Title: Download and Analyze New Jersey Car Crash Data
Description: Download and analyze crash data published by the New Jersey Department of Transportation. The data in this package is collected through the filing of NJTR-1 form by police officers, which provide a standardized way of documenting a crash that took place within New Jersey.
Depends: R (>= 3.5.0), stringr, lubridate, readr

Packaged: 2021-04-04 14:02:33 UTC; gavinrozzi

Title: Download, Analyze & Clean New Jersey Car Crash Data
Description: Download and analyze motor vehicle crash data released by the New Jersey Department of Transportation (NJDOT).
The data in this package is collected through the filing of NJTR-1 form by police officers, which provide a standardized way of documenting a motor vehicle crash that occurred in New Jersey.
3 different data tables containing data on crashes, vehicles & pedestrians released from 2001 to the present can be downloaded & cleaned using this package.
Packaged: 2021-04-20 20:58:36 UTC; gavinrozzi

Packaged: 2021-05-14 17:53:20 UTC; gavinrozzi

Imports: lubridate, httr, curl, readr, dplyr (>= 1.0.0), stringr,
Packaged: 2021-06-13 15:03:13 UTC; gavinrozzi

Packaged: 2022-04-28 21:51:13 UTC; gavinrozzi

Title: Regression in the Normal Linear ADG Model
Maintainer: Gruber, Lutz F. <nlADG@online.de>
Description: Regression in the Normal Linear ADG Model

Description: Package to select best model  among several linear and nonlinear models. The main function uses the gnls() function from the 'nlme' package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".
Imports: nlme, stats, graphics, grDevices

Title: Wald Test of nonlinear restrictions for regression parameters
Description: Nonlinear restrictions Wald Test for regression parameters using delta-method

Title: Wald Test of Nonlinear Restrictions and Nonlinear CI
Description: Wald Test for nonlinear restrictions on model parameters and confidence
intervals for nonlinear functions  of parameters using delta-method.  Applicable
after ANY model, provided parameters estimates and their covariance matrix are