Package: autovi 0.4.1

autovi: Auto Visual Inference with Computer Vision Models

Provides automated visual inference of residual plots using computer vision models, facilitating diagnostic checks for classical normal linear regression models.

Authors:Weihao Li [aut, cre, cph]

autovi_0.4.1.tar.gz
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autovi.pdf |autovi.html✨
autovi/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/tengmcing/autovi/issues

On CRAN:

10 exports 1 stars 2.13 score 66 dependencies 13 scripts 855 downloads

Last updated 23 days agofrom:8db44291f7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 26 2024
R-4.5-winOKAug 26 2024
R-4.5-linuxOKAug 26 2024
R-4.4-winOKAug 26 2024
R-4.4-macOKAug 26 2024
R-4.3-winOKAug 26 2024
R-4.3-macOKAug 26 2024

Exports:auto_viAUTO_VIcheck_python_library_availableget_keras_modelkeras_wrapperKERAS_WRAPPERlist_keras_modelremove_plotresidual_checkersave_plot

Dependencies:alphahullbandicootbootcassowaryrclicolorspacecpp11crayondeldirdplyrenergyfansifarvergenericsggplot2gluegslgtableherehmsigraphinterpisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpngpolyclipprettyunitsprogressR.methodsS3R.ooR.utilsR6rappdirsRColorBrewerRcppRcppEigenRcppTOMLreticulaterlangrprojrootscalessgeostatspspatstat.dataspatstat.geomspatstat.randomspatstat.univarspatstat.utilssplancstibbletidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
AUTO_VI class environmentAUTO_VI auto_vi residual_checker
Initialization method
String representation of the object
Compute auxiliary variables for the keras modelAUTO_VI$auxiliary
Get bootstrapped residuals from a fitted modelAUTO_VI$boot_method
Predict visual signal strength for bootstrapped residual plotsAUTO_VI$boot_vss
Conduct a auto visual inference check with a computer vision modelAUTO_VI$check
List of diagnostic resultsAUTO_VI$check_result
Conduct principal component analysis for features extracted from keras modelAUTO_VI$feature_pca
Draw a summary Plot for principal component analysis conducted on extracted featuresAUTO_VI$feature_pca_plot
Get data out of a model objectAUTO_VI$get_data
Get fitted values and residuals out of a model objectAUTO_VI$get_fitted_and_resid
Compute the likelihood ratio using the simulated resultAUTO_VI$likelihood_ratio
Conduct a auto visual inference lineup check with a computer vision modelAUTO_VI$lineup_check
Get null residuals from a fitted modelAUTO_VI$null_method
Simulate null plots and predict the visual signal strengthAUTO_VI$null_vss
Compute the p-value based on the check resultAUTO_VI$p_value
Draw a lineup of standard residual plotsAUTO_VI$plot_lineup
Draw a pair of standard residual plotsAUTO_VI$plot_pair
Draw a standard residual plotAUTO_VI$plot_resid
Get rotated residuals from a fitted linear modelAUTO_VI$rotate_resid
Save plot(s)AUTO_VI$save_plot
Summary of the objectAUTO_VI$summary
Draw a summary density plot for the resultAUTO_VI$summary_density_plot
Draw a summary plot for the resultAUTO_VI$summary_plot
Draw a summary rank plot for the resultAUTO_VI$summary_rank_plot
Predict the visual signal strengthAUTO_VI$vss
Check python library availabilitycheck_python_library_available
Download and load the keras modelget_keras_model
KERAS_WRAPPER class environmentKERAS_WRAPPER keras_wrapper
Initialization method
String representation of the object
Get keras model input image heightKERAS_WRAPPER$get_input_height
Get keras model input image widthKERAS_WRAPPER$get_input_width
Load an image as numpy arrayKERAS_WRAPPER$image_to_array
List all layer namesKERAS_WRAPPER$list_layer_name
Predict visual signal strengthKERAS_WRAPPER$predict
List all available pre-trained computer vision modelslist_keras_model
Remove a plotremove_plot
Save plot(s)save_plot