| MLCM-package | Maximum Likelihood Conjoint Measurement |
| anova.mlcm | Analysis of Deviance for Maximum Likelihood Conjoint Measurement Model Fits |
| as.mlcm.df | Coerce data frame to mlcm.df |
| binom.diagnostics | Diagnostics for Binary GLM |
| boot.mlcm | Resampling of an Estimated Conjoint Measurement Scale |
| BumpyGlossy | Conjoint Measurement Data for Bumpiness and Glossiness |
| fitted.mlcm | Fitted Responses for a Conjoint Measurement Scale |
| GlossyBumpy | Conjoint Measurement Data for Bumpiness and Glossiness |
| lines.mlcm | Plot an mlcm Object |
| logLik.mlcm | Extract Log-Likelihood from mlcm Object |
| make.wide | Create data frame for Fitting Conjoint Measurment Models by glm |
| make.wide.full | Create data frame for Fitting Conjoint Measurment Models by glm |
| MLCM | Maximum Likelihood Conjoint Measurement |
| mlcm | Fit Conjoint Measurement Models by Maximum Likelihood |
| mlcm.default | Fit Conjoint Measurement Models by Maximum Likelihood |
| mlcm.formula | Fit Conjoint Measurement Models by Maximum Likelihood |
| plot.mlcm | Plot an mlcm Object |
| plot.mlcm.df | Create Conjoint Proportion Plot from mlcm.df Object |
| plot.mlcm.diag | Diagnostics for Binary GLM |
| points.mlcm | Plot an mlcm Object |
| predict.mlcm | Predict Method for MLCM Objects |
| print.mlcm | Fit Conjoint Measurement Models by Maximum Likelihood |
| print.summary.mlcm | Summary Method for mlcm objects |
| summary.mlcm | Summary Method for mlcm objects |
| Texture | Three-way Conjoint Measurement Data for Texture Regularity. |