bellreg

The goal of bellreg is to provide a set of functions to fit regression models for count data with overdispersion using the Bell distribution. The implemented models account for ordinary and zero-inflated regression models under both frequentist and Bayesian approaches. Theoretical details regarding the models implemented in the package can be found in Castellares et al. (2018) doi:10.1016/j.apm.2017.12.014 and Lemonte et al. (2020) doi:10.1080/02664763.2019.1636940.

Installation

You can install the development version of bellreg from GitHub with:

# install.packages("devtools")
devtools::install_github("fndemarqui/bellreg")

Example

library(bellreg)

data(faults)

# ML approach:
mle <- bellreg(nf ~ lroll, data = faults, approach = "mle", init = 0)
summary(mle)
#> Call:
#> bellreg(formula = nf ~ lroll, data = faults, approach = "mle", 
#>     init = 0)
#> 
#> Coefficients:
#>               Estimate     StdErr z.value   p.value    
#> (Intercept) 0.98524220 0.33219474  2.9659  0.003018 ** 
#> lroll       0.00190934 0.00049004  3.8963 9.766e-05 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> logLik = -88.96139   AIC = 181.9228

# Bayesian approach:
bayes <- bellreg(nf ~ lroll, data = faults, approach = "bayes", refresh = FALSE)
summary(bayes)
#> 
#> bellreg(formula = nf ~ lroll, data = faults, approach = "bayes", 
#>     refresh = FALSE)
#> 
#>              mean se_mean    sd  2.5%   25%   50%   75% 97.5%    n_eff Rhat
#> (Intercept) 0.974   0.007 0.341 0.305 0.751 0.967 1.205 1.642 2459.956    1
#> lroll       0.002   0.000 0.000 0.001 0.002 0.002 0.002 0.003 2728.380    1
#> 
#> Inference for Stan model: bellreg.
#> 4 chains, each with iter=2000; warmup=1000; thin=1; 
#> post-warmup draws per chain=1000, total post-warmup draws=4000.