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. |