- factor.mix: Mixed Data Factor Analysis
- factor.ord: Ordinal Data Factor Analysis
- gamma: Gamma Regression for Continuous, Positive Dependent Variables
- irt1d: One Dimensional Item Response Model
- irtkd:
-Dimensional Item Response Theory Model
- logit: Logistic Regression for Dichotomous Dependent Variables
- logit.bayes: Bayesian Logistic Regression
- lognorm: Log-Normal Regression for Duration Dependent Variables
- ls: Least Squares Regression for Continuous Dependent Variables
- mlogit: Multinomial Logistic Regression for Dependent Variables with Unordered Categorical Values
- mlogit.bayes: Bayesian Multinomial Logistic Regression
- mloglm: Multinomial Log-Linear Regression for Contingency Table Models
- negbin: Negative Binomial Regression for Event Count Dependent Variables
- netls: Network Least Squares Regression for Continuous Proximity Matrix Dependent Variables
- netlogit: Network Logistic Regression for Dichotomous Proximity Matrix Dependent Variables
- normal: Normal Regression for Continuous Dependent Variables
- normal.bayes: Bayesian Normal Linear Regression
- ologit: Ordinal Logistic Regression for Ordered Categorical Dependent Variables
- oprobit: Ordinal Probit Regression for Ordered Categorical Dependent Variables
- oprobit.bayes: Bayesian Ordered Probit Regression
- poisson: Poisson Regression for Event Count Dependent Variables
- poisson.bayes: Bayesian Poisson Regression
- probit: Probit Regression for Dichotomous Dependent Variables
- probit.bayes: Bayesian Probit Regression
- relogit: Rare Events Logistic Regression for
Dichotomous Dependent Variables
- Syntax
- Arguments
- Example 1: One Tau with Prior Correction and Bias Correction
- Example 2: One Tau with Weighting, Robust Standard Errors, and Bias Correction
- Example 3: Two Taus with Bias Correction and Prior Correction
- Model
- Quantities of Interest
- Output Values
- Differences with Stata Version
- Contributors
- tobit: Linear Regression for a Left-Censored Dependent Variable
- tobit.bayes: Bayesian Linear Regression for a Censored Dependent Variable
- weibull: Weibull Regression for Duration Dependent Variables
- describe: Describe a model's systematic and stochastic parameters
- model.end: Cleaning up after optimization
- model.frame.multiple: Extracting the ``environment'' of a model formula
- model.matrix.multiple: Design matrix for multivariate models
- parse.formula: Parsing the inputs
- parse.par: Select and reshape parameter vectors
- put.start: Set specific starting values for certain parameters
- set.start: Set starting values for all parameters
- tag: Constrain parameter effects across equations