R: Maximum Likelihood Conjoint Measurement

# Maximum Likelihood Conjoint Measurement

## Documentation for package ‘MLCM’ version 0.4.4

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

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.