Package index • SciDataReportR

All functions

AddToCodebook(): Add a new variable to a codebook
ApplyFDRCorrection(): Apply multiple-comparison correction across a p-value matrix
ApplyNormativeTScores(): Apply a normative T-score model to new data
AssemblePlots(): Assemble ggplot objects into a unified multi-panel figure
CodebookMergeApp(): Interactive codebook harmonization dashboard
CombineCodebooks(): Combine Two Codebooks with Conflict Detection
CompareDatasets(): Compare two versions of a dataset
ConvertOrdinalToNumeric(): Prepare ordinal variables for analysis
CreateClusterModel_Gower_PAM(): Fit a projectable Gower-distance PAM model for mixed clinical data
CreateClusterModel_HDBSCAN(): Fit a projectable HDBSCAN model
CreateClusterModel_KMeans(): Fit a projectable K-means clustering model
CreateClusterModel_LatentClass(): Fit a projectable latent class model for categorical measures
CreateClusterModel_MCA_MClust(): Fit MCA followed by Mclust for nominal categorical data
CreateClusterModel_MClust(): Fit a projectable Gaussian-mixture clustering model
CreateClusterModel_PCA_KMeans(): Fit PCA followed by K-means
CreateClusterModel_PCA_MClust(): Fit PCA followed by Mclust
CreateClusterModel_SOM_HDBSCAN(): Fit HDBSCAN clusters on a frozen self-organizing map
CreateClusterModel_SOM_MClust() Pipeline_SOM_MClust() Pipeline_SOMClust() CreateSOMClusterModel(): SOM + latent profile clustering pipeline (with AHP and distance baselines)
CreateMCAObject() CreateMCATable(): Create a reusable MCA object and visualizations
CreateMScoreObject() CalcMScore(): Calculate robust M-scores for numeric variables
CreateNormativeTScoreModel() CreateNormativeTScores(): Create normative T-scores from a regression model
CreatePCAObject() CreatePCATable(): Create a reusable PCA object and visualizations
CreateProjectFolders(): Create Project Folder Structure
CreateRCIObject(): Create a Reliable Change Index (RCI) object
CreateStatisticsTable(): Create Statistics Table
CreateSummaryTable(): Create Summary Table
CreateVariableTypesTemplate(): Create a Template for Variable Types
CreateZScoreObject() CalcZScore(): Calculate Z-scores (or standardized scores) and return data + parameters
DeriveFreesurferVolumes(): Derive Freesurfer bilateral measures and optional ICV-adjusted ratios
EvaluateBiomarkerPerformance(): Evaluate biomarker performance
ExploreDatasetComparison(): Explore dataset comparison results interactively
ExploreMergeValidation(): Explore merge validation results interactively
ExtractPCAComponentSummary(): Extract PCA component summaries
FormattedDataDictionary(): Create a formatted data dictionary table
FreezeTableHeader(): Freeze the header row of a long table when scrolling
IQROutliers(): Detect outliers using the Tukey IQR rule and visualize results
InsertValues(): Insert Values into a String Array
InspectCategoricalSummary(): Inspect categorical variables
InspectFile(): Inspect a scientific data file before import
KeepEnv(): Keep selected objects in an environment and remove everything else
MakeComparisonTable(): Make comparison table with covariate adjustment, effect sizes, and pairwise contrasts
MakeDataDictionary() Make_DataDictionary(): Create a data dictionary for a data frame
MakeFacetCatComparisonTable(): Create a merged gtsummary table by faceting comparisons across multiple categorical variables
MakePairwiseHeatmap(): Make a pairwise referent heatmap
MakeTable1(): Create Summary Table using gtsummary
MakeUnivariateRegressionTable() UnivariateRegressionTable(): Univariate Regression Table
MergeCodebooks(): Merge multiple codebooks using harmonization rules
MergeFragmentedRecords(): Merge fragmented records into a single observation
Merge_ByClosestTime(): Merge Two Data Frames by Closest Time
MultivariableRegressionTable(): Multivariable regression table
Plot2GroupStats(): Plot & Summarize Group Stats via MakeComparisonTable (BH q from p; SHAPE by p; COLOR by Category (vector or data frame); stable point size; palette via paletteer)
PlotAnovaRelationshipsMatrix(): Plot ANOVA Relationships Matrix
PlotAssociations(): Plot Associations
PlotBlandAltman(): Plot Bland-Altman Agreement Plot
PlotCatInteractionEffectsMatrix(): Plot Categorical Interaction Effects Matrix
PlotCategoricalDistributions(): Plot categorical distributions
PlotChiSqCovar(): Plot Chi-Square Tests for Categorical Associations (optionally stratified by covariates)
PlotClusterBoxplot(): Plot cluster boxplots by variable
PlotClusterCentreHeatmap() PlotClusterCentreProfile(): Plot cluster centre profiles as a heatmap
PlotClusterComposition(): Plot categorical composition by cluster
PlotClusterDiagnostic(): Plot a per-cluster diagnostic value
PlotClusterFitReview(): Plot cluster fit-review metrics
PlotClusterMap() PlotClusterAssignment(): Plot a two-dimensional cluster review map
PlotClusterProfiles(): Plot labelled numeric profiles by cluster
PlotClusterSilhouette(): Plot a per-participant silhouette profile
PlotContinuousDistributions(): Plot Continuous Distributions
PlotCorrelationsHeatmap(): Plot correlations heatmap
PlotDatasetComparison(): Plot dataset comparison diagnostics
PlotDirectionalHeatmaps(): Create directional heatmaps across continuous & binary variables
PlotForestFromTable() plotForestFromTable(): Create a Forest Plot from Univariate Regression Tables
PlotInteractionEffectsContinuous(): Plot Single Interaction Effect
PlotInteractionEffectsMatrix(): Plot Interaction Effects Matrix
PlotMergeValidation(): Plot merge validation diagnostics
PlotMiningMatrix(): PlotMiningMatrix
PlotMissingData(): Plot Missing Data
PlotNumInteractionEffectsMatrix(): Plot Numerical Interaction Effects Matrix
PlotPValueComparisons(): Plot P-Value Comparisons
PlotPartialRegressionScatter(): Partial Regression Plot
PlotPathway_KT() CreatePathwayPlot_KT(): Plot the kynurenine-tryptophan pathway
PlotPhiHeatmap(): Plot Phi Correlations Between Binary Variables
PlotPointCorrelationsHeatmap(): Plot Point-Biserial Correlations Between Binary and Continuous Variables
PlotSpiderChart(): Plot a spider chart across continuous and binary variables
PlotSplitViolin(): Split violin with aligned half-boxplots, significance label, sample sizes, and label-aware title
PlotSwimmerTransitions(): Plot swimmer-style transitions for a binary condition over repeated visits
PlotTimeDistribution(): Plot Time Distribution
PlotTimeSwimmer(): Plot longitudinal swimmer timelines
PlotVolcanoEffects(): Plot volcano-style association effects
PlotZScore() CreateZScorePlot(): Plot Z-score group differences with statistical significance
PrepNumericData(): Prepare numeric data safely for analysis
PrepSPSS(): Prepare a data frame for SPSS export
ProjectCluster() Project_SOMClust() ProjectSOMCluster(): Project cases through a fitted clustering model
ProjectPCA(): Project PCA scores onto new data
ProjectRCI(): Project a trained RCI object onto new data
ProjectZScore() Project_ZScore(): Project standardized scores onto new data using external parameters
ReValueFactors(): Revalue Factors
ReadSciData(): Read a scientific data file with optional inspection
ReplaceMissingCode(): Replace Missing Codes with NA
ReplaceMissingLabels(): Replace Missing Labels in Dataframe Columns
RevalueData(): Revalue Data
SampleData: SampleData for practicing SciDataReportR functions
SampleVariableTypes: Example Dataset: SampleVariableTypes
SciDataPalette(): SciDataReportR qualitative color palette
ScreenBiomarkerPerformance(): Screen biomarker performance across outcomes
SimulatedPhenotypeData: Neutral simulated clustering and phenotyping benchmark
SummarizeTransitions(): Summarize participant transitions for a binary longitudinal condition
UpdateCodebook(): Update an existing codebook based on a given dataframe
UpdateDataDictionary(): Update an existing data dictionary with new variables and types
ValidateMerge(): Validate a merge between two source data frames and a merged result
add_biomarker_values(): Add values to a biomarker performance heatmap
add_r_and_stars(): Add r-values and significance stars to a correlations heatmap
calculate_pathway_results(): Calculate Pathway Results for Metabolite Comparisons
createBinaryMapping(): Create a Mapping Table for Binary Variables
createFacetLabels(): Create facet labels for ggplot2 based on variable labels in a data frame
geom_starcaption(): Add a Caption Explaining Star Annotations
getBinaryVars(): Identify Binary Variables
getCatVars(): Get Categorical Variables
getNumVars(): Get Numeric Variables
merge_detail(): Print a plain-text detail report for one safe_merge result
merge_summary_table(): Combine safe_merge logs into a single summary table
`%!in%` `%notin%`: Negated "in" Operator
plotPCA(): Plot PCA scores
plotSigAssociations(): Plot Significant Associations
plotSigCorrelations(): Plot Significant Correlations
removeString(): Remove Strings from a Vector
reverseFactorLevels(): Reverse Levels of Categorical Factors
safe_merge(): Safely merge two data frames with relationship-aware validation
scale_color_SciData(): SciDataReportR discrete color scale
scale_color_pvalue(): Apply an evidence-aware p-value color scale
scale_fill_SciData(): SciDataReportR discrete fill scale
scale_fill_pvalue(): Apply an evidence-aware p-value fill scale
use_EDATemplate(): Use the EDATemplate Quarto Template
use_GetStartedScript(): Use the Get Started Script Template
windsorize(): Winsorize a numeric vector using SD or IQR thresholds