The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.