r4subscore

r4subscore is the scoring and calibration engine of the R4SUB ecosystem.

It converts standardized evidence (from r4subcore and companion packages like r4subtrace) into:

Indicator Scores (per-indicator 0–1)
Pillar Scores (per-domain 0–1)
Submission Confidence Index (SCI, 0–100)
Decision Bands (Ready / Minor Gaps / Conditional / High Risk)
Sensitivity Analysis (SCI stability under weight variations)
Explainability Tables (which indicators drive SCI up/down)

It answers the executive question:

Are we ready for regulatory submission – and how confident are we?

Installation

pak::pak(c("R4SUB/r4subcore", "R4SUB/r4subscore"))

Quick Start

library(r4subcore)
library(r4subscore)

# assume ev is a validated evidence table
pillar_scores <- compute_pillar_scores(ev)
sci <- compute_sci(pillar_scores)

sci$SCI
sci$band

Core Functions

Function	Purpose
`sci_config_default()`	Pillar weights + decision bands config
`classify_band()`	Classify an SCI value into a decision band
`compute_indicator_scores()`	Severity-weighted indicator-level scores
`compute_pillar_scores()`	Aggregate indicators into pillar scores
`compute_sci()`	Compute SCI (0–100) + band classification
`sci_sensitivity_analysis()`	SCI under alternative weight scenarios
`sci_explain()`	Top loss contributors + pillar breakdown

SCI Bands

SCI	Band	Interpretation
85–100	`ready`	Ready for Submission
70–84	`minor_gaps`	Minor Gaps to Address
50–69	`conditional`	Conditional – Address Key Issues
0–49	`high_risk`	High Risk

Scoring Logic

Each evidence row gets a weighted score: result_score * (1 - severity_weight)
Indicator scores = mean weighted score per indicator
Pillar scores = mean indicator score per domain
SCI = weighted sum of pillar scores x 100

License

MIT