Changelog • outstandR

outstandR 2.0.0

New features

Explicit Model Specifications: The package architecture has been fundamentally updated to explicitly separate outcome prediction from population weighting. The formula argument across all strategy_*() functions now accepts a list containing outcome_model and balance_model. This conceptual split makes the statistical intent of methods like MAIC much clearer (i.e., defining exactly which covariates to balance vs. which to regress on) and future-proofs the package for upcoming doubly-robust methods that require both models simultaneously.
```
# New recommended approach 
strategy_maic(
  formula = list(
    outcome_model = y ~ trt, 
    balance_model = ~ age + sex
  )
)

# Legacy approach (still supported) 
strategy_maic(formula = y ~ trt + age + sex)
```
To support this new architecture, strategy_maic() introduces intelligent defaults for missing formula components:
- If a traditional single formula is passed (legacy support), it automatically infers the balance_model by stripping the response and treatment variables.
- If balance_model is omitted from a list, it acts as a delayed function defaulting to a linear sum of all covariates found in the aggregate-level data (ALD).
- If outcome_model is omitted, it gracefully defaults to y ~ trt.
outstandR() gains a new verbose argument (defaulting to TRUE), allowing users to silence console output during simulations or repetitive tasks. It also introduces proactive warnings for computationally expensive operations (e.g., very high n_boot for MAIC, or large $N \times R$ for ML G-computation) to prevent users from thinking the session has hung.
MAIC weight estimation can now balance covariate variances and covariances, reducing residual confounding from mismatched distributions.
- Added the moments argument to strategy definition functions and calc_maic(). Setting moments = 2 automatically expands the balancing formula to include squared terms.
- Added the int argument to strategy definition functions and calc_maic(). Setting int = TRUE automatically expands the balancing formula to include all two-way interactions between covariates.
- Automatic variance target calculation when moments = 2, the maic.boot() function now automatically calculates the required aggregate target for squared terms ( $E[X^2]$ ) internally using the base variable’s mean and sd provided in the ALD.

Minor improvements and fixes

Fixed a critical bug in strategy_mim() and strategy_gcomp_bayes() where covariate uncertainty was not being fully propagated due to incorrect looping over predicted samples.
Significantly improved the performance of the MAIC method by refactoring maic.boot() to pre-calculate balance and outcome matrices, reducing redundant computations during bootstrap iterations.
guess_treatment_name() (used under the hood by all strategies when trt_var is NULL) is now smarter. It correctly identifies the treatment variable by checking for duplicated variables in interaction terms, or by falling back to the last main effect term.
check_balance_formula() has been added to provide strict validation for balance models. It ensures the formula is one-sided (no response variable) and throws an informative warning if the treatment variable is mistakenly included.
check_formula() now explicitly intercepts Surv objects in outcome models. It throws a clear, informative error stating that survival data support is officially scheduled for a later version.
Improved reproducibility and side-effect management by switching to withr::local_seed() for local random seed handling.
Updated the print.outstandR() method to handle delayed balance model functions, providing clearer output for auto-generated models.
Standardized strategy naming: strategy_gcomp_stan() is now strategy_gcomp_bayes() to more accurately reflect the underlying statistical approach.

Deprecated and defunct

strategy_stc() is now deprecated and will be removed in a future release. Simulated Treatment Comparison (STC) is being phased out in favor of G-computation approaches (e.g., strategy_gcomp_ml()), which offer better statistical properties for population adjustment.

outstandR 1.0.0

outstandR 1.0.0 marks a major milestone, transforming the original code from the Remiro-Azocar study into a generalized, user-friendly R package.

Breaking changes & major updates

The aggregate-level data (ALD) input has been redesigned to use a standard long format, replacing the legacy format used in the original study.
Treatment labels and covariate names are no longer hard-coded. outstandR now dynamically extracts treatment arms (e.g., rather than “A”, “B”, “C”) and general covariates directly from your input data (5119596).
Covariate generation functions have been separated and moved into their own standalone package, simcovariates.

Expanded modelling capabilities

The family argument in outstandR() has been expanded to support binary (binomial), continuous (gaussian), and count data (poisson).
Input data can now contain any combination of binary and continuous covariates, specifically upgraded for MAIC (4fb1e21).
simulate_ALD_pseudo_pop() now accepts user-provided marginal arguments for a target distribution. These can be defined optionally via marginal_distns and marginal_params in strategy_gcomp_ml() and strategy_gcomp_bayes().
Users can now explicitly select the outcome scale (e.g., log-odds, risk difference, relative risk) using the new scale argument in outstandR().
Added support for optional correlation structures in ALD simulations (d48d0ab).

Output & documentation improvements

The main outstandR() function now returns absolute values in addition to treatment contrasts (2f1fbd7).
Added a dedicated print() method for outstandR objects to cleanly display analysis results (983cb2f).
Comprehensive, separate vignettes demonstrating workflows for binary, continuous, and count outcome data (fc10a68).
A complete package documentation website has been built using pkgdown and is now available via GitHub Pages (4df16f2).