Download, Analyze & Clean New Jersey Car Crash Data
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.

lubridate, httr, curl, readr, dplyr (>= 1.0.0), stringr,
tidyselect

lubridate, httr, curl, readr, dplyr (>= 1.0.0), stringr,
stringi, tidyselect
njtr1, new-jersey,road-safety, car-crashes,
car-accidents, data

Regression in the Normal Linear ADG Model

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".

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

Wald Test of Nonlinear Restrictions and Nonlinear CI
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
available.

Interface to the National Lakes Assessment
person("Joseph", "Stachelek", email = "stachel2@msu.edu", role = c("aut", "cre"))
Client for programmatic access to the 2007 and 2012 National
Lakes Assessment database <https://www.epa.gov/national-aquatic-resource-surveys/nla>
containing data for hundreds of lakes in the lower 48 states of the contiguous US.
knitr, rmarkdown, dplyr, magrittr, skimr, sf, maps, R.rsp

Nested loop cross validation for classification purposes for misclassification error rate estimation.
The package supports several methodologies for feature selection: random forest, Student t-test, limma,
and provides an interface to the following classification methods in the 'MLInterfaces' package: linear,
quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized
linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of
the classifier are included: plot of the ranks of the features, scores plot for a specific
classification algorithm and number of features, misclassification rate
for the different number of features and classification algorithms tested and ROC plot.
For further details about the methodology, please check:
Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004)
<doi:10.2202/1544-6115.1078>.
R (>= 2.10), a4Core, MLInterfaces (>= 1.22.0), xtable
limma, MASS, methods, graphics, Biobase, multtest,
RColorBrewer, pamr, randomForest, ROCR, ipred, e1071, kernlab