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Penalized DLNM Framework

Fit penalized DLNMs using R's mgcv::gam() with cbPen().

Updated Mar 7, 2026
Search Tags:#r#gam#smoothing#dlnm#penalized#mgcv
One-click install
npx skills add https://github.com/ntluong95/agent-skills-statistics --skill penalized-dlnm-framework
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Penalized DLNM Framework
Source: https://github.com/ntluong95/agent-skills-statistics/tree/main/skills/dlnm/penalized-dlnm
Command: npx skills add https://github.com/ntluong95/agent-skills-statistics --skill penalized-dlnm-framework

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of selecting appropriate smoothing parameters for Distributed Lag Non-Linear Models (DLNMs) by automating the process using penalized splines and Generalized Additive Models (GAMs).

Core Features & Use Cases

  • Automatic Smoothing: Leverages mgcv::gam() and cbPen() to estimate optimal smoothing parameters from data, removing the need for manual selection.
  • Flexible Model Fitting: Integrates penalized cross-basis terms with other smooth terms (e.g., s(date)) within a GAM framework.
  • Use Case: When exploring exposure-lag-response relationships where the optimal degree of smoothness is uncertain, this Skill provides a data-driven approach to model fitting, ensuring more robust and reproducible results.

Quick Start

Use the Penalized DLNM Framework skill to fit a GAM model with penalized cross-basis terms for exposure and date.

Frequently Asked Questions about Penalized DLNM Framework

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate smoothing parameter selection for DLNMs in R?▼

Automate DLNM smoothing parameter selection by fitting penalized splines with mgcv::gam() and the cbPen() utility, which estimates optimal smoothness directly from data without manual tuning.

What is a penalized Distributed Lag Non-Linear Model and when do I need it?▼

A penalized DLNM integrates data-driven smoothness estimation into exposure-lag-response analyses, needed when exploring complex delayed effects where the optimal degree of smoothness is uncertain.

Can I integrate penalized DLNM cross-basis terms with other GAM smooth terms?▼

Yes, you can integrate penalized cross-basis terms with other GAM smooth terms like s(date) within a unified mgcv framework for flexible model fitting.

What R packages do I need to fit penalized DLNMs with mgcv?▼

To fit penalized DLNMs with mgcv, you need the R environment along with the dlnm and mgcv packages installed for implementing the cross-basis and penalized spline functions.

Why use penalized splines for DLNM instead of manual smoothing parameter selection?▼

Penalized splines remove the need for manual smoothing parameter selection by estimating optimal values directly from data, yielding more robust and reproducible exposure-lag-response results.

What are the limitations of using mgcv GAMs for DLNM exposure-lag-response modeling?▼

Limitations include dependency on R with dlnm and mgcv packages, and potential complexity when blending cross-basis penalized terms with other GAM smooth terms requiring careful integration.

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