Function Index & S3 Class System Breakdown

foundr organizes its source code across 45+ source files in R/. Functions are grouped into five core architectural categories:

1. Data Analysis & Statistical Modeling

Function	Primary Role / Description	Source File
`partition()`	Decomposes raw trait values into orthogonal `signal`, `rest`, and `noise` terms	`R/partition.R`
`strainstats()`	Computes strain/sex/condition summary stats, standard deviations, cell means, and ANOVA F-tests	`R/strainstats.R`
`conditionContrasts()`	Calculates differences and contrasts between experimental conditions across founder strains	`R/conditionContrasts.R`
`bestcor()`	Identifies top correlated traits across founder strains using Spearman rank correlation	`R/bestcor.R`
`eigen_cor()` / `eigen_contrast()`	Computes eigentrait correlations and condition contrast eigentrait projections	`R/eigentrait.R`
`traitAncova()`	Performs Analysis of Covariance (ANCOVA) combining continuous covariates and strain factors	`R/traitAncova.R`

2. Trait S3 Class System & Method Dispatch

foundr implements custom S3 classes to encapsulate structured trait data and provide unified visualization & summary APIs.

S3 Classes

traitSolos: Represents single-trait data structured for strain comparisons (R/traitSolos.R).
traitPairs: Encapsulates bivariate trait comparisons, joint distributions, and correlations (R/traitPairs.R).
traitTimes: Wraps time-series measurement trajectories over time points (R/traitTimes.R).
conditionContrasts: Encapsulates condition contrast effect estimates and p-values (R/conditionContrasts.R).

Standardized S3 Generics

Each S3 trait class supports standard S3 generics: - autoplot() / plot(): Generate ggplot2 visual representations. - summary(): Produce numerical summaries and strain contrast tables. - subset() / split() / c(): Manipulate, slice, or combine trait objects seamlessly.

3. Module Analysis & WGCNA Integration

foundr integrates with WGCNA module outputs to analyze co-expression gene modules alongside physiological phenotypes:

module_kMEs(): Extracts module eigengene kME values for specified traits (R/module_kMEs.R).
module_band(): Computes module band intervals across founder strain trait signals (R/module_band.R).
wgcnaModules() / listof_wgcnaModules(): Constructors for managing single and list-of-WGCNA module specifications (R/wgcnaModules.R).

4. Visualization & Biplot Engine

All plot generators in foundr build upon the centralized ggplot_template() design system:

Plot Function	Description	Source File
`ggplot_template()`	Core styling template (Okabe-Ito CC color palette, custom themes, facet options)	`R/ggplot_template.R`
`ggplot_traitSolos()`	Bar charts and point plots for single trait strain values	`R/traitSolos.R`
`ggplot_traitPairs()`	Scatter plots and correlation grids for paired traits	`R/traitPairs.R`
`ggplot_traitTimes()`	Longitudinal trajectory plots across experimental time points	`R/traitTimes.R`
`volcano()`	Volcano plots mapping effect size against statistical significance ( $-\log_{10}(p)$ )	`R/volcano.R`
`biplot_signal()` / `biplot_pca()`	PCA biplot visualizations powered by the `ordr` package	`R/biplot.R`
`ggplot_evidence_spread()`	Evidence spread and rank variance dot plots	`R/evidence_spread.R`

5. Utilities & Data Transformation Helpers

nqrank() / normalscores(): Normal quantile rank transformations for non-Gaussian trait normalization (R/nqrank.R).
area_under_curve(): Integrates trait values across longitudinal time points (R/area_under_curve.R).
unite_datatraits() / pivot_pair(): Reshaping helpers for combining dataset names and trait identifiers (R/trait_names.R).
timetraits() / separate_time(): Utilities for parsing time component tags from trait string names (R/timetraits.R).