API reference¶

Every help topic in the package, generated from its help pages. In R, the same text is available with ?name.

Tests of the variance¶

Each test returns an htest object, whose fields test_results documents. The tests are grouped by the family they belong to.

What every test returns¶

Topic	Functions
What every test returns

Auxiliary-regression tests¶

Topic	Functions
Perform White's test for heteroscedasticity	`performWhiteTest()`
Robust White test with bootstrap and effect sizes	`performWhiteTestRobust()`
Memory-efficient White test for large datasets	`performWhiteTestStreaming()`
Perform Breusch-Pagan test for heteroscedasticity	`performBPTest()`, `performBreuschPaganTest()`
Robust Breusch–Pagan test with bootstrap and effect sizes	`performBPTestRobust()`
Streaming Breusch\–Pagan test for large datasets	`performBPTestStreaming()`
Perform Koenker studentized Breusch-Pagan test	`performKoenkerTest()`
Streaming Koenker studentized Breusch\–Pagan test	`performKoenkerTestStreaming()`
Studentized Breusch–Pagan test (deprecated)	`performStudentizedBPTest()`
Perform Cook-Weisberg test for heteroscedasticity	`performCookWeisbergTest()`
Perform the non-constant variance (NCV) score test (deprecated)	`performNCVTest()`
Perform Harvey test for multiplicative heteroscedasticity	`performHarveyTest()`
Perform Glejser test for heteroscedasticity	`performGlejserTest()`
Perform Park test for heteroscedasticity	`performParkTest()`
Test the functional form of the error variance	`performVarianceFormTest()`
Perform Davidian-Carroll test	`performDavidianCarrollTest()`
Perform Spearman rank correlation test	`performSpearmanTest()`
Perform Spread-Level test	`performSpreadLevelTest()`
High-dimensional projection heteroscedasticity test	`performHighDimensionalTest()`

Tests on ordered observations¶

Topic	Functions
Perform Goldfeld-Quandt test for heteroscedasticity	`performGQTest()`
Szroeter test for ordered alternatives	`performSzroeterTest()`
Harrison-McCabe test for heteroscedasticity	`performHarrisonMcCabeTest()`

Group-wise tests¶

Topic	Functions
Perform Levene's test for equality of variances	`performLeveneTest()`
Perform Brown-Forsythe test for equality of variances	`performBrownForsytheTest()`
Perform Fligner-Killeen test for homogeneity of variances	`performFlignerKilleenTest()`
Perform Bartlett's test for equality of variances	`performBartlettTest()`
Perform Hartley's Fmax test	`performHartleyFmaxTest()`
Perform O'Brien test for equality of variances	`performOBrienTest()`
Box's M Test for Equality of Covariance Matrices	`performBoxMTest()`

Time series, panel and spatial tests¶

Topic	Functions
Perform Engle's ARCH LM test	`performArchLMTest()`
Perform McLeod-Li test	`performMcLeodLiTest()`
Breusch-Pagan LM test for random effects	`performBPRandomEffectsTest()`
Perform Pesaran CD test	`performPesaranTest()`
Spatial heteroscedasticity test	`performSpatialHeteroTest()`

Resampling and robust tests¶

Topic	Functions
Bootstrap White test	`performWhiteTestBootstrap()`
Wild bootstrap heteroscedasticity test	`performWildBootstrapTest()`
Rank-based permutation heteroscedasticity test	`performRankPermutationTest()`
Quantile regression heteroscedasticity test	`performQuantileRegressionTest()`
Bootstrap a test statistic	`bootstrapTestStatistic()`, `rbootstrap_test_statistic()`
Bootstrap utilities for heteroscedasticity diagnostics
Robust heteroscedasticity diagnostics
Modern heteroscedasticity diagnostics	`modern_diagnostics`
Additional heteroscedasticity tests

Model checks¶

Checks of the fitted model that are not tests of the variance: the functional form, collinearity, influence and the fit itself. They are listed apart so that they are not mistaken for heteroscedasticity tests.

Topic	Functions
Ramsey's RESET test for nonlinearity	`performRESETTest()`
Variance inflation factors	`performVIFDiagnostic()`
Identify influential observations	`performInfluenceDiagnostics()`
Scatter-plot diagnostics for heteroscedasticity	`performScatterDiagnostic()`
Validate model argument	`checkModel()`
Enhanced model diagnostics (deprecated)	`checkModelEnhanced()`

Running several diagnostics¶

Topic	Functions
Run multiple heteroscedasticity diagnostics	`runHeteroTests()`
Parallel test execution helper	`runHeteroTestsParallel()`
Run a suite of model diagnostics	`runDiagnostics()`
Run registered diagnostic plots	`runDiagnosticPlots()`
Run multivariate heteroscedasticity tests	`runMultivariateTests()`
Run time-series heteroscedasticity tests	`runTimeSeriesTests()`
Run panel-data heteroscedasticity tests	`runPanelTests()`
Run heteroscedasticity diagnostics on the data of a survey design	`runSurveyHeteroTests()`
Run enhanced heteroscedasticity diagnostics	`runAdvancedDiagnostics()`, `rrunAdvancedDiagnostics()`
Compare diagnostics across models	`compareModelDiagnostics()`
Summarise heteroscedasticity test results	`compareTestResults()`
Machine-learning residual analysis	`analyzeMLResiduals()`
Register a custom diagnostic	`registerDiagnostic()`
Register a diagnostic plot	`registerPlot()`
List the registered diagnostics	`listDiagnostics()`
Create a diagnostic object	`HeteroDiagnostic()`
Run diagnostic tests	`test()`
Summarize diagnostics	`summary(<HeteroDiagnostic>)`
Plot diagnostics	`plot(<HeteroDiagnostic>)`
S3 containers for heteroscedasticity diagnostics
broom methods for heteroscedasticity diagnostics	`tidy(<hetero_test>)`, `glance(<hetero_test>)`, `augment(<hetero_test>)`, `tidy(<hetero_test_suite>)`, `glance(<hetero_test_suite>)`, `augment(<hetero_test_suite>)`, `tidy(<hetero_grouped_suite>)`
Cache results of a heteroscedasticity test	`cachedTest()`
Clear cached test results	`clearTestCache()`
Clear cached diagnostic run results	`clearAnalysisCache()`
Heteroscedasticity Test Factory
Shared instance of the test factory

Remediation¶

Topic	Functions
Weighted Least Squares wrapper	`fitWLS()`
Robust regression wrapper	`fitRobust()`
Automatic transformation helper	`autoTransform()`
Suggest remediation actions for heteroscedasticity	`suggestRemediation()`, `print(<remediation_suggestions>)`
Automatically compare remedial models	`autoCompareRemediations()`

Recommendations¶

Topic	Functions
Intelligent recommendations for heteroscedasticity diagnostics	`analyseDatasetCharacteristics()`, `suggestDiagnosticsForProfile()`, `interpretHeteroTestResults()`, `recommendRemediationStrategies()`, `warnDiagnosticAssumptions()`, `buildDiagnosticDecisionTree()`, `composeDiagnosticNarrative()`, `generateHeteroRecommendations()`

Simulation¶

The simulation functions are named in snake_case on purpose; the rest of the package uses camelCase.

Topic	Functions
Simulate heteroscedastic data	`simulate_hetero()`, `simulate_arch1()`, `sigma_linear()`, `sigma_exponential()`, `sigma_group()`, `sigma_piecewise()`, `sigma_poly()`, `sigma_sin()`, `sigma_multiplicative()`, `sigma_spatial()`, `sigma_logistic()`, `sigma_inverse()`, `sigma_power()`, `sigma_step()`, `sigma_u_shape()`, `sigma_exp_decay()`, `sigma_gaussian_peak()`, `sigma_piecewise_linear()`
Advanced simulation framework for test validation	`simulate_size()`, `simulate_power()`, `simulate_type_I_errors()`, `simulate_power_analysis()`
Plot method for power analysis results	`plot(<power_analysis>)`

Plotting¶

Topic	Functions
Plot residuals vs fitted values	`plotResidualsFitted()`
Enhanced residuals vs fitted plot (deprecated)	`plotResidualsFittedEnhanced()`
Spread-Level plot for variance diagnostics	`plotSpreadLevel()`
Density plot of residuals	`plotResidualDensity()`
QQ plot of residuals	`plotResidualQQ()`
Bubble plot of residual variance by covariate	`plotBubbleVariance()`
Generate a suite of diagnostic plots	`plotDiagnosticSuite()`
Enhanced diagnostic plot suite (deprecated)	`plotDiagnosticSuiteEnhanced()`
Compare residuals before and after remediation	`plotBeforeAfter()`
Autoplot heteroscedasticity diagnostics	`autoplot(<hetero_test_suite>)`, `autoplot(<hetero_grouped_suite>)`
Heteroscedasticity diagnostic theme	`theme_hetero()`
Colour scale for heteroscedasticity diagnostics	`scale_colour_hetero_diagnostic()`, `scale_fill_hetero_diagnostic()`

Reporting and data¶

Topic	Functions
Automated diagnostic report generation	`generateDiagnosticReport()`
Download example teaching dataset	`downloadTeachingData()`
Simulated dataset with collinearity and mild nonlinearity	`diagnostic_data`
Simulated dataset with heteroscedastic errors	`hetero_data`
Print method for enhanced White test results	`print(<htest_enhanced>)`

Dashboard and benchmarks¶

To move to a companion package, once it has a name.

Topic	Functions
Interactive diagnostic dashboard	`launchDiagnosticDashboard()`
Benchmark heteroTests against reference implementations	`run_benchmark_suite()`, `generate_benchmark_report()`

Package infrastructure¶

Topic	Functions
Control logging for heteroTests diagnostics	`ht_set_log_level()`, `ht_enable_log_capture()`, `ht_log_history()`, `ht_clear_log_history()`
Generate standardized error message (deprecated)	`std_error()`
Generate standardized warning message (deprecated)	`std_warning()`
Validate model and data inputs before running diagnostics (deprecated)	`validateTestInputs()`
Validate data argument (deprecated)	`checkData()`
Effect size calculations for heteroscedasticity diagnostics	`rcalculateEffectSize()`
Estimate test power from observed effect size	`restimate_test_power()`
Validate diagnostics against reference implementations	`rvalidate_against_reference()`

Internal¶

Topic	Functions
Diagnostics registry	`.diagnostic_registry`
Bibliography of Diagnostic Algorithms
Bootstrap implementation for White test	`bootstrap_white_test()`
Validate numeric vector	`checkNumericVector()`
Check memory usage and warn for large datasets	`check_memory_usage()`
Reset cached validation results	`clearValidationCache()`
Ecosystem integration helpers	`.ht_null`
Log a formatted message	`ht_log()`
Interpret White test results	`interpret_white_test()`
Standardized error and warning messages
Test-specific sample size requirements	`rTEST_REQUIREMENTS`
Handle missing values according to a specified strategy	`rhandleMissingValues()`
Process validation helper outputs	`rprocessValidationResult()`
Validate data inputs for heteroscedasticity tests	`rvalidateDataInputs()`
Validate distributional assumptions	`rvalidateDistributionalAssumptions()`
Validate grouping variable structure	`rvalidateGroupingVariable()`
Validate regression model inputs	`rvalidateModelInputs()`
Validate test-specific sample size requirements	`rvalidateSampleSize()`
Validate test-specific requirements	`rvalidateTestRequirements()`
Safely fit a linear model	`safe_lm()`
Safe variance calculation	`safe_var()`