R: Flexible Modeling of Count Data

# Flexible Modeling of Count Data

## Documentation for package ‘countSTAR’ version 1.0.2

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

a_j | Inverse rounding function
bam_star | Fit Bayesian Additive STAR Model with MCMC
bart_star | MCMC Algorithm for BART-STAR
blm_star | STAR Bayesian Linear Regression
confint.lmstar | Compute asymptotic confidence intervals for STAR linear regression
credBands | Compute Simultaneous Credible Bands
ergMean | Compute the ergodic (running) mean.
gbm_star | Fitting STAR Gradient Boosting Machines via EM algorithm
genEM_star | Generalized EM estimation for STAR
genMCMC_star | Generalized MCMC Algorithm for STAR
getEffSize | Summarize of effective sample size
g_bc | Box-Cox transformation
g_bnp | Bayesian bootstrap-based transformation
g_cdf | Cumulative distribution function (CDF)-based transformation
g_inv_approx | Approximate inverse transformation
g_inv_bc | Inverse Box-Cox transformation
init_lm_gprior | Initialize linear regression parameters assuming a g-prior
lm_star | Fitting frequentist STAR linear model via EM algorithm
plot_coef | Plot the estimated regression coefficients and credible intervals
plot_fitted | Plot the fitted values and the data
plot_pmf | Plot the empirical and model-based probability mass functions
predict.lmstar | Predict method for response in STAR linear model
pvals | Compute coefficient p-values for STAR linear regression using likelihood ratio test
randomForest_star | Fit Random Forest STAR with EM algorithm
roaches | Data on the efficacy of a pest management system at reducing the number of roaches in urban apartments.
round_floor | Rounding function
sample_lm_gprior | Sample the linear regression parameters assuming a g-prior
simBaS | Compute Simultaneous Band Scores (SimBaS)
simulate_nb_friedman | Simulate count data from Friedman's nonlinear regression
simulate_nb_lm | Simulate count data from a linear regression
spline_star | Estimation for Bayesian STAR spline regression
warpDLM | Posterior Inference for warpDLM model with latent structural DLM