 Infinite and not-a-number values
    • 25.8 Quoted variable names
    • 25.9 Line breaks
    • 25.10 BNF grammar for dump data
  • 26 Using external C++ code
    • 26.1 Derivative specializations
    • 26.2 Special functions: RNGs, distributions, editing target
  • Bibliography

CmdStan User’s Guide

22 print (deprecated): MCMC Output Analysis

The print utility is deprecated, but is still available until CmdStan v3.0. Use the stansummary utility instead.