Package: Brq 3.0

Brq: Bayesian Analysis of Quantile Regression Models

Bayesian estimation and variable selection for quantile regression models.

Authors:Rahim Alhamzawi

# Install 'Brq' in R:

install.packages('Brq', repos = c('https://rahim69.r-universe.dev', 'https://cloud.r-project.org'))

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

Last updated 4 years agofrom:c22bfef3f3. Checks:OK: 7. Indexed: yes.

Dependencies:

Help page	Topics
Bayesian adaptive Lasso quantile regression	BALqr
Bayesian adaptive Lasso tobit quantile regression	BALtqr
Bayesian Binary Quantile Regression	BBqr
Bayesian Lasso Binary quantile regression	BLBqr
Bayesian Lasso quantile regression	BLqr
Bayesian tobit quantile regression	BLtqr
Exploring Relationships in Body Dimensions	Body
Bayesian Quantile Regression	Bqr Brq Brq.default Brq.formula
Bayesian tobit quantile regression	Btqr
Estimate Deviance Information Criterion (DIC)	DIC
Immunoglobulin G Data	ImmunogG
Model selection based on credible intervals	model
Produce the histogram, autocorrelation and trace plots for the objects of 'Brq' function	plot.Brq
Print the Bayesian quantile regression methods	print.Brq
Print the summary of Bayesian quantile regression methods.	print.summary.Brq
Prostate Cancer Data	Prostate
Salaries of 459 US statistics professor data	salary
Summarize the output of the 'Brq' function	summary.Brq
Weight and age for a sample of 4011 US girl data	USgirl
Wheat Data	wheat