bbw: Blocked Weighted Bootstrap

The blocked weighted bootstrap (BBW) is an estimation technique for use with data from two-stage cluster sampled surveys in which either prior weighting (e.g. population-proportional sampling or PPS as used in Standardized Monitoring and Assessment of Relief and Transitions or SMART surveys) or posterior weighting (e.g. as used in rapid assessment method or RAM and simple spatial sampling method or S3M surveys). The method was developed by Accion Contra la Faim, Brixton Health, Concern Worldwide, Global Alliance for Improved Nutrition, UNICEF Sierra Leone, UNICEF Sudan and Valid International. It has been tested by the Centers for Disease Control (CDC) using infant and young child feeding (IYCF) data. See Cameron et al (2008) <doi:10.1162/rest.90.3.414> for application of bootstrap to cluster samples. See Aaron et al (2016) <doi:10.1371/journal.pone.0163176> and Aaron et al (2016) <doi:10.1371/journal.pone.0162462> for application of the blocked weighted bootstrap to estimate indicators from two-stage cluster sampled surveys.

Package details

AuthorMark Myatt [aut], Ernest Guevarra [ctb, cre]
MaintainerErnest Guevarra <[email protected]>
LicenseAGPL-3
Version0.1.3
URL https://github.com/validmeasures/bbw
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("bbw")

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bbw documentation built on May 2, 2019, 4:51 a.m.