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| artificial | Hawkins, Bradu, Kass's Artificial Data |
| binary | Convert decimal base number to binary base |
| cavendish | Cavendish's determinations of the mean density of the earth Data |
| coef.wle.lm | Accessing Linear Model Fits for wle.lm |
| fitted.wle.lm | Accessing Linear Model Fits for wle.lm |
| formula.wle.lm | Accessing Linear Model Fits for wle.lm |
| hald | Hald Data |
| mde.vonmises | von Mises Minimum Distance Estimates |
| mde.wrappednormal | Wrapped Normal Minimum Distance Estimates |
| mle.aic | Akaike Information Criterion |
| mle.cp | Mallows Cp |
| mle.cv | Cross Validation Selection Method |
| mle.stepwise | Stepwise, Backward and Forward selection methods |
| model.frame.wle.lm | Accessing Linear Model Fits for wle.lm |
| plot.mle.cp | Plot the Mallows Cp |
| plot.wle.cp | Plot the Weighted Mallows Cp |
| plot.wle.lm | Plots for the Linear Model |
| print.mde.vonmises | von Mises Minimum Distance Estimates |
| print.mde.wrappednormal | Wrapped Normal Minimum Distance Estimates |
| print.mle.aic | Summaries and methods for mle.aic |
| print.mle.cp | Summaries and methods for mle.cp |
| print.mle.cv | Summaries and methods for mle.cv |
| print.mle.stepwise | Accessing summaries for mle.stepwise |
| print.summary.mle.aic | Summaries and methods for mle.aic |
| print.summary.mle.cp | Summaries and methods for mle.cp |
| print.summary.mle.cv | Summaries and methods for mle.cv |
| print.summary.mle.stepwise | Accessing summaries for mle.stepwise |
| print.summary.wle.aic | Summaries and methods for wle.aic |
| print.summary.wle.aic.ar | Summaries and methods for wle.aic.ar |
| print.summary.wle.cp | Summaries and methods for wle.cp |
| print.summary.wle.cv | Summaries and methods for wle.cv |
| print.summary.wle.lm | Accessing Linear Model Fits for wle.lm |
| print.summary.wle.stepwise | Accessing summaries for wle.stepwise |
| print.wle.aic | Summaries and methods for wle.aic |
| print.wle.aic.ar | Summaries and methods for wle.aic.ar |
| print.wle.binomial | Robust Estimation in the Binomial Model |
| print.wle.cp | Summaries and methods for wle.cp |
| print.wle.cv | Summaries and methods for wle.cv |
| print.wle.gamma | Robust Estimation in the Gamma model |
| print.wle.lm | Accessing Linear Model Fits for wle.lm |
| print.wle.normal | Summaries and methods for wle.normal |
| print.wle.normal.mixture | Robust Estimation in the Normal Mixture Model |
| print.wle.normal.multi | Summaries and methods for wle.normal.multi |
| print.wle.onestep | Summaries and methods for wle.onestep |
| print.wle.poisson | Robust Estimation in the Poisson Model |
| print.wle.smooth | Bandwidth selection for the normal kernel and normal model. |
| print.wle.stepwise | Accessing summaries for wle.stepwise |
| print.wle.t.test | Weighted Likelihood Student's t-Test |
| print.wle.vonmises | von Mises Weighted Likelihood Estimates |
| print.wle.wrappednormal | Wrapped Normal Weighted Likelihood Estimates |
| rocky | Rockwell hardness, 100 coils produced in sequence at a Chicago Steel Mill Data |
| selection | Selection's Data |
| summary.mle.aic | Summaries and methods for mle.aic |
| summary.mle.cp | Summaries and methods for mle.cp |
| summary.mle.cv | Summaries and methods for mle.cv |
| summary.mle.stepwise | Accessing summaries for mle.stepwise |
| summary.wle.aic | Summaries and methods for wle.aic |
| summary.wle.aic.ar | Summaries and methods for wle.aic.ar |
| summary.wle.cp | Summaries and methods for wle.cp |
| summary.wle.cv | Summaries and methods for wle.cv |
| summary.wle.lm | Accessing Linear Model Fits for wle.lm |
| summary.wle.stepwise | Accessing summaries for wle.stepwise |
| weights.wle.lm | Accessing Linear Model Fits for wle.lm |
| wle.aic | Weighted Akaike Information Criterion |
| wle.aic.ar | Weighted Akaike Information Criterion for AR models |
| wle.ar | Fit Autoregressive Models to Time Series - Preliminary Version |
| wle.ar.wls | Weighted Akaike Information Criterion for AR models |
| wle.binomial | Robust Estimation in the Binomial Model |
| wle.cp | Weighted Mallows Cp |
| wle.cv | Model Selection by Weighted Cross-Validation |
| wle.fracdiff | Fit Fractional Models to Time Series - Preliminary Version |
| wle.gamma | Robust Estimation in the Gamma model |
| wle.lm | Fitting Linear Models using Weighted Likelihood |
| wle.normal | Robust Estimation in the Normal Model |
| wle.normal.mixture | Robust Estimation in the Normal Mixture Model |
| wle.normal.multi | Robust Estimation in the Normal Multivariate Model |
| wle.onestep | A One-Step Weighted Likelihood Estimator for Linear model |
| wle.poisson | Robust Estimation in the Poisson Model |
| wle.smooth | Bandwidth selection for the normal kernel and normal model. |
| wle.stepwise | Weighted Stepwise, Backward and Forward selection methods |
| wle.t.test | Weighted Likelihood Student's t-Test |
| wle.var.test | Weighted F Test to Compare Two Variances |
| wle.vonmises | von Mises Weighted Likelihood Estimates |
| wle.weights | Weights based on Weighted Likelihood for the normal model |
| wle.wrappednormal | Wrapped Normal Weighted Likelihood Estimates |
| x.artificial | Hawkins, Bradu, Kass's Artificial Data |
| x.hald | Hald Data |
| xdata | Selection's Data |
| y.artificial | Hawkins, Bradu, Kass's Artificial Data |
| y.hald | Hald Data |
| ydata | Selection's Data |