| TriDimRegression-package | The 'TriDimRegression' package. |
| CarbonExample1Data | Carbon, C. C. (2013), data set #1 |
| CarbonExample2Data | Carbon, C. C. (2013), data set #2 |
| CarbonExample3Data | Carbon, C. C. (2013), data set #3 |
| coef.tridim_transformation | Posterior distributions for transformation coefficients in full or summarized form. |
| EyegazeData | Eye gaze calibration data |
| Face3D_M010 | Face landmarks, male, #010 |
| Face3D_M101 | Face landmarks, male, #101 |
| Face3D_M244 | Face landmarks, male, #244 |
| Face3D_M92 | Face landmarks, male, #092 |
| Face3D_W070 | Face landmarks, female, #070 |
| Face3D_W097 | Face landmarks, female, #097 |
| Face3D_W182 | Face landmarks, female, #182 |
| Face3D_W243 | Face landmarks, female, #243 |
| fit_transformation | Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Formula. |
| fit_transformation.formula | Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Formula. |
| fit_transformation_df | Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Two Tables. |
| FriedmanKohlerData1 | Friedman & Kohler (2003), data set #1 |
| FriedmanKohlerData2 | Friedman & Kohler (2003), data set #2 |
| is.tridim_transformation | Checks if argument is a 'tridim_transformation' object |
| loo.tridim_transformation | Computes an efficient approximate leave-one-out cross-validation via loo library. It can be used for a model comparison via loo::loo_compare() function. |
| NakayaData | Nakaya (1997) |
| plot.tridim_transformation | Posterior interval plots for key parameters. Uses bayesplot::mcmc_intervals. |
| predict.tridim_transformation | Computes posterior samples for the posterior predictive distribution. |
| print.tridim_transformation | Prints out tridim_transformation object |
| R2 | Computes R-squared using Bayesian R-squared approach. For detail refer to: Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari (2018). R-squared for Bayesian regression models. The American Statistician, doi:10.1080/00031305.2018.1549100. |
| R2.tridim_transformation | Computes R-squared using Bayesian R-squared approach. For detail refer to: Andrew Gelman, Ben Goodrich, Jonah Gabry, and Aki Vehtari (2018). R-squared for Bayesian regression models. The American Statistician, doi:10.1080/00031305.2018.1549100. |
| summary.tridim_transformation | Summary for a tridim_transformation object |
| TriDimRegression | The 'TriDimRegression' package. |
| tridim_transformation | Class 'tridim_transformation'. |
| tridim_transformation-class | Class 'tridim_transformation'. |
| waic.tridim_transformation | Computes widely applicable information criterion (WAIC). |
| _PACKAGE | The 'TriDimRegression' package. |