Stan User's Guide
Overview
Part 1. Example Models
1 Regression Models
2 Time-Series Models
3 Missing Data and Partially Known Parameters
4 Truncated or Censored Data
5 Finite Mixtures
6 Measurement Error and Meta-Analysis
- 6.1 Bayesian measurement error model
  - Regression with measurement error
  - Rounding
- 6.2 Meta-analysis
  - Treatment effects in controlled studies
7 Latent Discrete Parameters
8 Sparse and Ragged Data Structures
- 8.1 Sparse data structures
- 8.2 Ragged data structures
9 Clustering Models
10 Gaussian Processes
11 Directions, Rotations, and Hyperspheres
12 Solving Algebraic Equations
13 Ordinary Differential Equations
14 Computing One Dimensional Integrals
- 14.1 Calling the integrator
  - 14.1.1 Limits of integration
  - 14.1.2 Data vs. parameters
- 14.2 Integrator convergence
  - 14.2.1 Zero-crossing integrals
  - 14.2.2 Avoiding precision loss near limits of integration in definite integrals
15 Complex Numbers
16 Differential-Algebraic Equations
Part 2. Programming Techniques
17 Floating Point Arithmetic
18 Matrices, Vectors, Arrays, and Tuples
19 Multiple Indexing and Range Indexing
20 User-Defined Functions
21 Custom Probability Functions
- 21.1 Examples
22 Proportionality Constants
23 Problematic Posteriors
24 Reparameterization and Change of Variables
25 Efficiency Tuning
26 Parallelization
Part 3. Posterior Inference & Model Checking
27 Posterior Predictive Sampling
28 Simulation-Based Calibration
29 Posterior and Prior Predictive Checks
30 Held-Out Evaluation and Cross-Validation
31 Poststratification
32 Decision Analysis
33 The Bootstrap and Bagging
Appendices
34 Using the Stan Compiler
35 Stan Program Style Guide
36 Transitioning from BUGS
References

Stan User’s Guide

This is an old version, view current version.

1 Regression Models

Stan supports regression models from simple linear regressions to multilevel generalized linear models.