Nothing

```
# fancy display names in the package
model_names <- c("mutau" = "Aggregate data (with control group)",
"mutau_full" = "Individual-level data with control and treatment",
"rubin" = "Rubin model with aggregate data",
"rubin_full" = "Rubin model with individual-level data",
"quantiles" = "Effects on quantiles",
"logit" = "Logistic model",
"sslab" = "Spike & slab model (3 components)")
data_type_names <- c("pool_noctrl_narrow" = "Aggregate (effects only)",
"pool_wide" = "Aggregate (control and effects)",
"pool_binary" = "Aggregate (event counts)",
"individual" = "Individual-level with continuous outcome",
"individual_binary" = "Individual-level with binary outcome")
# Default settings for matching models with input data types (convert_inputs)
model_data_types <- c("rubin" = "pool_noctrl_narrow",
"mutau" = "pool_wide",
"logit" = "individual_binary",
"rubin_full" = "individual",
"mutau_full" = "individual",
"sslab" = "individual",
#for now no quantiles model from summary level data
"quantiles" = "individual")
data_type_default_model <- c("pool_noctrl_narrow" = "rubin",
"pool_wide" = "mutau",
"pool_binary" = "logit",
"individual" = "rubin_full",
"individual_binary" = "logit")
# Allowed priors for different types of variables
available_priors <- list(
"real" = c("normal", "uniform", "cauchy", "lognormal", "student_t"),
"positive_real" = c("normal", "uniform", "cauchy", "lognormal", "student_t"),
"real_2" = c("multinormal"),
"corr" = c("lkj")
)
prior_dist_fam <- c("uniform" = 0,
"normal" = 1,
"cauchy" = 2,
"multinormal" = 3,
"lkj" = 4,
"lognormal" = 5,
"student_t" = 6)
```

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