I am trying to build random parameters model with heterogeneity in mean and variance (RPNB-HMV) models for road traffic crashes on a highway in India.
Random parameter Negative Binomial models are the most popular models for assessing unobserved heterogeneity in crash prediction models.
I want to know some free or open-source software like R for fitting such models.
E.g. A sample syntax of the RPNB-HMV model in R software.
There are several free software options available for building random parameters models for count data with heterogeneity in mean and variance. Here are a few popular ones:
R is one popular free software for building random parameters models for count data with heterogeneity in mean and variance. R is a powerful programming language and software environment for statistical computing and graphics. It has a vast collection of packages specifically designed for various statistical modeling tasks, including count data analysis.
R packages that you can use to build random parameters models for count data:
1. lme4: The lme4 package provides functions for fitting linear mixed-effects models, including models with random parameters. It uses a maximum likelihood estimation approach and supports various model specifications and random effects structures. You can use this package to build random parameters models for count data with heterogeneity in mean and variance.
2. GLMMadaptive: The GLMMadaptive package is specifically designed for fitting generalized linear mixed models (GLMMs) with adaptive Gaussian quadrature for efficient estimation. It supports various distributions, including Poisson and negative binomial, which are commonly used for count data. GLMMadaptive allows for modeling heterogeneity in mean and variance, making it suitable for building random parameters models.
3. brms: The brms package provides a flexible interface to fit Bayesian multilevel models using a syntax similar to lme4. It utilizes Stan, a powerful probabilistic programming language, for Bayesian inference. brms allows for specifying complex models with random effects and supports a wide range of count distributions, including zero-inflated and hurdle models.
4. MCMCglmm: The MCMCglmm package is focused on fitting multilevel models using Markov chain Monte Carlo (MCMC) methods. It provides a Bayesian framework for estimating random parameters models with different variance components. While it doesn't have built-in support for count data distributions, you can use transformations or link functions to model count outcomes.
These packages offer different approaches and functionalities for building random parameters models for count data in R. They provide flexibility, extensive documentation, and user communities for support. You can install these packages in R using the `install.packages()` function and access their documentation and examples through the R help system.
Good luck
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