Add Stan Support and netmeta-style Advanced Plotting Features

[choxos]

Overview

This PR adds two major feature sets to BUGSnet:

1. Full Stan (cmdstanr) support alongside JAGS for all model types
2. netmeta-inspired advanced plotting for evidence splitting, heat plots, and bias detection

All features are fully tested with both JAGS and Stan backends and maintain 100% backward compatibility.

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🆕 New Features

1. Stan Support via cmdstanr

New function: nma.run.stan() - Run NMA using Stan instead of JAGS

20 Stan model files in inst/stan/ covering all combinations:

• Families: binomial, normal, poisson
• Links: logit, log, cloglog, identity
• Effects: fixed, random
• Types: consistency, inconsistency
• Full meta-regression support: UNRELATED, EXCHANGEABLE, EQUAL priors

Benefits:

• ⚡ HMC sampling often more efficient than Gibbs
• 📊 Superior diagnostics (divergences, R-hat, ESS)
• 🔄 Easy parallel chain execution
• ✅ 100% backward compatible - all existing functions work with Stan results

Example:

# Works exactly like nma.run() but uses Stan
results.stan <- nma.run.stan(model, 
                             iter_warmup = 1000,
                             iter_sampling = 2000,
                             chains = 4)

# All downstream functions work identically
nma.league(results.stan)
nma.rank(results.stan)
nma.forest(results.stan)

2. Advanced Plotting (netmeta-style)

Five new functions inspired by the netmeta package:

📊 nma.netsplit()

Splits network estimates into direct and indirect evidence

• Tests for local inconsistency (z-tests, p-values)
• Back-calculation method following Dias et al. (2010)
• Shows proportion of direct evidence for each comparison

🌲 forest.nma.netsplit()

S3 method for forest plots of split evidence

• Displays network, direct, and indirect estimates side-by-side
• ggplot2-based with full customization
• Filtering options: "all", "with.direct", "both"

🔥 nma.heatplot()

Color-coded league tables

• Gradient shows treatment effect magnitude
• Works with SUCRA rankings for optimal display
• Customizable colors and ordering

📈 nma.funnel()

Comparison-adjusted funnel plots

• Detects publication bias and small-study effects
• Follows Chaimani & Salanti (2012) methodology
• Adjusts for different treatment comparisons

🎯 nma.radial()

Comparison-adjusted radial (Galbraith) plots

• Alternative visualization for bias detection
• Better for studies with similar precision

All plotting functions:

• ✅ Work with both JAGS and Stan results
• ✅ Support meta-regression via cov.value
• ✅ Integrate with existing functions like nma.rank()

Example workflow:

# Evidence splitting
split <- nma.netsplit(results)
print(split)
forest(split, show = "with.direct")

# Heat plot with SUCRA ordering
ranks <- nma.rank(results, largerbetter = FALSE)
nma.heatplot(results, order = ranks$order)

# Assess publication bias
trt_order <- c("Placebo", "Drug_A", "Drug_B", ...)
nma.funnel(results, order = trt_order)
nma.radial(results, order = trt_order)

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📚 Documentation

New Vignette: advanced-plotting.Rmd

Comprehensive guide with 10 sections:

1. Introduction to advanced plotting
2. Network evidence splitting
3. Forest plots for split evidence
4. Heat plots (color-coded league tables)
5. Comparison-adjusted funnel plots
6. Comparison-adjusted radial plots
7. Treatment rankings (SUCRA)
8. Meta-regression support
9. Complete workflow example
10. Tips and best practices

Updated Vignette: using_stan.Rmd

• cmdstanr installation instructions
• Stan-specific parameters and tuning
• Diagnostic interpretation
• Advanced plotting integration
• JAGS vs Stan comparison

Updated References

Added key citations:

• Dias et al. (2010) - Consistency checking in NMA
• König et al. (2013) - Evidence flow visualization
• Chaimani & Salanti (2012) - Comparison-adjusted plots
• Salanti et al. (2011) - SUCRA and rankograms
• netmeta package

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🔧 Technical Details

Files Changed

Created (23 files):

• 20 Stan model files (inst/stan/*.stan)
• 5 new R functions (R/nma.netsplit.R, R/forest.nma.netsplit.R, etc.)
• 1 new vignette (vignettes/advanced-plotting.Rmd)

Modified (6 files):

• DESCRIPTION - Added cmdstanr and posterior to Suggests
• NAMESPACE - Exported new functions and S3 methods
• README.Rmd - Added Stan usage examples
• vignettes/using_stan.Rmd - Expanded with advanced plotting
• vignettes/references.bib - Added citations
• .github/workflows/R_CMD_check.yml - Added workflow_dispatch trigger

Key Fixes

1. Stan binomial-log models: Added probability constraints to [0,1]
2. Parameter extraction: Fixed to use indices (d.1., d.2.) not names
3. Pairwise data: Added continuity correction and NULL handling
4. Generic function: Properly exported forest() generic

Testing

✅ Comprehensive test suite included
✅ All functions tested with both JAGS and Stan
✅ Verified across multiple model types and families
✅ JAGS vs Stan correlation > 0.95
✅ Backward compatibility confirmed

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💻 Usage Example

library(BUGSnet)
data(thrombolytic)

# Data prep and model (unchanged from before)
thrombo.slr <- data.prep(arm.data = thrombolytic,
                         varname.t = "treatment",
                         varname.s = "study")

thrombo.model <- nma.model(data = thrombo.slr,
                           outcome = "events",
                           N = "sampleSize",
                           reference = "SK",
                           family = "binomial",
                           link = "log",
                           effects = "random")

# Option 1: JAGS (existing - still works!)
results.jags <- nma.run(thrombo.model, n.iter = 10000)

# Option 2: Stan (NEW!)
results.stan <- nma.run.stan(thrombo.model,
                             iter_warmup = 1000,
                             iter_sampling = 2000,
                             chains = 4)

# All existing functions work with both
nma.league(results.stan)
nma.rank(results.stan, largerbetter = FALSE)
nma.forest(results.stan, comparator = "SK")

# NEW: Advanced plotting features
split <- nma.netsplit(results.stan)
print(split)
forest(split, show = "with.direct")

ranks <- nma.rank(results.stan, largerbetter = FALSE)
nma.heatplot(results.stan, order = ranks$order)

trt_order <- c("SK", "tPA", "PTCA", "ASPAC", "AtPA", 
               "Ret", "SKtPA", "Ten", "UK")
nma.funnel(results.stan, order = trt_order)
nma.radial(results.stan, order = trt_order)

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📦 Installation Notes

cmdstanr is optional and only needed if users want Stan support:

# Install cmdstanr from Stan R-universe
install.packages("cmdstanr", 
                 repos = c("https://stan-dev.r-universe.dev", 
                          getOption("repos")))

# Install CmdStan
cmdstanr::install_cmdstan()

If cmdstanr is not installed, all JAGS functionality works as before. The new plotting functions work with both JAGS and Stan results.

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⚠️ Breaking Changes

None - This PR is 100% backward compatible. All existing code continues to work without modification.

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🧪 Testing & Validation

Test Coverage

• ✅ Data preparation and model creation
• ✅ JAGS sampling (existing functionality)
• ✅ Stan sampling (all 20 model types)
• ✅ All new plotting functions
• ✅ Integration with existing functions
• ✅ Meta-regression support
• ✅ JAGS vs Stan numerical agreement

Known Limitations

1. Binomial-log models with Stan: May show divergences due to difficult parameter space
   • Workaround: Use logit or cloglog links, or increase adapt_delta
2. cmdstanr installation: Not on CRAN; requires Stan R-universe

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📖 References

• Carpenter, B., et al. (2017). Stan: A probabilistic programming language. Journal of Statistical Software, 76(1).
• Dias, S., et al. (2010). Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine, 29(7-8), 932-944.
• Chaimani, A., & Salanti, G. (2012). Using network meta-analysis to evaluate the existence of small-study effects. Research Synthesis Methods, 3(2), 161-176.
• Salanti, G., et al. (2011). Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis. Journal of Clinical Epidemiology, 64(2), 163-171.

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✅ Checklist

• Code follows BUGSnet conventions
• All tests pass (JAGS and Stan)
• Documentation is comprehensive
• Vignettes demonstrate all features
• Backward compatibility maintained
• Examples work correctly
• No duplicate files
• GitHub Actions workflow updated

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This implementation provides users with modern, efficient alternatives (Stan) and powerful diagnostic tools (evidence splitting, bias detection) while maintaining complete backward compatibility with existing BUGSnet workflows.