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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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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