Description: These are miscellaneous functions for working with panel data, quantiles, and printing results.  For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance.  For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions.  Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas.

Authors@R: person("Brantly", "Callaway", email = "bmcallaw@olemiss.edu", role = c("aut", "cre"))

Authors@R: person("Brantly", "Callaway", email = "brantly.callaway@uga.edu", role = c("aut", "cre"))

Suggests: testthat (>= 3.0.0), plm, tibble

Title: Data Generator based on Bayesian Network Model
Description: Data generator based on Bayesian network model
Packaged: 2014-12-28 06:33:28 UTC; JaeseongYoo

Title: Bayesian Neural Network for High-Dimensional Nonlinear Variable
Authors@R: c(person("Bochao", "Jia", role = c("aut", "cre", "cph"), email = "jbc409@ufl.edu"),
person("Faming", "Liang", role = c("ctb"), email = "fmliang@purdue.edu"),
person("Robert", "Gentleman", role = c("cph")), person("Ross", "Ihaka", role = c("cph")), person("The R Core Team", role = c("cph")))
Description: Perform Bayesian variable selection for high-dimensional nonlinear systems and also can be used to test nonlinearity for a general regression problem. The computation can be accelerated using multiple CPUs. You can refer to Liang, F., Li, Q. and Zhou, L. (2017) at <https://www.samsi.info/wp-content/uploads/2016/09/SAMSI_DPDA-Liang.pdf> for detail. The publication "Bayesian Neural Networks for Selection of drug sensitive Genes" will be appear on Journals of American Statistical Association soon.