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cbq  

Conditional Binary Quantile Models
View on CRAN: Click here


Download and install cbq package within the R console
Install from CRAN:
install.packages("cbq")

Install from Github:
library("remotes")
install_github("cran/cbq")

Install by package version:
library("remotes")
install_version("cbq", "0.2.0.4")



Attach the package and use:
library("cbq")
Maintained by
Xiao LU
[Scholar Profile | Author Map]
First Published: 2020-01-09
Latest Update: 2023-04-02
Description:
Estimates conditional binary quantile models developed by Lu (2020) . The estimation procedure is implemented based on Markov chain Monte Carlo methods.
How to cite:
Xiao LU (2020). cbq: Conditional Binary Quantile Models. R package version 0.2.0.4, https://cran.r-project.org/web/packages/cbq. Accessed 07 Apr. 2025.
Previous versions and publish date:
0.1.0.0 (2020-01-09 12:20), 0.2.0.1 (2020-05-27 15:10), 0.2.0.2 (2020-10-11 11:10), 0.2.0.3 (2023-04-02 17:00)
Other packages that cited cbq R package
View cbq citation profile
Other R packages that cbq depends, imports, suggests or enhances
Complete documentation for cbq
Functions, R codes and Examples using the cbq R package
Some associated functions: cbq-package . cbq . coef.cbq . dald . inverse . is.dichotomous . pald . plot.cbq . plot_coef.cbq . plot_trace.cbq . predict.cbq . print.cbq . print_coef.cbq . print_mcmc.cbq . print_text.cbq . qald . rald . 
Some associated R codes: cbq-package.R . cbq.R . plotfun.R . printfun.R . source_fun.R . stanmodels.R .  Full cbq package functions and examples
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