NEWS

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rdborrow 0.0.5.1 (2026-10-30)

Bug fixes

• scm() now works after install.packages("rdborrow"). Its solver package, ECOSolveR, was only suggested, so a default install did not include it and scm() stopped with an error until ECOSolveR was installed by hand. ECOSolveR is now a required dependency.

rdborrow 0.0.5.0 (2026-10-08)

Breaking changes

• Formulas in ec_ipw(), ec_aipw(), did_ec_ipw(), did_ec_aipw(), and did_ec_or() now give an error when the right-hand side uses . or a variable that is not in covariates_col_name. . added the outcomes and the treatment to the model (#105), and a variable not in the data was taken from the R session (#107). An outcome used as a predictor, such as y1 in the model for y2, now gives a warning: it is measured after randomization, so adjusting for it can bias the treatment effect. The primary simulation vignette no longer does this.
• Outcome formulas in ec_aipw(), did_ec_aipw(), and did_ec_or() must now have the outcome name alone on the left-hand side. A transformed left side, such as log(y1) ~ x1, was accepted, but did_ec_or() used the transformed scale while ec_aipw() and did_ec_aipw() mixed it with the raw outcome, so the estimates were wrong. Transform the outcome column before the analysis instead. One-sided formulas such as ~ x1 now fail with a clear message (#138, #139).
• setup_simulation_primary() and setup_simulation_OLE() now require true_effect to be a single number, the true effect at the final visit. run_simulation() scores only the final visit, so a vector was recycled across simulated trials rather than matched to visits, and bias, coverage, type I error, and power were silently wrong unless every element was equal. With an even number of trials there was no warning (#90).

Minor improvements

• New example dataset SyntheticDataII, an unbalanced companion to SyntheticData: 2:1 randomization (160 treated, 80 controls), 140 external controls, and a covariate shift between the trial and the external controls. Balanced data can hide errors, such as exchanging the randomization probability and its complement (#102).
• Progress output from quiet = FALSE in run_analysis(), estimate(), and run_simulation() now uses message() instead of cat(), so suppressMessages() silences it (#129).
• did_ec_ipw(), did_ec_aipw(), did_ec_or(), and scm() results now include a standard_deviation column, the standard deviation of the bootstrap replicates, as the primary methods do with bootstrap inference (#129).
• ec_ipw() and ec_aipw() now compute the sandwich variance without an N x N matrix, so memory use grows linearly with the sample size. At N = 8,000, peak memory falls from about 650 MB to 185 MB, and N = 50,000 runs in 0.2 seconds instead of 20. The results are unchanged (#117).
• scm() now solves its optimizations with CVXR's current interface (psolve() and value()) instead of solve() and $getValue(), which CVXR has deprecated and will remove. rdborrow now requires CVXR >= 1.8.1. Estimates are unchanged (#97).
• setup_analysis_primary() now returns its analysis object visibly, as setup_analysis_OLE() does, so that it prints at the console (#129).

Bug fixes

• All method constructors now require bootstrap to be at least 2. bootstrap = 1 was accepted and then failed inside run_analysis() with an opaque confidence interval error (#91).
• ec_aipw() now computes its sandwich variance from the outcome models of the point estimate. It fitted them again on all patients, so a formula with a data-dependent basis such as splines::ns() got different knots, and the standard error was slightly off (#120).
• ec_aipw(), did_ec_aipw(), and did_ec_or() now match each outcome formula to an outcome by its left-hand side. They used the formulas in list order, so formulas listed in a different order from the outcomes silently gave wrong estimates; in did_ec_or(), reversing them flipped the signs. A formula for a variable that is not an outcome, two formulas for one outcome, or an outcome with no formula is now an error (#104).
• ec_ipw(), ec_aipw(), did_ec_ipw(), and did_ec_aipw() now work when the trial-status column is not named S, and trt_formula works when the treatment column is not named A. The formula's left-hand side was rewritten to the user's column name after the data had been copied into columns named S and A, so the model could not find it, or silently used a same-named object from the R session (#103).
• ec_ipw() and ec_aipw() now give an error when bootstrap_ci_type is set without bootstrap. The value was kept but not used, so a user who asked for, say, BCa intervals got sandwich intervals with no message (#123).
• ec_ipw() and ec_aipw() no longer stop with a LAPACK "system is exactly singular" error when a covariate is collinear with others or constant. glm() and lm() already drop such a covariate from the fit, but the sandwich variance kept its column. The results now equal those without the covariate (#118).
• run_analysis() now checks that its first argument is an analysis object and that quiet is TRUE or FALSE. A wrong type gave "no applicable method for @" (#125).
• run_simulation() no longer errors with "'x' is NULL" when a method's results have a single column. It kept the last row of each replicate without drop = FALSE, so a one-column data frame collapsed to a vector (#93).
• scm() now works with two external controls, and gives a clear error with one. Its leave-one-out cross-validation left a single external control, which R turned into a vector, and the analysis failed with "'x' must be an array of at least two dimensions" (#115).
• scm() now gives an error that names a covariate that is not numeric or logical, such as a factor. It failed with "Cannot convert object of class <matrix/array> to a CVXR Expression", because the matching matrix became a character matrix (#116).
• scm() now rejects an infinite lambda_min or lambda_max, which failed later in the analysis, and gives an error when nlambda = 1 and lambda_max differs from lambda_min, because only lambda_min was used. The result of run_analysis() now has an attribute "lambda" with the penalty that cross-validation selected (#124).
• scm() now checks the status of each optimization. When the solver does not find a solution, for example with a covariate in very different units such as age in days, the analysis stops with an error that names the status and suggests rescaling. It stopped later with "requires numeric/complex matrix/vector arguments". Solutions that CVXR marks as inaccurate are still used, with CVXR's warning (#112).
• setup_analysis_OLE() and the OLE methods now round a T_cross that is a whole number only up to floating-point error, such as 0.6 / 0.2. It passed validation but was used unrounded, so one visit fell in both periods and did_ec_ipw() and did_ec_aipw() gave silently wrong estimates. Calling estimate() directly now also validates T_cross. simulate_trial() and simulate_outcome_from_model() round T_cross the same way, so simulated data no longer starts the crossover one visit early (#110).
• setup_analysis_primary() and setup_analysis_OLE() now require the trial-status and treatment columns to be numeric or logical. A factor with levels such as c("1", "0") passed the 0/1 check, and trt_formula then modeled the wrong level, so did_ec_ipw() and did_ec_aipw() gave silently wrong estimates. Calling estimate() directly runs the same check (#111).
• setup_analysis_primary() and setup_analysis_OLE() now give an error when an external control has treatment 1. The methods assume that external controls are untreated, and ec_ipw() and ec_aipw() used such patients in different ways: ec_ipw() kept them as controls, and ec_aipw() left them out of its outcome model. Calling estimate() directly runs the same check (#108).
• setup_analysis_primary() and setup_analysis_OLE() now give an error when an outcome or covariate column has missing values, and name the first column and row. The methods gave NA estimates, a finite confidence interval next to an NA estimate, or, in did_ec_or(), an estimate from the complete cases for some visits only, with no message. Remove or impute missing values before the analysis. Calling estimate() directly runs the same check (#114).
• setup_analysis_primary(), setup_analysis_OLE(), setup_simulation_primary(), and setup_simulation_OLE() now require alpha to be more than 0 and less than 1. alpha = 0 and alpha = 1 were accepted and gave infinite, missing, or zero-width confidence intervals; with bootstrap and alpha = 1, the interval did not contain the point estimate. Calling estimate() directly runs the same check (#121).
• setup_analysis_primary() and setup_analysis_OLE() now give an error that names the empty group when the data has no trial treated patients or no trial controls, and setup_analysis_OLE() also when it has no external controls. The methods gave NaN results or an unclear error. The primary methods check for external controls when they run; ec_ipw(weight = 0) does not use them, so it still runs without them. Calling estimate() directly runs the same checks (#122).
• setup_analysis_primary() and setup_analysis_OLE() now give an error when an outcome or covariate is named S or A, or is the trial-status or treatment column. The internal data uses S and A, so an outcome named A with the treatment in another column was read as the treatment, and the estimate was silently wrong. Calling estimate() directly runs the same check (#106).
• setup_analysis_primary() and setup_analysis_OLE() now give an error when an outcome or covariate name is listed twice, including as both an outcome and a covariate. outcome_col_name = c("y1", "y1") gave two identical estimates (#125).
• setup_analysis_primary() and setup_analysis_OLE() now accept outcome and covariate names that are not syntactic R names, such as "week 12". The internal data renamed them (to week.12), and the analysis failed with "undefined columns selected". Write such names with backticks in formulas (#125).
• setup_simulation_OLE() now checks T_cross as setup_analysis_OLE() does: it must be a whole number of at least 1 and less than the number of outcomes. An invalid value was accepted, and the error came later from run_simulation() (#141).

Documentation

• Two new developer articles. "Adding a new method" explains how an estimator plugs into run_analysis() and run_simulation() through S4 dispatch, walks through a runnable toy method, and gives a checklist for adding a method to the package (#87). "Developing with AI coding agents" explains the new AGENTS.md file of agent instructions and gives a workflow, example prompts, and review habits for working on the package with an AI coding agent (#88).

rdborrow 0.0.4.2 (2026-10-01)

Bug fixes

• Two tests asserted exact floating-point equality between a marginal treatment model and an equivalent intercept-only model. The two are mathematically identical but computed by different routes, so they can differ in the last bit under BLAS libraries such as BLIS; both now compare with a tolerance.

rdborrow 0.0.4.1 (2026-09-24)

Breaking changes

• bootstrap_ci_type no longer accepts "stud" in any method constructor. Studentized intervals require a variance estimate for each bootstrap replicate, which the estimators do not produce, so the option failed whenever it was used (#77).

Bug fixes

• Corrected the DOIs in the package references. The two DOIs cited in DESCRIPTION pointed to unrelated papers, and the open-label extension methods and vignettes cited an unrelated paper rather than Zhou X, Pang H, Drake C, Burger HU, Zhu J (2024). Reported by Herb Pang.
• bootstrap_ci_type = "bca" no longer errors. boot.ci() re-invokes the bootstrap statistic through empinf() without the arguments passed to boot(), so those are now captured in a closure (#77).
• bootstrap_ci_type = "norm" no longer returns NA confidence bounds. The normal component of boot::boot.ci() output has three columns rather than five, so the fixed index used to read the bounds ran past the end of it (#77).
• did_ec_ipw() and did_ec_aipw() no longer return NA estimates when trt_formula is left at its default of NULL. The marginal randomization probability was a scalar, so subsetting the treatment weights by a length-N logical produced NA for all but the first subject (#71).

rdborrow 0.0.4.0 (2026-08-31)

Breaking changes

• setup_bootstrap() has been removed. Bootstrap settings are now specified directly in method constructors (e.g., ec_ipw(bootstrap = 500, bootstrap_ci_type = "perc")). Calling setup_bootstrap() now raises an error with migration instructions.
• setup_method_weighting(), setup_method_DID(), and setup_method_SCM() have been removed. Use ec_ipw(), ec_aipw(), did_ec_ipw(), did_ec_aipw(), did_ec_or(), or scm() instead. Calling the old constructors now raises an error with migration instructions.
• EC_IPW_OPT(), EC_AIPW_OPT(), and all legacy_DID_EC_* / legacy_SCM* internal functions have been deleted.
• did_ec_ipw() and did_ec_aipw(): trt_formula default changed from "" to NULL.

Changes

• ec_ipw() and ec_aipw() QC'd against Zhou et al. (2024a, JRSS-A). Internal code now maps directly to paper equations (Def 1/2, Eq 6/7, Theorems 3/4).
• All estimator internals refactored: redundant parameters removed, potential trick replaced with direct weighted means, consistent naming (_core, _se, _boot_statistic).
• build_analysis_df() is now an S4 method on method_weighting_obj.
• Bootstrap logic moved from .format_primary_results() (deleted) to inline control flow in each estimate() method.
• Added input validation: S/A must be binary 0/1, T_cross must be a positive integer less than the number of outcomes, outcome_formula length must match number of outcomes.
• Vignette improvements: T_cross semantics documented, head() calls added, rank-deficiency warnings suppressed in OLE simulation output.

rdborrow 0.0.3.0

Changes

• New API introduced: eg ec_ipw() and ec_aipw() replace setup_method_weighting().
• Internally, core functions such as EC_IPW_OPT() are further broken down into atomic functions.
• Bootstrapping functions now call atomic functions (instead of similar, duplicated code).
• Additional test cases in preparation of deprecating legacy_ functions such as EC_IPW_OPT().
• Bug fixes (AIPW called the IPW bootstrapping function)

rdborrow 0.0.2.0

Changes

• Refactor out imports, prepare for CRAN

rdborrow 0.0.1.0

Changes

• Original package with both IPW and AIPW methods