Data format, model types, priors, and sampler settings for Bayesian statistical and PK/PD modeling.

You provide a data file (CSV or Parquet) plus the model type and the outcome/predictor columns to fit. Your model-type choice most shapes what you get back: a plain regression returns parameter posteriors, while pkpd returns compartmental PK parameters (clearance, volume of distribution) instead.

Required inputs

Parameter	Type	Description
data_url	string	URL to a CSV or Parquet file.
model_type	string	`regression`, `hierarchical`, `time_series`, `mixture`, `pkpd`, `dose_response`, or `custom`.
outcome_column	string	Column name for the outcome/dependent variable.
predictor_columns	array of string	Column names for predictor variables (at least one).

Model-specific columns

Parameter	Used by	Description
group_column	hierarchical	Column defining the grouping/level.
time_column	time_series, pkpd	Column for the time variable.
pk_model	pkpd	`one_compartment`, `two_compartment`, or `three_compartment`.
administration	pkpd	`iv_bolus`, `iv_infusion`, or `oral`.
concentration_column	pkpd	Column for measured drug concentration.
dose_column	pkpd	Column for administered dose.
subject_column	pkpd	Column for subject ID, for population (mixed-effects) PK.
population_model	pkpd	Boolean; fit a population model with per-subject random effects. Default `false`.
random_effects	pkpd	Parameters with random effects in a population model. Default `["CL", "V"]`.
model_code	custom	Your own PyMC model code, when `model_type` is `custom`.

Likelihood and link function

Parameter	Options	Default
likelihood	`normal`, `student_t`, `poisson`, `binomial`, `beta`	`normal`
link_function	`identity`, `log`, `logit`	`identity`

Priors

Optional. Supply as {param_name: {distribution: "Normal", params: {mu: 0, sigma: 1}}} to override the model's default priors for one or more parameters.

Sampler settings

Parameter	Range	Default	Description
inference_method	`mcmc`, `variational`, `pathfinder`	`mcmc`	Posterior inference method.
num_chains	1–8	4	Number of MCMC chains.
num_samples	500–10,000	2,000	Posterior samples drawn.
num_tune	200–5,000	1,000	Tuning/warmup samples before drawing.
target_accept	0.5–0.99	0.9	Target acceptance rate (raise it if you see divergences).

Output options

Parameter	Default	Description
compute_loo	`true`	Compute leave-one-out cross-validation for model comparison.
compute_waic	`true`	Compute WAIC for model comparison.
generate_plots	`true`	Generate trace, posterior, and posterior-predictive-check plots.

Data requirements

The outcome and predictor columns must be present and numeric (categorical predictors need to be encoded upstream).
For pkpd, provide time_column, concentration_column, and dose_column; add subject_column for population PK.
For hierarchical, provide group_column so the model can pool information across groups.

Bayesian Optimization inputs and parameters

Required inputs

Model-specific columns

Likelihood and link function

Priors

Sampler settings

Output options

Data requirements

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