Built-in methods for publication-quality tables using 'texreg', post-estimation
diagnostics using 'DHARMa', and testing for marginal zero-modification
via <doi:10.1177/0962280217749991>. For information on copula regression for count data,

Title: An API Wrapper for the Bureau of Justice Statistics (BJS)
Authors@R: person("Dylan", "McDowell", email = "dylan.mcdowell226@gmail.com", role = c("aut", "cre"))
Description: Drawing heavy influence from 'blscrapeR', this package scrapes crime data from <https://www.bjs.gov/>. Specifically, it scrapes data from the National Crime Victimization Survey which tracks personal and household crime in the USA. The idea is to utilize the 'tidyverse' methodology to create an efficient work flow when dealing with crime statistics.

Title: Bayesian Kernel Machine Regression
Description: Implementation of a statistical approach
for estimating the joint health effects of multiple
Imports: dplyr, magrittr, nlme, fields, truncnorm, tidyr, MASS
Packaged: 2016-06-30 19:32:17 UTC; jenniferbobb
Maintainer: Jennifer F. Bobb <jenniferfederbobb@gmail.com>

Imports: dplyr, magrittr, nlme, fields, truncnorm, tidyr, MASS,
Packaged: 2017-03-24 17:22:08 UTC; jenniferbobb

Packaged: 2022-03-04 01:16:50 UTC; jenniferbobb

concurrent exposures, as described in Bobb et al (2015)
Packaged: 2022-03-25 03:14:48 UTC; jenniferbobb

Title: Parallel Chain Tools for Bayesian Kernel Machine Regression
Description: Bayesian kernel machine regression (from the 'bkmr'
package) is a Bayesian semi-parametric generalized linear model
approach under identity and probit links. There are a number of
functions in this package that extend Bayesian kernel machine
regression fits to allow multiple-chain inference and diagnostics,
which leverage functions from the 'future', 'rstan', and 'coda'
packages.  Reference: Bobb, J. F., Henn, B. C., Valeri, L., & Coull,
B. A. (2018). Statistical software for analyzing the health effects of
multiple concurrent exposures via Bayesian kernel machine regression.

Description: Bayesian kernel machine regression (from the 'bkmr' package)
is a Bayesian semi-parametric generalized linear model approach under
identity and probit links. There are a number of functions in this
package that extend Bayesian kernel machine regression fits to allow
multiple-chain inference and diagnostics, which leverage functions
from the 'future', 'rstan', and 'coda' packages.  Reference: Bobb, J.
F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical
software for analyzing the health effects of multiple concurrent
exposures via Bayesian kernel machine regression. ;
Imports: bkmr, data.table, future, rstan