Package: TriDimRegression 1.0.3

TriDimRegression: Bayesian Statistics for 2D/3D Transformations

Fits 2D and 3D geometric transformations via 'Stan' probabilistic programming engine ( Stan Development Team (2021) <https://mc-stan.org>). Returns posterior distribution for individual parameters of the fitted distribution. Allows for computation of LOO and WAIC information criteria (Vehtari A, Gelman A, Gabry J (2017) <doi:10.1007/s11222-016-9696-4>) as well as Bayesian R-squared (Gelman A, Goodrich B, Gabry J, and Vehtari A (2018) <doi:10.1080/00031305.2018.1549100>).

Authors:Alexander Pastukhov [aut, cre], Claus-Christian Carbon [aut]

TriDimRegression_1.0.3.tar.gz
TriDimRegression_1.0.3.zip(r-4.7-x86_64)TriDimRegression_1.0.3.zip(r-4.6-x86_64)TriDimRegression_1.0.3.zip(r-4.5-x86_64)
TriDimRegression_1.0.3.tgz(r-4.6-x86_64)TriDimRegression_1.0.3.tgz(r-4.6-arm64)TriDimRegression_1.0.3.tgz(r-4.5-x86_64)TriDimRegression_1.0.3.tgz(r-4.5-arm64)
TriDimRegression_1.0.3.tar.gz(r-4.7-arm64)TriDimRegression_1.0.3.tar.gz(r-4.7-x86_64)TriDimRegression_1.0.3.tar.gz(r-4.6-arm64)TriDimRegression_1.0.3.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
TriDimRegression/json (API)

# Install 'TriDimRegression' in R:
install.packages('TriDimRegression', repos = c('https://alexander-pastukhov.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/alexander-pastukhov/tridim-regression/issues

Pkgdown/docs site:https://alexander-pastukhov.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

bidimensional-regressiontridimenisional-regressioncpp

4.22 score 11 scripts 379 downloads 21 exports 65 dependencies

Last updated from:5d2d9655df. Checks:12 OK, 1 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK309
linux-devel-x86_64OK349
source / vignettesOK343
linux-release-arm64OK321
linux-release-x86_64OK295
macos-release-arm64OK160
macos-release-x86_64OK639
macos-oldrel-arm64OK180
macos-oldrel-x86_64OK398
windows-develOK323
windows-releaseOK375
windows-oldrelOK342
wasm-releaseFAIL206

Exports:check_exponential_priorcheck_normal_priorcheck_variablescoef_summaryfit_transformationfit_transformation_dfget_beta_nis.tridim_transformationm2_affinem2_euclideanm2_projectivem2_translationm3_affinem3_euclidean_xm3_euclidean_ym3_euclidean_zm3_projectivem3_translationR2transformation_matrixvariable_summary

Dependencies:abindbackportsbayesplotBHcallrcheckmateclicodetoolscpp11descdigestdistributionaldplyrfarverFormulafuturegenericsggplot2ggridgesglobalsgluegridExtragtableinlineisobandlabelinglifecyclelistenvloomagrittrmatrixStatsnumDerivotelparallellypillarpkgbuildpkgconfigplyrposteriorprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsS7scalesStanHeadersstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr

Eye gaze mapping
Plotting raw data | Using lm2 to transform the eye gaze

Last update: 2022-01-14
Started: 2020-10-12

Comparing faces

Last update: 2022-01-14
Started: 2021-03-19

Transformation matrices
Bidimensional regression | Translation | Euclidean | Affine | Projective | Tridimensional regression | Euclidean, rotation about x axis | Euclidean, rotation about y axis | Euclidean, rotation about z axis

Last update: 2021-10-05
Started: 2021-03-19

Readme and manuals

Help Manual

Help pageTopics
The 'TriDimRegression' package.TriDimRegression-package TriDimRegression
Carbon, C. C. (2013), data set #1CarbonExample1Data
Carbon, C. C. (2013), data set #2CarbonExample2Data
Carbon, C. C. (2013), data set #3CarbonExample3Data
Posterior distributions for transformation coefficients in full or summarized form.coef.tridim_transformation
Eye gaze calibration dataEyegazeData
Face landmarks, male, #010Face3D_M010
Face landmarks, male, #101Face3D_M101
Face landmarks, male, #244Face3D_M244
Face landmarks, male, #092Face3D_M92
Face landmarks, female, #070Face3D_W070
Face landmarks, female, #097Face3D_W097
Face landmarks, female, #182Face3D_W182
Face landmarks, female, #243Face3D_W243
Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Formula.fit_transformation fit_transformation.formula
Fitting Bidimensional or Tridimensional Regression / Geometric Transformation Models via Two Tables.fit_transformation_df
Friedman & Kohler (2003), data set #1FriedmanKohlerData1
Friedman & Kohler (2003), data set #2FriedmanKohlerData2
Checks if argument is a 'tridim_transformation' objectis.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.loo.tridim_transformation
Nakaya (1997)NakayaData
Posterior interval plots for key parameters. Uses bayesplot::mcmc_intervals.plot.tridim_transformation
Computes posterior samples for the posterior predictive distribution.predict.tridim_transformation
Prints out tridim_transformation objectprint.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.R2 R2.tridim_transformation
Summary for a tridim_transformation objectsummary.tridim_transformation
Class 'tridim_transformation'.tridim_transformation tridim_transformation-class
Computes widely applicable information criterion (WAIC).waic.tridim_transformation