# Censored Regression

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- **Type:** Skill
- **Install:** `agentstack add skill-jskherman-engg-skills-censored-regression`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [jskherman](https://agentstack.voostack.com/s/jskherman)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** Apache-2.0
- **Upstream author:** [jskherman](https://github.com/jskherman)
- **Source:** https://github.com/jskherman/engg-skills/tree/main/skills/censored-regression

## Install

```sh
agentstack add skill-jskherman-engg-skills-censored-regression
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Censored Regression (Censored Normal / Censored Lognormal)

## Overview

Many process lab measurements (H2S, COS, mercaptans, DMS, disulfides,
unknown sulfur, trace metals) are reported as below-LOD, below-LOQ, above an
instrument range, or within an interval. Ordinary least squares on
`log(S + epsilon)` is biased and understates uncertainty; the correct treatment
is a likelihood-based censored regression.

This skill provides:

- A censored-normal regression on the log scale by default (censored lognormal
  on the original response scale).
- A censored-normal regression on the raw scale with `--no-log`.
- Left, right, and interval censoring.
- Maximum-likelihood fitting through SciPy.

The implementation uses a hand-rolled SciPy likelihood. It does not calculate
standard errors, robust covariance estimates, or the naive substitution fit.
Use bootstrap/profile-likelihood checks externally when inference quality matters.

## Prerequisites

1. `uv` available.
2. The script declares `numpy`, `scipy`, and `pandas` in its PEP-723 header.
3. On first use, writes `LICENSE_NOTIFICATION.txt`.

## When to Use

- Lab data has below-LOQ rows that you cannot drop without introducing
  selection bias.
- Reported values include intervals, for example between LOD and LOQ.
- Fitting a regression of `log(species)` on operating variables, where the
  species is sometimes censored.

## Don't use for

- Fully uncensored regression: use `engineering-statistics`.
- Time-to-event survival analysis: use a survival-analysis package.
- Compositional response data (sulfur speciation fractions): combine with
  `compositional-data-analysis`.

## Utility Scripts

- `uv run scripts/censored.py fit --data data.csv --response S_total --predictors temperature,c5_c6plus_balance --lower-col LOQ --output /tmp/fit.json`
- `uv run scripts/censored.py interval --data data.csv --response S_total --predictors temperature --lower-col LOD --upper-col LOQ --output /tmp/int.json`

Input CSV layout:

- One row per observation.
- `response` column with the measured value when fully observed; NaN or blank
  when censored.
- `lower-col` and/or `upper-col` columns with the censoring bound for censored
  rows.
- Predictor columns referenced by name in `--predictors`.

Censoring-column semantics used by the script:

- finite `response`: exact observation; bounds ignored.
- missing `response` + only `lower-col` finite: left-censored, meaning
  `y = upper_col` (above-range row).
- missing `response` + both bounds finite: interval-censored, meaning
  `lower_col  80%), report only the censoring fraction
  and rank/order conclusions; full regression coefficients are unstable.
- If the row order is a time series, use block bootstrap or a dynamic model for
  uncertainty rather than i.i.d. inference.

## Verification

- Run the listed script with representative inputs and an `--output` file when a deterministic calculation is available.
- Confirm the JSON result contains `ok: true`, expected units, and no unhandled warnings.
- Check result magnitudes against the stated assumptions, references, and a hand calculation or known operating range before reporting them.

## References

- `references/censored_likelihood.md` — likelihood derivation.
- Greene, *Econometric Analysis*, chapter on truncated/censored models.
- Helsel, *Statistics for Censored Environmental Data Using MINITAB and R*.

## Anti-Patterns

- Hiding the censoring fraction in the report.
- Reporting a censored-lognormal coefficient as if it were on the original
  response scale; by default, the response model is on log scale.
- Using ordinary bootstrap on autocorrelated censored data without preserving
  time dependence.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [jskherman](https://github.com/jskherman)
- **Source:** [jskherman/engg-skills](https://github.com/jskherman/engg-skills)
- **License:** Apache-2.0

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-jskherman-engg-skills-censored-regression
- Seller: https://agentstack.voostack.com/s/jskherman
- Browse the marketplace: https://agentstack.voostack.com/browse

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