# v1.5.1

`v1.5.1` packages a more scientific public SDK workflow: stronger study tooling, clearer protocol bundles, and a more reproducible evidence path.

## Highlights

- Added a scientific workflow layer for hypothesis-driven studies:
  - `iints study-protocol`
  - `docs/SCIENTIFIC_WORKFLOW.md`
  - protocol bundles with `STUDY_PROTOCOL.md`, `study_design.json`, and `study_matrix.csv`
- Added controlled corruption tooling for clean-vs-corrupted experiments:
  - `iints data corrupt-for-study`
  - timestamp shifts, missing blocks, duplicated rows, glucose spikes, dropped meal annotations, and unit-scale errors
- Added a fixed study-preset path for repeatable benchmarking
- Extended study analysis:
  - descriptive statistics with standard deviation and 95% confidence intervals
  - failure analysis for severe hypo, early terminations, and safety-heavy runs
  - optional external plausibility comparison against imported CareLink metrics
- Extended study comparison:
  - confidence intervals on differences in means
  - Cohen's d effect estimates for key metrics
- Improved study posters:
  - failure-analysis visibility
  - external plausibility verdicts

## New Scientific Commands

```bash
iints scenarios export-study-pack --output-dir scenarios/study_pack

iints study-protocol --output-dir results/study_protocol

iints data corrupt-for-study data/demo/diabetes_cgm.csv \
  --output-csv data/demo/diabetes_cgm_corrupted.csv \
  --mode timestamp_shift \
  --mode missing_block \
  --mode glucose_spikes

iints analyze results/study \
  --output-json results/study_summary.json \
  --output-markdown results/study_summary.md \
  --output-csv results/evidence_table.csv \
  --output-evidence-markdown results/evidence_table.md \
  --carelink-metrics results/personal_carelink/carelink_metrics.json

iints compare-study results/study_clean results/study_corrupted \
  --output-json results/study_comparison.json \
  --output-markdown results/study_comparison.md

iints run-study \
  --algo algorithms/example_algorithm.py \
  --output-dir results/study_bundle
```

## Why This Release Matters

Before `v1.5.1`, the SDK already had simulation, certification, AI review, and poster tooling, but the scientific story still depended too much on manually assembled study folders and ad-hoc experiment design.

With `v1.5.1`, the SDK can now:

- define the study before running it
- document the intended corruption operators
- keep a fixed clean/corrupted/supervisor-off matrix
- quantify uncertainty and effect size
- compare simulation behavior against imported real-world glucose metrics

That makes the project much easier to defend in front of a science jury, a public audience, or a technical reviewer.

## Installation

Latest release:

```bash
python -m pip install -U "iints-sdk-python35[mdmp]"
```

Pinned:

```bash
python -m pip install -U "iints-sdk-python35[mdmp]==1.5.1"
```
