## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----basic, eval = FALSE------------------------------------------------------
# library(causalfrag)
# 
# # 1. Fit your model
# fit <- lm(mpg ~ am + wt + hp, data = mtcars)
# 
# # 2. Run the full sensitivity pipeline
# result <- sens_report(
#   model     = fit,
#   treatment = "am",
#   data      = mtcars
# )
# 
# # 3. Print the structured results + fragility flag
# print(result)
# 
# # 4. Access the plain-language narrative
# cat(result$narrative)

## ----stepwise, eval = FALSE---------------------------------------------------
# library(causalfrag)
# 
# fit <- lm(mpg ~ am + wt + hp, data = mtcars)
# 
# # Step 1: detect the design
# design <- detect_design(fit)
# 
# # Step 2: run sensitivity analysis
# res <- run_sensitivity(fit, treatment = "am", data = mtcars, design = design)
# 
# # Step 3: flag fragility
# res <- flag_fragility(res)
# 
# # Step 4: interpret (uses LLM if configured, template otherwise)
# res <- interpret_sensitivity(res)
# 
# # Step 5: visualize (uses confoundvis if installed)
# plot(res)
# 
# # Step 6: generate a report paragraph
# cat(generate_report(res))

## ----llm, eval = FALSE--------------------------------------------------------
# # Anthropic (key from ANTHROPIC_API_KEY environment variable)
# use_llm_provider("anthropic")
# 
# # OpenAI
# use_llm_provider("openai")
# 
# # Disable explicitly
# use_llm_provider("none")

## ----confoundvis, eval = FALSE------------------------------------------------
# # Visualize with confoundvis (if installed)
# visualize_sensitivity(res, engine = "confoundvis")

