Package index • TempleCBE

Data Quality & Missingness Analysis

Functions for missing data auditing, summary tables, and visualizations.

count_na(): Count Total Missing (NA) Values
features_percent_miss(): Calculate Percentage of Missing Data Per Feature
plot(<features_percent_miss>) plot_features_percent_miss(): Plot method for features_percent_miss objects
missmap(): Missingness Map
add_missing(): Add Missing Values Completely at Random
summary_table(): Summary Table Function
get_dataset_info() describe_columns(): Summarize a Data Frame or Joint Model's Columns and Components
compare_df(): Compare Two Data Frames (Modelled on SAS PROC COMPARE)
autoplot(<cbe_compare_df>): Autoplot Method for CBE Data Frame Comparison
reconcile_column_dictionary(): Reconcile a Data Frame's Column Names With a Column Dictionary

Clinical Databases & Multi-Table Management

Functions for multi-table clinical databases, metadata export/import, variable role assignment, and relational linkage analysis.

dataset_label() `dataset_label<-`() database_name() `database_name<-`() database_label() `database_label<-`() set_dataset_labels() get_dataset_labels(): Get or Set Dataset and Database Labels
write_database_to_excel() write_workbook() write_database(): Write an R Database or Data Frames to an Excel Workbook
read_database_from_excel() read_database(): Read an R Database from an Excel Workbook
write_xlsx_file(): Write Data Frame to Excel (.xlsx) File
write_database_metadata() read_database_metadata(): Export or Import Dataset Metadata
apply_database_metadata(): Apply Metadata and Variable Roles to an R Database
variable_roles() set_roles() get_roles() get_predictors() get_outcomes() get_id_cols() get_time_cols(): Define and Manage Clinical Variable Roles
database_relationships() as_tbl_graph(<cbe_database_relationships>) find_shared_keys() check_key_integrity(): Analyze Relationships and Linkages Across Datasets in an R Database
autoplot(<cbe_database_relationships>): Autoplot Method for Database Relationships
cbe_database_venn() database_venn(): Multi-Dataset Key Overlap Venn Diagram
autoplot(<cbe_key_integrity>): Autoplot Method for Key Integrity
as_database(): Convert a Graph Object Back Into an R Database
database_setops: Set Operations on R Databases
database_to_igraph(): Build an igraph From an R Database's Nodes/Edges Tables

Normalization & Outlier Detection

Functions for scaling numeric features and flagging statistical outliers.

min_max_norm(): Min-Max Data Normalization
z_norm(): Z-Score Standard Normalization
range_norm(): Range Normalization
detect_outliers(): Detect Outliers Across a Data Frame's Numeric Columns
calculate_fences(): Calculate Inner and Outer IQR Fences
flag_outliers(): Flag and Classify Outliers

Principal Component Analysis & Dimension Reduction

Unsupervised dimensionality reduction matching SAS PROC PRINCOMP output: variance decomposition, eigenvalue tables, Kaiser-Guttman scree plots, biplots with concentration ellipses, and variable correlation circles.

run_pca(): Process and Plot Principal Component Analysis (PCA)
pca_plot(): Plot a PCA Fit
pca_scree_plot(): PCA Scree Plot
pca_variables_plot(): PCA Variable Correlation Circle
pca_biplot(): PCA Loadings Biplot
plot_pca_bi(): PCA Biplot
pca_percent_var_explained(): Percent Variance Explained by Each Principal Component
pca_feature_loading_heatmap(): PCA Feature-Loading Heatmap
rotation_matrix() pca_loadings(): PCA Rotation Matrix (Loadings)
pca_eqns(): PCA Equations
pca_loading_diff(): Difference in PCA Loadings Between Two Fits
pca_loading_diff_heatmap(): Heatmap of PCA Loading Differences Between Two Fits
pca_loading() pca_variance_pct() pca_top_features(): Scalar Accessors for PCA Results (for Inline Prose)
pca_equation_latex(): PCA Component as a LaTeX Linear Combination
pca_component_cutoff(): Number of Components Needed to Reach a Cumulative Variance Threshold

Penalized Cox Models for Survival Data

Elastic-net Cox regression for right-censored and start/stop outcomes, cross-validation and nested cross-validation with yardstick survival metrics, and helpers for scoring start/stop models correctly.

coxnet(): Penalized Cox Regression for Right-Censored or Start/Stop Survival Data
coxnet_train() predict_coxnet_linear_pred() predict_coxnet_time() predict_coxnet_survival(): The `coxnet` Engine for `parsnip::proportional_hazards()`
predict(<coxnet_model>): Predict From a `coxnet` Model
tidy(<coxnet_model>): Tidy the Coefficients of a `coxnet` Model
cv_coxnet(): Cross-Validate a Penalized Cox Model With yardstick Survival Metrics
predict(<cv_coxnet>) tidy(<cv_coxnet>) collect_metrics(<cv_coxnet>) autoplot(<cv_coxnet>): Use a `cv_coxnet` Result
nested_cv_coxnet() collect_metrics(<nested_cv_coxnet>): Nested Cross-Validation of a Penalized Cox Model
surv_subject_truth(): Collapse Survival Outcomes to One Row per Subject
validate_start_stop(): Pre-Flight Validation for Counting-Process (Start/Stop) Survival Data
prune_start_stop(): Clean Counting-Process Intervals
censoring_km() predict(<censoring_km>): Kaplan-Meier Estimate of the Censoring Distribution
graf_weights(): Inverse Probability of Censoring Weights (Graf et al.)
add_graf_weights(): Add Censoring Weights to Survival Predictions for yardstick
glmnet_IBS(): Integrated Brier Score of a Penalized Cox Model on Start/Stop Survival Data
tune_over_alpha(): Tune a Penalized Cox Model Over a Grid of `alpha` Values
tune_alpha_by_split(): Tune Over `alpha` for Every Split of a Resample
checkpoint_map(): Map Over Elements With Fingerprinted Checkpoints
tune_grid_checkpointed(): Checkpointed Tuning Over Resamples (`tune_grid_checkpointed`)
get_templecbe_cox_ties() with_quiet_cox_ties() apply_workflow_cox_ties(): Cox Tie-Handling Configuration and Quiet Scopes
fit_resamples_checkpointed(): Checkpointed Model Evaluation Across Resamples (`fit_resamples_checkpointed`)
workflow_map_checkpointed(): Checkpointed Workflow Sets Mapping (`workflow_map_checkpointed`)
tune_race_survival_checkpointed(): Checkpointed Survival Racing Optimization (`tune_race_survival_checkpointed`)
collect_checkpoints(): Collect and Merge Sharded Checkpoints From `checkpoint_dir`
clean_checkpoints(): Clean Up Mismatched or Superseded Checkpoint Files (E5)
invalidate_downstream_checkpoints(): Delete the Checkpoints Downstream of a Changed Node
estimate_runtime(): Estimate Runtime Before a Long Tuning or Scoring Run
nested_survival_run(): Run an End-to-End Nested Cross-Validation Survival Pipeline (E1)
summary(<cbe_nested_survival_run>): Summarize a `cbe_nested_survival_run` Object
plot(<cbe_nested_survival_run>) autoplot(<cbe_nested_survival_run>): Plot a `cbe_nested_survival_run` Object
tidy(<cbe_nested_survival_run>): Tidy a `cbe_nested_survival_run` Object
nested_survival_report_template(): Packaged Nested Survival Report Quarto Child Template
as_tuning_results(): Coerce Historical or Tidymodels Tuning Outputs to Standardized Schema (G5)
compare_censoring_weights(): Compare Model Performance Under Unweighted and IPCW Censoring Weights
score_outer(): Score Candidate Models on Outer Resampling Split(s)
select_candidate_models(): Select Candidate Models From Tuning Results
sample_candidate_formulas(): Sample Candidate Model Formulas
sample_candidate_formulas_adaptive(): Sample Candidate Formulas Conditioned on Earlier Tuning Results
calculate_feature_weights(): Weight Features by the Candidate Models That Use Them
tidy_outer_coefficients() unnest_outer_coefficients(): Tidy Outer Model Coefficients into Long Format
summarize_outer_importance(): Summarize Predictor Importance and Selection Stability Across Outer Folds
plot_outer_importance(): Plot Outer-Fold Coefficient Importance and Selection Frequency
fit_ibs_mixed_model(): Fit a Mixed-Effects Model for Integrated Brier Score Against Alpha
fit_lmer_safely(): Fit a Linear Mixed-Effects Model With Safe Fallback
boot_lmer(): Cluster Bootstrap of a Linear Mixed-Effects Model
tune_breakpoint_lmm(): Sweep Candidate Knots of a Piecewise-Linear Mixed Model
impute_piecewise_lmm(): Impute Missing Values with a Piecewise-Linear Mixed Model
inspect_rank_deficiency(): Audit Rank Deficiency of a Model's Fixed-Effects Matrix
boot_p_value(): Two-Tailed Bootstrap P-Value
plot_tune_alpha_ibs(): Plot Integrated Brier Score Against Alpha With Dynamic Model Fit
plot_tune_lambda_ibs(): Plot Penalty Lambda Versus Integrated Brier Score
plot_tune_coef_boxplot(): Plot Coefficient Estimates Distribution Across Tuning Resamples
plot_tune_ibs_by_resample(): Plot Integrated Brier Score Distribution Across Resampling Splits
tbl_tune_alpha(): Summary Table of Tuning Performance Across Alpha Values
tbl_tune_coef(): Summary Table of Coefficient Stability Across Tuning Resamples
tbl_censoring_comparison(): Render Censoring Weight Sensitivity and Rank Agreement Table
compare_runs(): Compare and Diff Outer-Fold Runs Across Platforms With Tolerance
compare_tuning_runs(): Compare and Summarize Two Tuning Runs
tune_diagnostics: Tuning Diagnostics and Visualization Family
workflow_tuning: Tidymodels Workflow Tuning and Feature Importance Summaries
racing_workflows: Racing Methods and Controls for Survival Workflows
control_race_survival(): Control Parameters for Racing Survival Workflows
tune_race_survival(): Adaptive Racing Tuning for Survival Workflows and Workflow Sets
step_lencode_coxnet() tunable(<step_lencode_coxnet>) required_pkgs(<step_lencode_coxnet>): Supervised Linear Encoding of Factors via Penalized Cox Models
loco_mp_coxnet(): Leave-One-Covariate-Out Inference with MiniPatch Ensembles (LOCO-MP) for Cox Models
autoplot(<loco_mp_coxnet>): Autoplot Method for LOCO-MP Feature Importance
tidy(<loco_mp_coxnet>): Tidy a LOCO-MP Cox Model Object

Joint Survival-Status-Time Models

Coordinated multi-paradigm modeling blending penalized Cox survival, binary event classification, follow-up duration regression, and calibrated stacked ensembles.

joint_model(): Joint Survival-Status-Time Model
predict(<joint_model>): Predict Method for Joint Models
tidy(<joint_model>): Tidy Method for Joint Models
print(<joint_model>): Print Method for Joint Models
cv_joint_model(): Cross-Validation for Joint Models
nested_cv_joint_model(): Nested Cross-Validation for Joint Models
autoplot(<cv_joint_model>): Autoplot Method for Cross-Validated Joint Models
extract_surv_components(): Extract Survival Outcome Components and Predictors
step_lencode_joint_model() required_pkgs(<step_lencode_joint_model>): Supervised Linear Encoding of Factors via the Cox Component of a Joint Model

Model Explainability & Variable Attributions

Unified explainability across survival and joint models using survex and DALEX, custom IPCW Integrated Brier loss functions, and SurvSHAP(t) attributions.

explain_survival(): Explain Survival Models via survex and DALEX
adapter_tidymodels_survex(): Survival Probabilities From a Tidymodels Workflow as a Matrix (for survex)
explain_joint() explain_model() explain_status() explain_time() explain_stack(): Explain Joint Model Components via DALEX and survex
predict_parts_shap(): SHAP Variable Attributions across Survival and Joint Models
survex_loss_ibs() survex_loss_brier(): TempleCBE Custom Loss Functions for survex

Tidymodels Recipe Steps & Modeling Utilities

Recipe steps and workflow set helpers.

step_famd() tidy(<step_famd>): Factor Analysis of Mixed Data (FAMD) Recipe Step
workflow_set_params(): Tuning Parameters of a Ranked Workflow in a Workflow Set
workflow_set_rank_fit(): Fit the Configuration Ranked `.rank` in a Workflow Set
tune_best_glmnet_workflow(): Select and Finalize Best Penalized Cox Workflow
summarize_dalex_importance(): Extract and Summarize Permutation Variable Importance
plot_permutation_importance(): Plot Permutation Variable Importance
tbl_permutation_importance(): Summary Table of Permutation Variable Importance

Missing Data Imputation

Per-column mtry sweeps for missForest and missRanger imputation.

missforest_sweep_mtry(): Impute a Data Frame by Sweeping `missForest` Over `mtry`
missforest_oob_by_mtry(): Run `missForest` at a Single `mtry` and Report Variablewise OOB Error
missforest_impute_by_mtry(): Assemble Imputed Columns From Their Best-`mtry` Runs
missranger_sweep_mtry(): Impute a Data Frame by Sweeping `missRanger` Over `mtry`
missranger_oob_by_mtry(): Run `missRanger` at a Single `mtry` and Report Per-Column OOB Error
missranger_max_mtry(): Largest `mtry` `missRanger` Will Accept For a Data Set
tune_impute_numeric(): Benchmark Numeric Imputation Methods Using Relative Squared Error (RSE)
compare_imputation_rse(): Relative Squared Error of Imputation Methods Against a Null Baseline
tbl_best_imputation_method(): Best Imputation Method per Column
build_best_imputed_data(): Assemble the Final Imputed Data From the Winning Methods
plot_rse_by_column(): Plot Imputation Performance Across Columns
plot_rse_vs_missingness(): Plot Imputation Error Against the Percent Missing
compare_domain_datasets(): Compare an Original and an Imputed Version of One Domain's Dataset
compare_imputed_ecdf(): Compare Original vs Imputed Empirical Cumulative Distribution Functions
test_imputed_distributions(): Statistical Distribution Comparison of Original vs Imputed Values
plot_imputed_ridgeline(): Ridgeline Density Comparison of Original vs Imputed Distributions
imputation_diagnostics: Imputation Quality Auditing and Method Comparison Toolkit

Statistical Testing & EDA

Biostatistical test wrappers, correlation matrices, and distribution tests.

single_t_test(): Single T-Test, Tidied
multiple_t_test(): Multiple T-Tests Against One Classifier
one_vs_rest_t_test(): One-vs-Rest T-Tests Across a Multi-Level Factor
corr_test_all(): Pairwise Correlation Tests Across All Numeric Columns
correlation_plot(): Correlation Plot
correlation_plot_split(): Correlation Plot, Split Into Legible Sub-Plots
plot_target_correlation_heatmap(): Correlation of One Target With Many Candidate Predictors
plot_scatter_glm(): Scatter Plot With a Regression Line and an Automatic Statistics Sentence
correlation_diff(): Difference in Correlation Matrices Between Two Datasets
correlation_diff_heatmap(): Heatmap of Correlation Differences Between Two Datasets
drop_correlated_columns(): Find Highly Correlated Columns
correlated_pairs_table(): Formatted Table of Highly Correlated Variable Pairs
plot_corr_scatter(): Scatter Plot of Two Correlated Variables With Regression Fit and Statistics
plot_rank_heatmap(): Heatmap of Variable or Model Ranks Across Resamples or Metrics
distribution_test(): Check a Vector or Data Frame's Distribution
distribution_plot(): Distribution Plot
is_normal(): Test Whether a Vector Looks Normally Distributed
is_poisson(): Test Whether a Vector Looks Poisson-Distributed
is_integer_valued(): Is a Vector Composed of Integer-Valued Numbers
significance_stars(): P-value Significance Stars

Composite Clinical & Biomarker Indices

Construct composite indices, center variables, and generate summary tables and correlation structures of index components.

compute_composite_index(): Compute a Composite Clinical or Biomarker Index
composite_index: Composite Index Construction and Variable Correlation Table
center(): Center a Numeric Vector
index_variable_table(): Summary Table of Composite Index Component Variables

PCA & Dimensionality Reduction

Principal Component Analysis utilities, loadings, and biplots.

rotation_matrix() pca_loadings(): PCA Rotation Matrix (Loadings)
pca_eqns(): PCA Equations
pca_percent_var_explained(): Percent Variance Explained by Each Principal Component
pca_feature_loading_heatmap(): PCA Feature-Loading Heatmap
plot_pca_bi(): PCA Biplot
pca_biplot(): PCA Loadings Biplot
pca_loading_diff(): Difference in PCA Loadings Between Two Fits
pca_loading_diff_heatmap(): Heatmap of PCA Loading Differences Between Two Fits
pca_plot(): Plot a PCA Fit

Visualization & Reporting

Manhattan/volcano plots, report generation, and document conversions.

manhattan_plot(): Manhattan Plot
volcano_plot(): Volcano Plot
create_report(): Scaffold a New Report From a Template
zip_render(): Render a Quarto Document and Zip It With Its Dependencies
pdf_to_rtf(): Convert a PDF's Text to Rich Text Format (RTF)
create_toc_from_sas_pdf(): Build a Table of Contents from a SAS-Generated PDF
read_excel_multiple_headers(): Read Excel Data With Multi-Row Column Headers
read_workbook(): Read Every Sheet of an Excel Workbook
reexports %>%: Objects exported from other packages
km_summary_to_prism(): Convert a Kaplan-Meier Summary Table to a GraphPad Prism Survival Table
convert_pdf_to_docx(): Convert a Single PDF to DOCX Using the Best Available Backend
convert_pdfs_to_docx(): Convert PDFs to DOCX Using the Best Available Backend
check_docx_toolchain(): Report Which PDF -> DOCX Backends Are Usable on This Machine
find_python(): Locate a Python Interpreter That Can Import a Module
find_soffice(): Locate a LibreOffice Headless Binary
run_sas_script(): Run a SAS Program in Batch Mode
find_sas() sas_available(): Locate a SAS Executable
sas_macro_dir(): Directory of TempleCBE SAS Macros
sas_macro_path(): Path to a TempleCBE SAS Macro File
rossi_data(): Rossi Recidivism Dataset
render_pipeline(): Render Pipeline Documents and Reports
render_variants(): Render One Report Template for Several Variants of Its Parameters
render_parameterized_pair(): Render One Template Twice: the Original and the Imputed Analysis
render_parameterized_study(): Render a Whole Folder of Reports Once per Parameter Set
expand_child_template(): Expand and Knit a Child Template Once per Row of a Parameter Grid
zip_reports(): Package Multiple Already-Rendered Reports Into an Indexed Zip
write_run_manifest(): Write a Run Manifest JSON Beside Render Outputs
repro_appendix(): Generate a Self-Rendering Reproducibility Appendix (E2)
repro_appendix_template(): Path to the Ready-Made Quarto Reproducibility Appendix Child Template
domain_children(): Expand and Knit Per-Domain Child Templates With Failure Isolation (E4)
check_renv_guard(): Guard Against Inactive renv or Lockfile Disagreements
extract_yaml_formats(): Extract Output Formats From Document YAML Front Matter
scan_data_io(): Audit Data File Read/Write Calls Against a Project's Files on Disk
find_read_calls(): Search a Directory Tree for File-Read Calls
find_write_calls(): Search a Directory Tree for File-Write Calls

Pipeline & Computational Graph Architecture

Language-, format-, and tool-agnostic dependency graphs, file-level metadata tracking, and interactive DAG visualizations for reproducible biostatistical pipelines.

compute_graph: Computational Pipeline Dependency Graphs
pipeline_config(): Get or set global pipeline configuration options
pipeline_summary() summarize_pipeline(): Produce an executive summary of the computational pipeline
as_pipeline_graph(): Convert pipeline objects or igraph to a tidygraph tbl_graph
as_igraph(): Convert to an enriched igraph graph with rich metadata
as_tbl_graph(<list>): Convert pipeline list to tidygraph
summary(<tbl_graph>): S3 summary method for tbl_graph objects
visualize_pipeline(): Visualize FilePath dependencies with stage-based coloring and staleness check
visualize_pipeline_interactive(): Generate an interactive visNetwork visualization of the computation graph
print_pipeline(): Print the dependency graph as a PNG image
collapse_by_stage(): Collapse an igraph pipeline graph to one node per pipeline `stage`
export_subgraph(): Export a focused interactive subgraph centered around a focal report or stage
export_interactive_pipeline(): Export the computational graph to a self-contained interactive HTML file for stakeholders
list_stage_data_artifacts(): Inspect and categorize all on-disk data artifacts for a pipeline stage
produces_data(): Check if a FileOutputs object produces datasets (outputs an rds, xlsx, etc. file)
create_qmd_renderer(): Create a FileOutputs object for a rendering QMD or R Markdown document
get_render_plan(): Get the topologically sorted list of QMDs that need to be re-rendered
interactive_render_pipeline(): Choose Interactively What `render_pipeline()` Renders
is_stale(): Check if an Output File is Stale
record_pipeline_hashes(): Record or Update Content Hashes in `pipeline_hashes.json`
save_pipeline() load_pipeline(): Save and Load Computational Pipeline Graph
join_pipelines(): Join two computational pipeline graphs
dedupe_pipeline(): Deduplicate Pipeline Objects by Name
refresh_pipeline(): Refresh Modification Timestamps Across Pipeline Objects
export_pipeline_diagram(): Export Pipeline Diagram to Static Vector or Raster Format
graph_intersect() graph_subtract() graph_union(): Intersect, Subtract, or Union Two Graph Objects
FilePath(): Construct a FilePath object
show(<FilePath>): FilePath S4 Class
FileUses(): Construct a FileUses object
show(<FileUses>): FileUses S4 Class
FileOutputs(): Construct a FileOutputs object
show(<FileOutputs>): FileOutputs S4 Class

Investigator Name Pseudonyms

Generate synthetic investigator names and replace real investigator names with pseudonyms in reports. Scope: investigator names only. These functions do not de-identify dates, record numbers, or free text.

pi_anonymizer: Investigator Name Pseudonymization Helpers
generate_last_names(): Procedural Synthetic Last Name Generator
generate_pseudonym_token(): Generate Pseudonym Tokens (Cryptographic Hash)
generate_pi_names(): Generate Random PI Names or Tokens
anonymize_pi(): Pseudonymize Investigator Names (Vectorized)

Temple Brand

Temple University colors, ggplot2 scales and theme, and setup for the quarto_temple_brand Quarto extension.

temple_colors(): Temple University Brand Colors
temple_pal(): Temple Color Palettes
scale_colour_temple() scale_color_temple() scale_fill_temple(): Temple Color and Fill Scales for ggplot2
theme_temple(): Temple ggplot2 Theme
temple_brand_path(): Path to the Bundled Temple brand.yml
use_temple_brand(): Install the Temple Brand Quarto Extension Into a Project

Temple CBE Themes, Decks, & Formatters

Center for Biostatistics & Epidemiology ggplot2 themes, institutional cherry palettes, deck styling, and clinical reporting formatters.

cbe_palette: Temple CBE Color Palette
theme_cbe(): Temple University CBE ggplot2 Themes, Palettes, and Formatters
theme_cbe_deck(): CBE ggplot2 Theme for Presentation Decks
scale_color_cbe(): Discrete Color Scale for Temple CBE
scale_fill_cbe(): Discrete Fill Scale for Temple CBE
fmt_pct(): Format a Percentage
fmt_num(): Format Numbers to Fixed Decimals
fmt_sig(): Format Significant Figures
fmt_p(): Format P-Values
pformat() cbe_pformat(): Format p-values for Biostatistical and Clinical Reporting
fmt_hr(): Format Hazard Ratio and Confidence Interval
words(): Combine Character Vector into Natural-Language Words
words_list(): Oxford-Comma List With a Fallback for Empty Input
pluralize(): Count and Word With the Right Plural
include_landscape_figure(): Include a Figure on a Landscape Page (PDF Only)
plot_normalization_comparison(): Compare One Variable Before and After Normalization
read_positional_excel_headers(): Read an Excel Sheet Whose Header Spans Several Rows

Cox Models, Kaplan-Meier, & Diagnostics

Univariable and multivariable Cox proportional hazards screening engines with automated clinical interpretations, shared proportional hazards diagnostics, presentation tables, a paired Kaplan-Meier/Cox helper, a factor-releveling utility, and forest, survival-curve, and marginal-risk visualizations.

cox_single(): Univariable Cox Proportional Hazards Screening Engine
batch_cox_single(): Univariate Cox Screening of Many Features
tbl_cox_glance_compare(): Compare the Fit of Several Cox Models in One Table
glm_single() lmm_single() plot_coef_forest() tbl_coef_forest(): Univariate GLM and Mixed-Model Screens, With Coefficient Forest Plots and Tables
transform_longitudinal_features(): Per-Subject Summaries of Repeated Measurements
plot_feature_transform_heatmap(): Heatmap of Features by Transformation, Coloured by Significance
print(<cbe_cox>): Print Method for cbe_cox Object
cox_multi(): Multivariable Cox Proportional Hazards Modeling Engine
print(<cbe_cox_multi>): Print Method for cbe_cox_multi Object
cox_check(): Tidy Proportional Hazards Diagnostics for Cox Models
print(<cbe_cox_check>): Print Method for cbe_cox_check Object
cbe_cox_table() cox_table() tbl_cox(): Presentation-Ready Cox Coefficient Table
km_single(): Univariable Kaplan-Meier and Cox Screening Engine
print(<cbe_km>): Print Method for cbe_km Object
km_cutpoints: Kaplan-Meier Optimal Cutpoint Search and Visualization
km_cutpoint_search(): Search for Optimal Survival Cutpoint via Log-Rank Separation
km_cutpoint_time_varying(): Optimal Survival Cutpoint Search for Time-Varying Counting Process Data
plot_km_cutpoints(): Plot Cutpoint Search Profile
batch_cutpoint_survdiff(): Scan Cutpoints of Several Features with the Log-Rank Test
first_significant_cutpoint(): First Significant Cutpoint per Feature
relevel_factor(): Relevel a Factor's Reference Level
plot_cox_forest(): Forest Plot of Hazard Ratios with Confidence Intervals
plot_cox_forest_multi(): Forest Plot of Hazard Ratios for a Multivariable Cox Model
plot_cox_survival(): Model-Predicted Survival Curves Stratified by Predictor
plot_cox_marginal(): Marginal Event Probability / Relative Hazard Diagnostic Plot
cbe_theme_survival(): CBE ggplot2 Theme for Survival Analysis Visualizations
tidy_tmerge_cox(): Tidy Construction of Counting-Process (Start-Stop) Survival Data

Presentation Deck & Clinical Summary Plots

Presentation-ready Kaplan-Meier curves, longitudinal biomarker trajectories, group comparisons, data quality audits, and 2x2 contingency tables.

plot_survival_km(): Standardized Kaplan-Meier Survival Curve
plot_dynamic_trajectory(): Longitudinal Biomarker Trajectories by Cohort / Outcome
plot_group_comparison(): Group Comparison Bar Chart with Standard Errors
plot_missingness(): Missing Data Audit Plot
table_two_by_two(): Formatted 2x2 Contingency Table with Exact Test
tbl_two_sample_compare(): Two-Group Comparison Table for Continuous Variables
tbl_table_one(): Stratified Baseline "Table 1"
calc_grouped_summary(): Compact Groupwise Numeric Summary
tbl_feature_importance_dalex(): Table of DALEX Permutation Feature Importance
plot_imputed_vs_observed(): Observed Versus Imputed Values, per Cross-Validation Fold
plot_comissingness_matrix(): Co-Missingness Correlation Matrix
tbl_missingness_patterns(): Missingness Patterns Table
plot_marginal_effect_contrast(): Forest Plot Contrasting the Raw and the Normalized Effect Scale
tbl_contingency_2x2(): Publication-Ready 2x2 Contingency Table with Tests and Risk Measures
classify_quadrants(): Assign Observations to Bivariate Quadrants
tbl_quadrant_distribution(): Distribution of Clinical Strata Across Quadrants
quadrant_n(): One Quadrant Count for Inline Prose
tbl_paired_assay_agreement(): Paired Assay Agreement Table (Bias, Limits of Agreement, Lin's CCC)
tbl_correlated_pairs(): Table of Highly Correlated Feature Pairs
plot_imputation_hazard_stability(): Compare Bootstrap Hazard Ratios Between Complete and Imputed Data
tbl_tuning_by_quantile(): Tuning Results Summarised by Performance Quantile Tier
tbl_longitudinal_cox_screen(): Cox Screen of Longitudinal Summary Statistics
plot_trajectory_group_ci(): Group Trajectories with Empirical Quantile Ribbons
tbl_landmark_cutpoints(): Survival Cutpoints at Several Landmark Times
tbl_schoenfeld_audit(): Proportional-Hazards Audit of a Cox Model
tbl_cumulative_incidence(): Cumulative Incidence with Competing Risks
plot_cumulative_incidence(): Plot Cumulative Incidence Curves
glance_imputations(): Pool Model-Level Fit Statistics over Imputations
pool_imputations(): Pool Estimates from Several Imputations with Rubin's Rules
tbl_fine_gray_single(): Fine-Gray Subdistribution Hazard Regression for One Predictor
tbl_fine_gray_multi(): Multivariable Fine-Gray Subdistribution Hazard Regression
tbl_fine_gray_screen(): Screen Many Predictors with Univariable Fine-Gray Models
tbl_domain_correlations(): Correlations Within and Between Feature Domains
batch_glm_single() batch_lmm_single(): Univariable GLM / LMM Screens Over Inputs and Outputs
compute_longitudinal_composite_index(): Baseline-Anchored Longitudinal Composite Index
cut_with_missing_category(): Quantile Bins with an Explicit Missing Level
tbl_cox_binned_missing(): Cox Model on Quantile Bins with an Explicit Missing Level
sample_weighted_formulas(): Candidate Formulas Weighted by Feature Importance
adaptive_cox_formulas(): Adaptive Candidate Formulas from Top Models
expand_feature_formulas(): All Formulas with k Terms

Categorical Association & Contingency Tables

Biostatistical testing, exact 2x2 inference, 4-quadrant reporting, and publication-ready association visualizations (balloon, bar, contingency, mosaic, heatmap, and square plots).

exact2x2(): Exact Test for 2x2 Tables with Automatic Zero-Cell Mid-p Default
cbe_exact2x2_ci() exact2x2_ci(): Format Exact 2x2 Odds Ratio and Confidence Interval
test_categorical(): Institutional Categorical Hypothesis Test for gtsummary and Batch Testing
cbe_four_quadrant_report(): Standard 4-Quadrant Clinical Contingency Report
cbe_contingency_plot() contingency_plot() balloon_plot() bar_plot() mosaic_plot() heatmap_plot() four_quadrant_report() square_plot(): Visualizations for 2-Way Contingency Tables and Categorical Tests
cbe_balloon_plot(): Balloon Plot for 2-Way Contingency Tables
cbe_bar_plot(): Bar Chart for 2-Way Contingency Tables
cbe_mosaic_plot(): Mosaic Plot for 2-Way Contingency Tables
cbe_heatmap_plot(): Heatmap Plot for 2-Way Contingency Tables
cbe_square_plot(): Square Plot: Standard 4-Quadrant Contingency Report
pairwise_combos(): Generate All Pairwise Categorical Combinations
plot_categorical_associations(): Plot Categorical Association Matrix Using corrplot

Data Snapshot Manifest & Schema Engine

Tools to freeze, snapshot, and verify dataset copies against upstream sources via MD5 hashes, enforce schema mapping dictionaries, and simulate synthetic patient cohorts.

read_data_manifest(): Read Data Manifest File
copy_data_manifest(): Copy Datasets to Snapshot Directory
validate_data_manifest(): Validate Data Snapshot Copies Against Upstream Sources
stop_if_invalid_manifest(): Stop Execution if Data Copies are Invalid or Stale
validate_column_mapping(): Validate a Plug-and-Play Column Mapping
find_section_file(): Find Section Data File in a Directory
read_raw_table(): Read Raw Table with Multi-Row Header Support
read_mapped_section_data(): Read One Section's Data Using a Column Mapping
summarize_section_by_time(): Summarize One Section By Its Time Variable
demo_cbe_mapping(): Create Sample Plug-and-Play Mapping Dictionary
simulate_section_data(): Simulate Synthetic Section Data From a Schema Mapping

Deliverable Packaging & Code Auditing

Package pipeline outputs (PDF/DOCX/HTML reports and data deliverables) into structured distribution archives, audit source scripts for referenced deliverable tokens, and extract YAML metadata formats.

package_deliverables(): Package Pipeline Deliverables into a Structured Zip Archive
diff_deliverables(): Compare Two Deliverable Bundles and Diff DOCX Reports
audit_report_deliverables(): Audit Deliverable File Tokens in Analysis Scripts
audit_render_parity(): Audit a Rendered PDF Against the Word Version of the Same Report
extract_yaml_formats(): Extract Output Formats From Document YAML Front Matter
docx_review_extract(): DOCX Review Extractor & Multi-Reviewer Collaborative Tracker
extract_docx_qa(): Extract Questions, Answers and Cited Reports From a Word Document
review_tracker(): Build a Structured Review Response Tracker Requiring Location and Rationale
validate_review_tracker(): Validate a Review Response Tracker for Required Location and Rationale
review_config(): Get or set global multi-reviewer review-tracking configuration

Example Datasets

Clinical oncology and time-to-event datasets for testing and vignette demonstrations.

tumor_long(): Convert Wide Tumor Data to Counting Process (Start/Stop) Format
tumor_wide(): Internal SAS Benchmark Tumor Dataset (Wide Format)

Helper & Infix Operators

Clean column naming, string matching, and vector manipulation.

clean_names_keep_labels(): Clean Column Names, Preserving Originals as Labels
make_excel_names(): Generate Excel-Compatible Column Names
excel_colnames(): Sanitize Column Names for Excel Worksheets
pretty_name(): Convert Raw Variable Names to Human-Readable Display Labels
copy_var_labels(): Copy Variable Labels and Attributes Across Data Frames
cbe_labels(): Set or Retrieve the Package-Level Variable Label Map
tbl_label_map() cbe_label_table(): Format a Variable Label Map Table for Report Appendices
label_helpers: Variable Label and Excel Header Helpers
rm_all_except(): Keep Only Specified Objects in an Environment
delete_nul_files(): Delete Stray 'nul' Files
find_code(): Search for Code Patterns Across a Directory Tree
std_error(): Compute standard error
profvis_summary(): Summarize a `profvis` Profile
normalize_path_safely(): Normalize File Paths Without Failing
parse_here_call_vec(): Resolve `here::here()` Calls Found in Code Text
file_meta_fs(): File Metadata as a Tibble
extract_xlsx_paths(): Extract Full `.xlsx` Paths From Text
extract_all_xlsx_tokens(): Extract Every `.xlsx` File Name From Text
install_package_no_lock(): Install a Package, Bypassing an Existing Lock
reexports %>%: Objects exported from other packages
match_like() ilike() `%ilike%` flike() `%flike%` plike() `%plike%` notin() `%!in%`: Pattern Matching and Logical-Negation Infix Operators

Package Development & Release Tooling

Maintainer-facing helpers for package release and documentation artifacts.

build_manual_versioned(): Build a Versioned PDF Reference Manual
clean_publish(): Publish a Clean Snapshot of the Current Commit to a Remote
guard_public_remote(): Guard a Public Remote Against Accidental Direct Push
find_deprecated_calls() update_deprecated_calls(): Find and Update Deprecated Calls Across Project Code