lm_plot.4way            Create a Four-Panel Regression Assumptions Plot
lm_plot.ac              Plot Residuals vs. Observation Order
                        (Autocorrelation Check)
lm_plot.df              Augment Model Data for Diagnostic Plots
lm_plot.fit             Plot Observed vs. Fitted Values to Check
                        Linearity
lm_plot.infl            Plot Leverage vs. Fitted Values to Visualize
                        Inflential Observations
lm_plot.lev             Plot Standard Residuals vs. Leverage with
                        Cook's Distance Contours
lm_plot.parms           Set or Retrieve Default Plot Parameters for
                        Model Diagnostic Plots
lm_plot.qq              Q-Q Plot of Residuals to Assess Normality
lm_plot.var             Plot Residuals vs. Fitted Values to Assess
                        Homoskedasticity
outlier                 Identify Outliers Using Boxplot Heuristic
print.sumry.lm          Print a 'sumry' Summarization for Linear Model
                        Objects
print.sumry.regsubsets
                        Print Method for Best Subset Selection
                        ('regsubsets') Objects
print.table.sumry.lm    Print a Table from Linear Model Summary
sumry                   Summary Descriptive Statistics for BAQM
sumry.default           Summary Descriptive Statistics for List or Data
                        Frame
sumry.lm                Method 'sumry' to Summarize Linear Model ('lm')
                        Objects
sumry.regsubsets        Summary for Subset Selection ('regsubsets')
                        Objects
