Flexible Modeling of Count Data
Inverse rounding function
Fit Bayesian Additive STAR Model with MCMC
MCMC Algorithm for BART-STAR
MCMC sampler for BART-STAR with a monotone spline model for the transf...
STAR Bayesian Linear Regression
Gibbs sampler for STAR linear regression with BNP transformation
Monte Carlo sampler for STAR linear regression with a g-prior
Brent's method for optimization
Estimate the remaining time in the MCMC based on previous samples
Compute asymptotic confidence intervals for STAR linear regression
Compute Simultaneous Credible Bands
Compute the ergodic (running) mean.
Estimate the mean for a STAR process
Estimate the mean for a STAR process
Estimate the mean for a STAR process
Estimate the mean for a STAR process
Compute E(Y^2) for a STAR process
Box-Cox transformation
Bayesian bootstrap-based transformation
Cumulative distribution function (CDF)-based transformation
Approximate inverse transformation
Inverse Box-Cox transformation
Fitting STAR Gradient Boosting Machines via EM algorithm
Generalized EM estimation for STAR
Generalized MCMC Algorithm for STAR
MCMC sampler for STAR with a monotone spline model for the transformat...
Summarize of effective sample size
Initialize the parameters for an additive model
Initialize the parameters for an additive model
Initialize linear regression parameters assuming a g-prior
Initialize linear regression parameters assuming a horseshoe prior
Initialize linear regression parameters assuming a ridge prior
Initialize the parameters for a simple mean-only model
Estimate confidence intervals/bands for a STAR process
Compute the inverse log-odds
Fitting frequentist STAR linear model via EM algorithm
Compute the log-odds
Compute the pointwise log-likelihood for STAR
Compute the log-likelihood for STAR
Plot the estimated regression coefficients and credible intervals
Plot the fitted values and the data
Plot the empirical and model-based probability mass functions
pmax() in Rcpp
pmin() in Rcpp
Predict method for response in STAR linear model
Compute coefficient p-values for STAR linear regression using likeliho...
Fit Random Forest STAR with EM algorithm
Rounding function
Sample from a truncated normal distribution
Sample the parameters for an additive model
Sample the parameters for an additive model
Sample the linear regression parameters assuming a g-prior
Sample linear regression parameters assuming horseshoe prior
Sample linear regression parameters assuming a ridge prior
Sample the parameters for a simple mean-only model
Sample a Gaussian vector using the fast sampler of BHATTACHARYA et al.
Compute Simultaneous Band Scores (SimBaS)
Simulate count data from Friedman's nonlinear regression
Simulate count data from a linear regression
Estimation for Bayesian STAR spline regression
Monte Carlo predictive sampler for spline regression
Initialize and reparametrize a spline basis matrix
Compute the first and second moment of a truncated normal
Univariate Slice Sampler from Neal (2008)
Update parameters for warpDLM model with trend DLM
Posterior Inference for warpDLM model with latent structural DLM
For Bayesian and classical inference and prediction with count-valued data, Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable, and easy-to-use approach. STAR models the observed count data using a rounded continuous data model and incorporates a transformation for greater flexibility. Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of (i) transforming count-valued data and subsequently (ii) modeling the transformed data using Gaussian models. STAR is well-defined for count-valued data, which is reflected in predictive accuracy, and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. Importantly, STAR is easy to combine with existing MCMC or point estimation methods for continuous data, which allows seamless adaptation of continuous data models (such as linear regressions, additive models, BART, random forests, and gradient boosting machines) for count-valued data. The package also includes several methods for modeling count time series data, namely via warped Dynamic Linear Models. For more details and background on these methodologies, see the works of Kowal and Canale (2020) <doi:10.1214/20-EJS1707>, Kowal and Wu (2022) <doi:10.1111/biom.13617>, King and Kowal (2022) <arXiv:2110.14790>, and Kowal and Wu (2023) <arXiv:2110.12316>.