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

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4 Truncated or Censored Data

Data in which measurements have been truncated or censored can be coded in Stan following their respective probability models.