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car (version 3.1-5)
Companion to Applied Regression
Description
Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, 2019.
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Install
install.packages('car')
Monthly Downloads
419,564
Version
3.1-5
License
GPL (>= 2)
Issues
0
Pull Requests
0
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0
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Repository
https://github.com/bprice2652/car_repo
Homepage
https://CRAN.R-project.org/package=car
Maintainer
Brad Price
Last Published
February 3rd, 2026
Functions in car (3.1-5)
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Tapply
Apply a Function to a Variable Within Factor Levels
boxCox
Graph the profile log-likelihood for Box-Cox transformations in 1D, or in 2D with the bcnPower family.
car-defunct
Defunct Functions in the car Package
car-deprecated
Deprecated Functions in the car Package
TransformationAxes
Axes for Transformed Variables
boxCoxVariable
Constructed Variable for Box-Cox Transformation
bcPower
Box-Cox, Box-Cox with Negatives Allowed, Yeo-Johnson and Basic Power Transformations
avPlots
Added-Variable Plots
boxTidwell
Box-Tidwell Transformations
carPalette
Set or Retrieve
car
Package Color Palette
carWeb
Access to the R Companion to Applied Regression Website
brief
Print Abbreviated Ouput
densityPlot
Nonparametric Density Estimates
ceresPlots
Ceres Plots
compareCoefs
Print estimated coefficients and their standard errors in a table for several regression models.
carHexsticker
View the Official Hex Sticker for the car Package
car-internal.Rd
Internal Objects for the
car
package
dfbetaPlots
dfbeta and dfbetas Index Plots
deltaMethod
Estimate and Standard Error of a Nonlinear Function of Estimated Regression Coefficients
crPlots
Component+Residual (Partial Residual) Plots
invTranPlot
Choose a Predictor Transformation Visually or Numerically
influence.mixed.models
Influence Diagnostics for Mixed-Effects Models
leveneTest
Levene's Test
influencePlot
Regression Influence Plot
hccm
Heteroscedasticity-Corrected Covariance Matrices
hist.boot
Methods Functions to Support
boot
Objects
durbinWatsonTest
Durbin-Watson Test for Autocorrelated Errors
leveragePlots
Regression Leverage Plots
infIndexPlot
Influence Index Plot
invResPlot
Inverse Response Plots to Transform the Response
powerTransform
Finding Univariate or Multivariate Power Transformations
mmps
Marginal Model Plotting
logit
Logit Transformation
pointLabel
Label placement for points to avoid overlaps
outlierTest
Bonferroni Outlier Test
panel.car
Panel Function for Coplots
poTest
Test for Proportional Odds in the Proportional-Odds Logistic-Regression Model
linearHypothesis
Test Linear Hypothesis
mcPlots
Draw Linear Model Marginal and Conditional Plots in Parallel or Overlaid
ncvTest
Score Test for Non-Constant Error Variance
qqPlot
Quantile-Comparison Plot
recode
Recode a Variable
regLine
Plot Regression Line
scatterplot
Enhanced Scatterplots with Marginal Boxplots, Point Marking, Smoothers, and More
scatterplotMatrix
Scatterplot Matrices
some
Sample a Few Elements of an Object
scatter3d
Three-Dimensional Scatterplots and Point Identification
showLabels
Functions to Identify and Mark Extreme Points in a 2D Plot.
residualPlots
Residual Plots for Linear and Generalized Linear Models
sigmaHat
Return the scale estimate for a regression model
spreadLevelPlot
Spread-Level Plots
vif
Variance Inflation Factors
whichNames
Position of Row Names
strings2factors
Convert Character-String Variables in a Data Frame to Factors
testTransform
Likelihood-Ratio Tests for Univariate or Multivariate Power Transformations to Normality
wcrossprod
Weighted Matrix Crossproduct
subsets
Plot Output from regsubsets Function in leaps package
symbox
Boxplots for transformations to symmetry
Boot
Bootstrapping for regression models
Predict
Model Predictions
Export
Export a data frame to disk in one of many formats
Import
Import data from many file formats
Boxplot
Boxplots With Point Identification
ScatterplotSmoothers
Smoothers to Draw Lines on Scatterplots
Contrasts
Functions to Construct Contrasts
S
Modified Functions for Summarizing Linear, Generalized Linear, and Some Other Models
Anova
Anova Tables for Various Statistical Models
Ellipses
Ellipses, Data Ellipses, and Confidence Ellipses