CITATION.cff and inst/CITATION.inst/extdata/gazepoint_realistic_demo_exports/.data-raw/create_gazepoint_realistic_demo_exports.R.Added run_gazepoint_workflow() for one-command Gazepoint folder analysis.
Added folder-level import with read_gazepoint_folder().
Added check_gazepoint_file_pairs() for checking whether Gazepoint all-gaze and fixation export files are correctly paired.
Added flag_tracking_quality() for identifying recordings requiring review.
Added diagnostic plotting functions:
plot_tracking_quality()plot_sampling_rate()Added save_gazepoint_plots() for automatic diagnostic plot export.
Added create_gazepoint_report() for lightweight HTML diagnostic reports.
Integrated optional HTML report creation into run_gazepoint_workflow() with create_report = TRUE.
Added standard CSV export helpers:
export_gazepoint_tables()write_gazepoint_outputs()Added summarise_gazepoint_workflow() for creating a compact one-row summary of a completed workflow result object.
as_gazepoint_master() for converting Gazepoint all-gaze exports into a standard sample-level master table with time, gaze coordinates, pupil values, validity flags, missingness flags, off-screen gaze flags, AOI state, event labels, and fixation-related fields.create_gazepoint_master() as the advanced Gazepoint master-table constructor for creating analysis-ready sample-level data with participant, media, timing, gaze, pupil, AOI, screen, event, response, and metadata fields.audit_gazepoint_master() for producing compact quality-audit tables from Gazepoint master tables, including overview, subject-level, media-level, AOI-state, pupil, and coordinate summaries.validate_gazepoint_master() as a formal validation gate before pupil preprocessing, AOI modelling, or advanced statistical analysis.export_gazepoint_master_audit() for exporting the master table, audit tables, and validation tables to CSV files.summarise_gazepoint_pupil() as the first pupil-preprocessing gate for Gazepoint master tables. The function summarises pupil availability, missing-pupil percentages, valid-pupil percentages, pupil distributions, plausible-value checks, and IQR-based outlier counts by subject, media, subject-by-media, or overall.flag_gazepoint_pupil() for marking missing, non-finite, implausible, and IQR-outlying pupil samples in Gazepoint master tables. The function preserves the original master table, adds explicit pupil-quality flags, records the selected pupil/time columns and plausible-value thresholds, and creates pupil_for_preprocessing, where invalid pupil samples are set to NA before interpolation, filtering, or baseline correction.create_gazepoint_preprocessing_registry() for storing reusable preprocessing parameters, including blink/artifact padding, interpolation gap thresholds, smoothing window size, baseline windows, physiological pupil thresholds, speed-outlier thresholds, binocular-disagreement thresholds, baseline-quality thresholds, and overlap-risk settings.flag_gazepoint_pupil_artifacts() for conservative pupil artifact cleaning before interpolation. The function flags blink/trackloss contamination, missing pupil samples, non-finite and non-positive values, pupil-speed outliers, binocular left-right pupil disagreement, and temporal padding around bad samples. It also includes a scale-safety rule that suppresses millimetre-based physiological thresholds when they would remove nearly all non-missing samples, which protects Gazepoint raw-unit exports from being silently erased.interpolate_gazepoint_pupil() for linearly interpolating short internal gaps in pupil time series. The function automatically prefers pupil_clean when available, followed by pupil_for_preprocessing, preserves leading/trailing gaps, avoids long gaps, respects grouping by subject/media/trial, records interpolation status, gap size, gap duration, and produces pupil_interpolated for later filtering or baseline correction.baseline_correct_gazepoint_pupil() for flexible baseline correction of Gazepoint pupil data after flagging and interpolation. The function supports window-based baselines such as c(-200, 0) or c(0, 200), as well as user-defined logical baseline/pre-stimulus flag columns. It produces absolute baseline-corrected values, percent change, ratio, z-scored baseline correction, baseline availability flags, baseline sample counts, and baseline-status labels.smooth_gazepoint_pupil() for sample-based rolling smoothing of Gazepoint pupil time series after interpolation and optional baseline correction. The function supports mean or median smoothing, centred/right/left-aligned windows, custom grouping by subject/media/trial or other columns, optional preservation of missing input rows, and records smoothing status, window size, input column, method, alignment, and minimum valid-points settings.summarise_gazepoint_pupil_windows() for aggregating processed Gazepoint pupil data into user-defined analysis windows. The function supports numeric window breakpoints and labelled custom window tables, flexible grouping by subject, media, trial, condition, AOI, or other columns, and produces analysis-ready summaries including valid/missing pupil counts, percentages, mean, median, SD, quantiles, min/max, AUC, time span, and window-validity status.audit_gazepoint_pupil_gaps() for summarising pupil interpolation and missing-gap structure after interpolate_gazepoint_pupil().audit_gazepoint_pupil_baseline() for checking baseline-correction quality after baseline_correct_gazepoint_pupil().audit_gazepoint_pupil_imbalance() for checking whether preprocessing loss differs across conditions or other groups.audit_gazepoint_pupil_drift() for assessing tonic pupil/time-on-task drift.audit_gazepoint_pupil_overlap_risk() as an event-response overlap and deconvolution-readiness gate.summarise_gazepoint_pupil_trial_features() for converting processed pupil time series into trial-level feature summaries.plot_gazepoint_pupil_status() for visualising observed, interpolated, missing, artifact, and other pupil-sample statuses over time or as grouped percentages.plot_gazepoint_pupil_timecourse() for plotting binned pupil time courses with mean lines and confidence bands.plot_gazepoint_pupil_preprocessing() for single-trial visual audit plots of pupil preprocessing stages.prepare_gazepoint_pupil_window_model_data() for preparing pupil-window summaries or pupil trial-feature tables for confirmatory window-level modelling. The function standardises outcome, subject, condition, window, trial/media identifiers, valid-sample counts, total-sample counts, valid-sample proportions, weights, model-readiness status, and settings. It also supports common Gazepoint pupil-window aliases such as media_id, MEDIA_ID, n_valid_pupil, n_valid_samples, and n_samples.fit_gazepoint_pupil_window_lmm() for fitting confirmatory pupil-window linear mixed models with lme4::lmer(). The function supports condition, window, and condition-by-window fixed effects when available; automatic fallback for single-condition or single-window data; subject random intercepts; optional random window slopes; optional valid-sample weighting; singular-fit detection; fallback models; fixed-effect tables; model-comparison tables; fitted formulas; status labels; and settings.fit_gazepoint_pupil_window_sensitivity() for confirmatory pupil-window model-family sensitivity checks. The function compares unweighted LMMs, weighted LMMs, fixed-effects LMs, and weighted LMs without adding heavy robust-model dependencies, and returns fitted models, formulas, comparison tables, fixed-effect tables, model-status labels, error messages, and settings.summarise_gazepoint_aoi_entries() for converting sample-level AOI states into ordered AOI-entry episodes.prepare_gazepoint_aoi_sequences() for creating transition-ready AOI sequences from sample-level data or AOI-entry tables.summarise_gazepoint_aoi_transitions() for trial-level AOI transition summaries.compute_gazepoint_aoi_transition_matrix() for producing AOI transition count matrices, probability matrices, grouped matrices, and long-form transition tables.plot_gazepoint_aoi_transition_matrix() for plotting AOI transition count or probability heatmaps.summarise_gazepoint_aoi_trial_features() for trial-level AOI feature extraction.summarise_gazepoint_fixation_trials() for trial-level fixation feature extraction from Gazepoint fixation exports.summarise_gazepoint_aoi_windows() for converting sample-level AOI states into predefined AOI time-window summaries. The function supports numeric window breakpoints, labelled window tables, target/distractor AOI definitions, valid/all/AOI-only denominators, condition fallback to all_data, chronological window ordering, and status labels for target-observed and target-not-observed windows.audit_gazepoint_aoi_window_denominators() for checking denominator adequacy before binomial or logistic mixed-effects modelling. The function reports zero, low, missing, imbalanced, and variable denominators by row, window, and condition, and returns overview, row-audit, window-summary, condition-window, imbalance, and flagged-row tables.prepare_gazepoint_aoi_glmm_data() for preparing AOI-window summaries as binomial success/failure data. The function supports valid, all-sample, AOI-only, and custom denominators; creates success, failure, denominator, proportion, weight, subject, condition, and window columns; and records row-level GLMM-readiness status.fit_gazepoint_aoi_window_glmm() for fitting confirmatory AOI-window binomial mixed-effects logistic regression models using lme4::glmer(). The function supports condition, window, and condition-by-window fixed effects, subject random intercepts, optional random window slopes, singular-fit detection, fallback models, model comparison tables, and explicit model-status reporting.fit_gazepoint_aoi_model_sensitivity() for AOI-window model-family sensitivity checks. The function compares the main binomial GLMM against empirical-logit LMM, weighted proportion LMM, and fixed-effects quasibinomial GLM specifications, returning model comparisons, formulas, fixed effects, status labels, and settings.prepare_gazepoint_pupil_gamm_data() for preparing binned pupil time-course data for mgcv::bam() models.fit_gazepoint_pupil_gamm() for fitting pupil time-course GAMMs with mgcv::bam().fit_gazepoint_pupil_pfe_gamm() for gaze-position-adjusted pupil GAMM sensitivity analysis.prepare_gazepoint_gca_data() for Growth Curve Analysis preparation.fit_gazepoint_gca() for fitting GCA mixed models with lme4::lmer().plot_gazepoint_gca() for plotting observed and fitted GCA trajectories.prepare_gazepoint_cluster_data() for preparing sample-level or binned Gazepoint time-course data for cluster-based permutation testing. The function standardises subject, condition, time-bin, outcome, sample-count, trial-count, status, outcome-label, aggregation, bin-size, paired-design, and condition-status fields. It supports pupil time courses, AOI target-looking indicators, and other numeric or logical time-course outcomes.run_gazepoint_cluster_permutation() for paired within-subject cluster-based permutation testing of two-condition time-course divergence. The function uses sign-flip permutations, time-wise paired t-statistics, configurable cluster-forming thresholds, two-sided or directional tests, multiple cluster-statistic options, complete-subject filtering, permutation maximum-cluster distributions, cluster-level p-values, model-status labels, and explicit circularity warnings.summarise_gazepoint_clusters() for converting cluster-permutation results into compact reporting tables, including overview, all observed clusters, significant clusters, time-course summary, permutation-distribution summary, settings, and circularity warning tables.plot_gazepoint_cluster_results() for plotting cluster-permutation results. The function supports mean-difference, test-statistic, or two-panel plots; optional cluster shading; candidate-bin markers; threshold lines; zero-reference lines; custom titles and labels; and publication-ready ggplot2 output.prepare_gazepoint_aoi_gamm_data() for preparing sample-level or binned AOI data for AOI time-course GAMM analysis. The function creates subject-by-condition-by-time-bin binomial success/failure summaries for target-AOI looking, supports AOI-column and logical/numeric indicator workflows, valid/all/AOI-only denominator definitions, condition fallback to all_data, custom time bins, denominator filtering, and model-readiness status fields.fit_gazepoint_aoi_gamm() for fitting binomial AOI target-looking GAMMs using mgcv::bam(). The function models target-AOI looking over time using success/failure counts, supports condition fixed effects when available, condition-specific smooths, subject random-effect smooths, optional subject-specific time smooths, automatic single-condition fallback, model diagnostics, formula reporting, parametric and smooth tables, and captured model warnings.plot_gazepoint_aoi_gamm() for plotting observed AOI target-looking proportions and fitted AOI-GAMM trajectories. The function supports single-condition and multi-condition plots, observed-only and fitted-only views, confidence ribbons, population-level predictions with subject random effects excluded by default, custom labels, and publication-ready ggplot2 output.check_gazepoint_model_convergence() for compact convergence diagnostics across fitted models used in gp3tools workflows. The helper supports lme4 mixed models, mgcv GAM/BAM objects, glm objects, and ordinary lm objects where applicable, and returns a tidy diagnostic table with convergence status, model class, diagnostic status, and message.check_gazepoint_model_singularity() for checking singular random-effects structures in lme4 mixed models using lme4::isSingular(). The helper reports singular fits as structured diagnostic output rather than package failures, and returns not_applicable for model classes where singularity is not meaningful.check_gazepoint_model_overdispersion() for Pearson-residual overdispersion diagnostics in binomial, quasibinomial, Poisson, quasipoisson, and negative-binomial-like models. The helper returns dispersion ratios, Pearson chi-square values, residual degrees of freedom, threshold-based overdispersion flags, and diagnostic messages.diagnose_gazepoint_glmm() as a reusable diagnostics bundle for GLMM, LMM, and GLM workflows. The function combines convergence, singularity, overdispersion, and optional DHARMa simulation-based residual diagnostics into a structured gp3_model_diagnostics object with overview, convergence, singularity, overdispersion, DHARMa, and settings tables.diagnose_gazepoint_gamm() as a reusable diagnostics bundle for mgcv::gam() and mgcv::bam() workflows. The function combines convergence checks, mgcv::k.check() basis-dimension diagnostics, overdispersion checks when meaningful, and optional DHARMa diagnostics into a structured gp3_model_diagnostics object.DHARMa is listed in Suggests, not Imports, and diagnostics skip cleanly with skipped_missing_package when DHARMa is not installed.summarise_gazepoint_fixed_effects() for creating manuscript-ready fixed-effect summary tables from lm, glm, lme4 mixed models, and mgcv GAM/BAM models. The function supports gp3tools fit objects containing a $model element, Wald confidence intervals, optional exponentiation for odds ratios or rate ratios, intercept filtering, significance stars, and structured diagnostic status fields.tidy_gazepoint_model_summary() for combining model metadata, fixed-effect summaries, and optional model diagnostics into a structured gp3_model_summary object. The returned object contains overview, model_info, fixed_effects, diagnostics, and settings components.summarise_gazepoint_emmeans() for estimated marginal means and pairwise contrasts using optional emmeans. The function returns structured overview, emmeans, contrasts, and settings tables and skips cleanly with skipped_missing_package if emmeans is not installed.export_gazepoint_model_tables() for exporting manuscript-ready model summaries, fixed effects, diagnostics, estimated marginal means, contrasts, settings, and export-index tables to CSV files.emmeans support in Suggests for estimated marginal means and pairwise contrasts without making it a required package dependency.create_gazepoint_analysis_decision_audit() for creating a final analysis-decision audit across completed Gazepoint analysis branches. The function records which branches were run, classifies each branch as confirmatory, sensitivity, exploratory, diagnostic, preprocessing, reporting, or unknown, summarises available diagnostics, flags interpretation cautions, and creates a final analysis-readiness table.gp3_analysis_decision_audit object with overview, branch_audit, diagnostics_summary, interpretation_cautions, readiness, and settings components.create_gazepoint_preprocessing_multiverse() for defining preprocessing multiverse specifications across pupil and AOI workflows. The function creates structured pupil, AOI, and combined branch grids for alternative preprocessing decisions such as pupil interpolation gap thresholds, smoothing windows, baseline windows, artifact-padding settings, AOI denominator definitions, and minimum denominator thresholds.run_gazepoint_pupil_multiverse() for running pupil preprocessing branches from a preprocessing multiverse object. The runner applies branch-specific artifact flagging, interpolation, baseline correction, smoothing, and optional pupil-window summarisation, while recording completed and failed branches.run_gazepoint_aoi_multiverse() for running AOI preprocessing branches from a preprocessing multiverse object. The runner creates AOI-window summaries and branch-specific AOI GLMM preparation tables using alternative denominator and minimum-denominator decisions.summarise_gazepoint_multiverse_results() for combining pupil and AOI multiverse results into overview, branch-summary, failure-summary, and settings tables.plot_gazepoint_multiverse_results() for visualising multiverse branch status, retained rows, pupil preprocessing settings, and AOI denominator settings.audit_gazepoint_event_sync() for checking event-marker availability, expected event labels, duplicate timestamps, sparse units, and unusually large time gaps.audit_gazepoint_design_balance() for auditing observed subject-by-condition design balance before exclusions.audit_gazepoint_exclusion_flow() for summarising retained versus excluded analysis units, exclusion reasons, condition-level retention, and subject-level retention.audit_gazepoint_gaze_signal_quality() for auditing gaze-coordinate availability, validity columns, missing gaze, off-screen gaze, and optional pupil availability.audit_gazepoint_condition_quality_imbalance() for checking whether quality metrics differ across experimental conditions.audit_gazepoint_post_exclusion_balance() for checking whether retained analysis units remain balanced across subjects and conditions after exclusions.audit_gazepoint_aoi_geometry() for checking AOI size, area, coordinate validity, screen-bound status, and duplicate AOI geometry.audit_gazepoint_aoi_overlap() for identifying pairwise AOI overlap within each stimulus or media item.audit_gazepoint_aoi_margin_sensitivity() for auditing whether AOI assignments are sensitive to small boundary expansions or shrinkages.audit_gazepoint_aoi_coding_matrix() for validating observed AOI labels against geometry-derived AOI labels and producing coding/confusion matrices.plot_gazepoint_aoi_verification() for visual AOI verification with optional gaze-point overlays.Added advanced AOI/state sequence-model preparation helpers:
create_gazepoint_markovchain_object()prepare_gazepoint_semimarkov_data()prepare_gazepoint_hmm_data()Added create_gazepoint_markovchain_object() for creating dependency-free Markov-chain-style AOI/state objects with transition counts, transition probabilities, transition matrices, sequence-level transition data, state ordering, optional state exclusion, optional missing-state labelling, optional self-transition handling, and Laplace smoothing.
Added prepare_gazepoint_semimarkov_data() for converting ordered AOI/state observations into semi-Markov-ready state-visit and transition tables with dwell durations, next-state labels, terminal-state handling, sequence summaries, state summaries, transition summaries, optional covariate carry-through, and optional repeated-state collapsing.
Added prepare_gazepoint_hmm_data() for creating dependency-free HMM-ready AOI/state sequence structures with ordered observation data, initial-state probabilities, transition count/probability matrices, transition tables, observation summaries, emission-format data, optional numeric observation scaling, optional terminal-state transitions, optional covariate carry-through, and optional missing-state labelling.
Added dependency-free package-adapter helpers for exporting gp3tools master/sample tables to external R eye-tracking workflows:
prepare_gazepoint_eyetrackingr_data()prepare_gazepoint_pupillometryr_data()prepare_gazepoint_gazer_data()prepare_gazepoint_eyetools_data()These helpers create clean, package-friendly tibbles without importing or depending on the external packages directly.
Added eyetrackingR-style sample-level export with participant, trial, time, gaze coordinates, AOI labels, AOI indicator columns, trackloss status, and adapter metadata.
Added pupillometryR-style sample-level export with participant, trial, time, pupil, event, baseline, pupil-validity, trackloss, and adapter metadata.
Added gazer-style sample-level export with participant, trial, time, gaze coordinates, optional pupil, AOI labels, fixation IDs, validity flags, off-screen detection, trackloss status, and adapter metadata.
Added eyetools-style sample-level export with participant, trial, time, primary and binocular gaze coordinates, pupil columns, AOI labels, fixation IDs, event labels, validity flags, off-screen detection, trackloss status, and adapter metadata.
Added estimate_gazepoint_divergence_point() for estimating the earliest reliable divergence between two condition time courses using bootstrap confidence intervals. The helper supports participant-, trial-, and row-level bootstrap units, mean or median summaries, directional testing, consecutive-point onset rules, no-divergence handling, optional bootstrap-output retention, and onset-time uncertainty summaries.
Added run_gazepoint_model_leave_one_out() for generic leave-one-unit model sensitivity analysis. The helper refits a user-supplied model while leaving out one participant, item, stimulus, trial, or other unit at a time. It supports custom effect extraction, effect-term filtering, fit/extraction error tracking, optional model retention, and effect-stability summaries including maximum absolute change, largest-change unit, percent change, and sign-flip detection.
Added transform_gazepoint_aoi_empirical_logit() for transforming bounded AOI proportions into finite empirical logits. The helper supports numerator/denominator count input, proportion-only input with pseudo-denominators, correction constants for 0/1 proportions, custom output columns, overwrite protection, row-level transformation statuses, and overview/status/settings attributes.
Added prepare_gazepoint_fixation_aligned_data() for fixation-, saccade-, and AOI-contingent alignment. The helper aligns observations to first target entry, first fixation to target, first saccade to AOI, first fixation, or custom event markers, and returns aligned data, event tables, trial summaries, baseline/analysis-window flags, pre/post-event phases, target-preexisting flags, and already-on-target-at-start indicators.
Added plot_gazepoint_model_predictions() for plotting observed summaries together with model-implied prediction trajectories. The helper supports model objects for which predict() works, including lm, glm, mixed-model, GAMM, and GCA-style workflows, and stores observed summaries, prediction summaries, overview metadata, and settings as plot attributes.
Added compare_gazepoint_nested_models() for comparing ordered nested models. The helper returns model-level AIC, BIC, log-likelihood, degrees of freedom, likelihood-ratio tests, model rankings, sequential or against-first comparisons, extraction/comparison statuses, and fallback support for simple custom list-like model wrappers.
Added flag_gazepoint_pupil_hampel() as an optional Hampel-filter pupil artifact helper. The function applies a rolling Hampel filter to pupil data, supports grouping and time ordering, configurable window size, threshold multiplier, minimum valid samples, MAD scaling, optional corrected pupil output, custom output columns, overwrite protection, row-level statuses, and overview/status/settings attributes.
Added recalibrate_gazepoint_gaze() for offline gaze recalibration and drift correction using known target or check-target coordinates. The helper estimates group-level horizontal and vertical gaze shifts, applies median or mean drift correction, supports calibration-row filters, maximum-shift blocking, grouped correction summaries, row-level statuses, before/after target-error columns, custom output columns, and overview/status/settings attributes.
Added recommend_gazepoint_exclusions() for creating explicit trial-level and participant-level exclusion recommendations. The helper uses validity flags, gaze-coordinate missingness, pupil missingness, artifact flags, sample-count thresholds, missingness thresholds, and artifact-rate thresholds to return transparent participant recommendations, trial recommendations, a combined exclusion table, overview metadata, and settings. The helper recommends exclusions only; it does not remove data.
FPOGS, FPOGD, FPOGX, FPOGY, FPOGID, FPOGV, AOI, MEDIA_ID, and informative participant identifiers such as USER_FILE.all_data fallback in pupil time-course preparation, GAMM modelling, GCA preparation, GCA plotting, AOI-window summaries, AOI GLMM preparation, AOI-window mixed modelling, pupil-window model-data preparation, pupil-window mixed modelling, and cluster-data preparation.check_gazepoint_real_data_readiness(), an explicit final readiness gate for real-data analysis. The helper returns structured overview, gate_decision, checks, detected_columns, data_summary, condition_summary, and settings outputs, with pass/warn/fail readiness status.run_gazepoint_eyetools_fixation_detection(), an optional external-detector wrapper for eyetools. The wrapper prepares Gazepoint data using the expected pID, trial, time, x, and y schema, supports dispersion, VTI fixation, and VTI saccade branches, and records clean skipped, complete, partial-complete, and error statuses.create_gazepoint_reporting_checklist(), an auto-generated reporting checklist for manuscript/report preparation. It summarises reporting readiness across data structure, readiness gates, import/workflow checks, sampling/tracking quality, AOI reporting, pupil reporting, model diagnostics, sensitivity analyses, reproducibility, and optional advanced methods.compute_gazepoint_time_varying_transition_matrix(), a dedicated helper for transition-count and transition-probability matrices by time window and grouping variables.fit_gazepoint_transition_count_nb_sensitivity(), an optional negative-binomial transition-count sensitivity model using glmmTMB when available.run_gazepoint_gazer_crosscheck(), an optional external gazeR preprocessing cross-check wrapper.audit_gazepoint_stimulus_luminance(), audit_gazepoint_pupil_reliability(), and interpolate_gazepoint_pupil_pchip() for pupil-analysis robustness and reporting support..env pronoun use in internal helper functions by replacing dplyr pronoun-based assignment with explicit local data-frame assignment.Recent focused validations completed successfully for the following advanced helpers:
devtools::test(filter = "estimate_gazepoint_divergence_point")
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 95 ]
devtools::test(filter = "run_gazepoint_model_leave_one_out")
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 98 ]
devtools::test(filter = "prepare_gazepoint_fixation_aligned_data")
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 117 ]
devtools::test(filter = "transform_gazepoint_aoi_empirical_logit")
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 89 ]
devtools::test(filter = "plot_gazepoint_model_predictions")
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 83 ]
devtools::test(filter = "compare_gazepoint_nested_models")
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 95 ]
devtools::test(filter = "flag_gazepoint_pupil_hampel")
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 77 ]
devtools::test(filter = "recalibrate_gazepoint_gaze")
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 109 ]
devtools::test(filter = "recommend_gazepoint_exclusions")
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 89 ]
The current full-package validation status after the README, vignette, and example-data branches is:
devtools::test()
# [ FAIL 0 | WARN 0 | SKIP 0 | PASS 6788 ]
devtools::check()
# 0 errors | 0 warnings | 0 notes
During full tests, boundary (singular) fit: see help('isSingular') messages may appear in mixed-model diagnostic contexts. These are expected diagnostic messages from singular-fit test fixtures and are not package failures when the final test summary reports FAIL 0 | WARN 0.
On some Windows systems, a Quarto/TMPDIR message may appear after devtools::check(). This is harmless when the final R CMD check results report:
0 errors | 0 warnings | 0 notes
Added lightweight built-in example datasets so README examples, vignettes, tests, and user workflows can run without private Gazepoint files:
gazepoint_example_mastergazepoint_example_fixationsgazepoint_example_aoi_geometrygazepoint_example_aoi_windowsgazepoint_example_pupil_windowsAdded data-raw/create_gazepoint_example_data.R to regenerate the example datasets reproducibly.
Added dataset documentation in R/data.R.
Added focused tests for the example datasets and their compatibility with core master-table, pupil-window, AOI-window, and AOI-geometry workflows.
Excluded data-raw/ from the built package via .Rbuildignore to avoid R CMD check top-level file notes.
Current validation after the example-data branch:
devtools::test() reports FAIL 0 | WARN 0 | SKIP 0 | PASS 6788.devtools::check() reports 0 errors | 0 warnings | 0 notes.emmeans support, CSV export of model tables, model-implied prediction visualisation, and nested model-comparison reporting.devtools::test() passes with 6788 tests, and devtools::check() returns 0 errors, 0 warnings, and 0 notes.