Bounds Testing for Cointegration with Many Persistent Controls R package ardldml version 0.1.0 An implementation of the DML-Bounds procedure of Villena (2026) <doi:10.2139/ssrn.6472826> for testing cointegration in data-rich time-series settings. The Autoregressive Distributed Lag (ARDL) bounds test of Pesaran, Shin and Smith (2001) <doi:10.1002/jae.616> avoids pretesting the integration order of the regressors but is not designed for a high-dimensional conditioning set. Residualising the lagged levels against persistent controls can absorb stochastic trends and thereby change the finite-sample null distribution, so what governs the null is the effective number of stochastic trends surviving residualisation rather than the integration order of the original regressors. The procedure combines h-block cross-fitting, a balanced nuisance projection in the Double Machine Learning (DML) style of Chernozhukov and others (2018) <doi:10.1111/ectj.12097>, adaptive weighting after Zou (2006) <doi:10.1198/016214506000000735>, and a restricted system wild bootstrap that regenerates the dependent variable and the focal regressor jointly. No critical-value table is shipped: the classical bracket is regenerated by simulation and the operational critical value is bootstrapped. A trend-absorption diagnostic and a penalty-sensitivity sweep report whether a verdict survives a change of conditioning set. Monthly United States macroeconomic series from the 'FRED-MD' database of McCracken and Ng (2016) <doi:10.1080/07350015.2015.1086655> are bundled so every example runs offline. Software Depends: R (>= 4.1.0) Imports: glmnet, stats, graphics, grDevices, utils Suggests: knitr, rmarkdown, testthat (>= 3.0.0), tseries Merwan Roudane <merwanroudane920@gmail.com> Comprehensive R Archive Network (CRAN) Merwan Roudane [aut, cre, cph] MIT + file LICENSE (https://CRAN.R-project.org/package=ardldml/LICENSE) 2026-09-15 application/tgz https://CRAN.R-project.org/package=ardldml doi:10.32614/CRAN.package.ardldml en-GB