Bootstrap options with tight intergation within some statistical methods have become availabe in recent years in packages such as **SAS**, **Stata**, **Spss** and **Splus** and **Spss**/**AMos**. The degree and ease of use varies greatly. Options to Bootstrap in packages hosting a sample with replacement method can allow one, in principle, to bootstrap an estimator of choice. Why bootstrap? One does so to achieve better sampling distributions of estimators. Bootstrap methods potentially offer insights into inference matters that might be difficult or impossible to reconcile otherwise. Small and large sample size settings can present complicated data configurations to estimation tasks such as parameter estimates or functions of one or more parameters. For example, settings such as complex surveys have seen bootstrap methods contribute to challanging survey estimation and inference tasks. For users of **R** one finds several contributed packages for bootstrap methods. Packages **boot**, **bootstrap**, **pvclust**, **rqmcmb2**, **scaleboot**, **simpleboot**, and **Hmisc** offer standard and advanced options not found in the some of the above commercial packages.

Much has been written in the last 25 years about the bootstrap. Two useful references to consider are:

Efron, B & Tibshirani, R.J. (1993), An Introduction to the Bootstrap, Chapman and Hall.

And: Davison & Hinkley, (1997), Bootstrap Methods and their Applications, Cambridge Univ. Press.