Clustered Jackknife Instrumental Variables Estimation // Tools for instrumental variables estimation and inference under clustered errors with many instruments. The current release provides the cluster-jackknife IV estimator (CJIVE) of Frandsen, Leslie and McIntyre (2025) doi:10.1162/rest.a.263 for a single endogenous regressor in a just-identified design, with cluster-robust inference: each observation's first-stage value is fitted leaving out its entire cluster, which removes the many-instrument bias that survives clustering. The leave- cluster-out fits use an exact Woodbury block update -- one factorisation of the instrument Gram matrix plus a small solve per cluster -- so the estimator scales to large samples. A companion 'iv_compare()' reports ordinary least squares, two- stage least squares, the observation-level jackknife and CJIVE on a common cluster-robust standard error.