ols         Ordinary least squares linear model
lrm         Binary and ordinal logistic regression model
psm         Accelerated failure time parametric survival model
cph         Cox proportional hazards regression
bj          Buckley-James censored least squares linear model
specs       Detailed specifications of fit
robcov      Robust covariance matrix estimates
bootcov     Bootstrap covariance matrix estimates
summary     Summary of effects of predictors
plot.summary    
            Plot continuously shaded confidence
             bars for results of summary 
anova       Wald tests of most meaningful hypotheses
contrast    General contrasts, C.L., tests           
plot.anova  Depict results of anova graphically
plot        Plot effects of predictors
gendata     Generate data frame with predictor
             combinations (optionally interactively)
predict     Obtain predicted values or design matrix
fastbw      Fast backward step-down variable
             selection
residuals   Residuals, influence statistics from fit
which.influence 
            Which observations are overly influential
sensuc      Sensitivity of one binary predictor in
             lrm and cph models to an unmeasured
             binary confounder
latex       LaTeX representation of fitted
             model or anova() table
Function    S function analytic representation
             of a fitted regression model (X*Beta)
Hazard      S function analytic representation
             of a fitted hazard function (for psm)
Survival    S function analytic representation of
             fitted survival function (for psm,cph)
Quantile    S function analytic representation of
             fitted function for quantiles of
             survival time (for psm, cph)
nomogram    Draws a nomogram for the fitted model
survest     Estimate survival probabilities (for psm, cph)
survplot    Plot survival curves (psm, cph)
validate    Validate indexes of model fit using resampling
calibrate   Estimate calibration curve for model using resampling
vif         Variance inflation factors for a fit
naresid     Bring elements corresponding to missing 
             data back into predictions and residuals
naprint     Print summary of missing values
pentrace    Find optimum penality for penalized MLE
effective.df    
            Print effective d.f. for each type of 
             variable in model, for penalized fit or pentrace result
rm.impute   Impute repeated measures data with
             non-random dropout (experimental function, not working
             correctly)
