mlumr 0.1.0

Initial CRAN release.

Core models

• ML-UMR models: Bayesian multilevel unanchored meta-regression with two model variants:

  • SPFA (Shared Prognostic Factor Assumption): shared covariate effects across treatments
  • Relaxed SPFA: treatment-specific covariate coefficients allowing effect modification estimation

• Three outcome families: binary (binomial), continuous (normal), and count (Poisson) outcomes, each with appropriate link functions:

  • Binomial: logit (default), probit, cloglog
  • Normal: identity (default), log
  • Poisson: log

• Dual Stan backend: rstan (default, CRAN-compatible) with optional cmdstanr support. Switch engines with mlumr_engine("cmdstanr"), which guides installation of cmdstanr and CmdStan if needed. Per-call override via the engine argument in mlumr().

• Simulated Treatment Comparison (STC): Frequentist outcome regression via parametric G-computation with delta-method standard errors. Supports prediction at covariate means or marginalization over full covariate distributions using integration points.

• Naive unadjusted estimate: Benchmark comparison of crude outcome summaries with delta-method confidence intervals.

Data preparation

• set_ipd(), set_agd(), and combine_data() provide a unified interface for preparing IPD and AgD for all three methods.
• set_ipd() rejects covariate names that collide with reserved internal columns (.outcome, .study, .trt, .exposure) so user values cannot be silently overwritten by the standardized frame.
• set_agd() applies the same check to its covariate mean/SD columns (.n, .r, .y, .se, .study, .trt, .E) and additionally rejects cov_means entries that collapse to duplicate names after stripping the _mean / _prop suffix (e.g. c("age_mean", "age")).
• add_integration() generates Sobol-sequence quasi-Monte Carlo integration points with a Gaussian copula to account for covariate correlations, enabling accurate marginalization over the AgD covariate distribution.
• mlumr(), add_integration(), check_integration(), and prior_sensitivity() include verbose controls so scripts and tests can suppress package-level progress output while retaining warnings.
• The public API mirrors the function names used by the related multinma package for the data-setup, integration, and effect-summary workflow (set_ipd(), set_agd(), add_integration(), unnest_integration(), distr(), marginal_effects(), qbern()/pbern()/dbern()). Users familiar with ML-NMR can transfer their muscle memory directly to ML-UMR. When both packages are attached in the same R session R issues masking warnings on the shared names; disambiguate with mlumr::function() / multinma::function().

Prior system

• Prior constructors prior_normal(mean, sd), prior_student_t(df, mean, sd), prior_cauchy(mean, sd) (alias for prior_student_t(df = 1, ...)), and prior_exponential(rate). All six Stan models branch on the prior family at runtime, so any of these can be supplied to prior_intercept, prior_beta, or (normal family only) prior_sigma.
• prior_beta accepts either a single prior (broadcast to all covariates) or a list of per-coefficient priors. Per-coefficient priors must share the same family and df (Stan branches on a single dist code).
• prior_normal(), prior_student_t(), and prior_cauchy() carry an autoscale argument. When passed as prior_beta with autoscale = TRUE, each coefficient’s prior scale is divided by the empirical SD of its covariate (Gelman et al., 2008). autoscale = FALSE by default.
• Default priors follow the Stan community’s prior-choice recommendations (Vehtari et al., 2025):
  • prior_intercept: prior_normal(0, 10)
  • prior_beta: prior_normal(0, 2.5) (weakly informative; Gelman et al.,
  • prior_sigma: prior_normal(0, 2.5) (half-normal via the <lower=0> constraint in Stan) for the normal family.
• default_prior_intercept(), default_prior_beta(), and default_prior_sigma() accessors expose the package defaults. Values are tagged with $default = TRUE and the package $version so prior_summary() can report whether each prior is a default and which mlumr version produced it.
• prior_summary() S3 generic + prior_summary.mlumr_fit() method for human-readable introspection of every prior used in a fit, including post-autoscale per-coefficient scales.
• prior_sensitivity() refits a model across a grid of prior_beta scales and returns a posterior-summary table — the workflow recommended by Vehtari et al.’s prior-choice wiki for judging data- vs prior-driven inference.

Inference helpers

• predict.mlumr_fit() returns population-specific predicted outcomes.
• marginal_effects() returns posterior treatment-effect summaries.
• conditional_effects() returns covariate-conditional treatment effects.
• conditional_predict() returns predictions at specific covariate values.
• predict.mlumr_fit() and conditional_effects() document the Jensen’s-inequality gap on non-identity links: response-scale summaries are E[g^{-1}(eta)], not g^{-1}(E[eta]).

Model comparison

• calculate_dic() for DIC-based comparison (no extra dependencies).
• calculate_loo() and calculate_waic() using the optional loo package for PSIS-LOO and WAIC (Vehtari, Gelman, Gabry, 2017). loo is in Suggests, not Imports.
• compare_models() accepts criterion = c("dic", "loo", "waic"), defaulting to "dic".
• All six Stan models produce pointwise log-likelihood vectors (log_lik_ipd, log_lik_agd) — the standard contract for loo::loo() / loo::waic().

Sampling

• Regression coefficients beta (and beta_comparator in relaxed models) are sampled via an affine (non-centered) reparameterization: z_beta ~ std_* (0, 1), beta = prior_beta_mean + prior_beta_sd .* z_beta. This decouples HMC adaptation from the prior scale and typically improves mixing when the prior scale is mis-matched with the posterior scale.

Diagnostics

• Automatic MCMC diagnostic checks (divergences, Rhat, ESS, treedepth) via check_diagnostics().
• check_integration() provides a check_joint argument that compares pairwise correlation matrices at the current vs doubled n_int (and against the user-supplied correlation target when available).

Stan internals

• Stan prior hyperparameter declarations are shared across all six models via #include include/priors_hyperparameters.stan and include/priors_sigma_hyperparameters.stan. Prior log-density dispatchers live in include/priors_functions.stan.
• Binary-link numerical helpers are shared by the binary SPFA and relaxed models via include/binary_functions.stan.
• E_ipd in the two Poisson Stan models carries <lower=0> so off-API consumers who assemble stan_data manually get a Stan validation error rather than log(0) = -Inf on a non-positive exposure.
• Internal reference page ?mlumr-numerical-guards documents safe_logit, safe_divide, and the <lower=0> Stan guards.

Documentation

• A package startup message reports the installed mlumr version and GitHub repository when the package is attached.
• ?mlumr-package provides a full overview of the typical workflow (data preparation -> integration -> fit -> diagnostics -> inference) and points at the alternative methods (stc(), naive()).
• @seealso cross-links across predict.mlumr_fit(), marginal_effects(), conditional_effects(), conditional_predict(), prior_summary(), and prior_sensitivity().
• Six vignettes covering data preparation, ML-UMR models, STC and naive benchmarks, method comparison, and a complete worked example. Vignettes run compact examples during package checks; intentionally failing demonstrations and longer production-style fits remain non-executed.

Testing

• Test coverage spans data setup, integration, link functions, priors, prior summaries, prior sensitivity, engine selection, diagnostics, prediction, conditional effects, ML-UMR validation, fitted-model behavior, STC, naive benchmarks, utility functions, and LOO/WAIC/DIC model comparison.
• The test suite includes reserved-name guards, duplicate covariate-name checks, standardized-frame shape checks, pointwise log-likelihood extraction, integration diagnostics, posterior-summary validation, and family-specific behavior for binary, normal, and Poisson outcomes.