Flexible Modeling of Count Data

Documentation for package ‘countSTAR’ version 1.0.2

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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