{"ESTIMATOR":"Horvitz-Thompson","SHORTNAME":"HT","PACKAGE":"JoSAE","OUTNAME":"DIR","OUTSENAME":"DIR.se","ESTIMATOR_TYPE":"","ESTIMATOR_DATA":"direct","DESCRIPTION":"Design-based estimator that used no auxliary data and simplifies to the sample mean.","Citation":"Breidenbach, J. 2018. JoSAE: Unit-Level and Area-Level Small Area Estimation. R package version 0.3.0. Available Online at: https://CRAN.R-project.org/package=JoSAE."}
{"ESTIMATOR":"Post-stratified","SHORTNAME":"Post-stratified","PACKAGE":"FIESTA","OUTNAME":"PS","OUTSENAME":"PS.se","ESTIMATOR_TYPE":"model-assisted","ESTIMATOR_DATA":"direct","DESCRIPTION":"Design-based estimator which utilizes categorical auxiliary information to improve precision.","Citation":"Frescino, Tracey S.; Moisen, Gretchen G.; Patterson, Paul L.; Toney, Chris; White, Grayson W. (2023). FIESTA: A forest inventory estimation and analysis R package. Ecography 2023: e06428 (ver. 1.0)."}
{"ESTIMATOR":"Generalized Regression","SHORTNAME":"GREG","PACKAGE":"mase","OUTNAME":"GREG","OUTSENAME":"GREG.se","ESTIMATOR_TYPE":"model-assisted","ESTIMATOR_DATA":"direct","DESCRIPTION":"Design-based estimator which utilizes auxiliary information through a linear regression assisting model fit in the area of interest.","Citation":"McConville, K.; Yamamoto, J.; Tang, B.; Zhu, G.; Cheung, S.; Li, S. 2018. mase: Model-Assisted Survey Estimation. R package version 0.1.4 https://cran.r-project.org/package=mase."}
{"ESTIMATOR":"Modified Generalized Regression","SHORTNAME":"Modified GREG","PACKAGE":"JoSAE","OUTNAME":"JU.GREG","OUTSENAME":"JU.GREG.se","ESTIMATOR_TYPE":"model-assisted","ESTIMATOR_DATA":"direct","DESCRIPTION":"Design-based estimator which utilizes auxiliary information through a linear regression assisting model fit in a larger domain.","Citation":"Breidenbach, J. 2018. JoSAE: Unit-Level and Area-Level Small Area Estimation. R package version 0.3.0. Available Online at: https://CRAN.R-project.org/package=JoSAE."}
{"ESTIMATOR":"Unit-level empirical best linear unbiased predition based on the Battese-Harter-Fuller model","SHORTNAME":"Unit-level EBLUP","PACKAGE":"JoSAE","OUTNAME":"JU.EBLUP","OUTSENAME":"JU.EBLUP.se.1","ESTIMATOR_TYPE":"small area","ESTIMATOR_DATA":"indirect","DESCRIPTION":"Model-based estimator that uses plot-level observations as a function of plot-level auxiliary data and a domain random effect on the intercept to produce estimates. This estimator can be represented as a linear combination of the direct estimator and a synthetic estimator.  ","Citation":"Breidenbach, J. 2018. JoSAE: Unit-Level and Area-Level Small Area Estimation. R package version 0.3.0. Available Online at: https://CRAN.R-project.org/package=JoSAE."}
{"ESTIMATOR":"Area-level empirical best linear unbiased predition based on the Fay-Herriot model","SHORTNAME":"Area-level EBLUP","PACKAGE":"JoSAE","OUTNAME":"JFH","OUTSENAME":"JFH.se","ESTIMATOR_TYPE":"small area","ESTIMATOR_DATA":"indirect","DESCRIPTION":"Model-based estimator that uses area-level means of plot data as a function of area-level means of auxiliary data and a domain random effect on the intercept to produce estimates. This estimator can be represented as a linear combination of the direct estimator and a synthetic estimator.  ","Citation":"Breidenbach, J. 2018. JoSAE: Unit-Level and Area-Level Small Area Estimation. R package version 0.3.0. Available Online at: https://CRAN.R-project.org/package=JoSAE."}
{"ESTIMATOR":"Unit-level hierarchical Bayesian predition with half-Cauchy prior based on the battese-Harter-Fuller model","SHORTNAME":"Unit-level HB","PACKAGE":"hbsae","OUTNAME":"hbsaeU","OUTSENAME":"hbsaeU.se","ESTIMATOR_TYPE":"small area","ESTIMATOR_DATA":"indirect","DESCRIPTION":"Model-based estimator that uses the same model as the unit-level EBLUP, but with parameters fit with a hierarchical Bayesian approach. We put a half-Cauchy prior on the ratio of between- and within-area variance.  ","Citation":"Boonstra, H. 2022. _hbsae: Hierarchical Bayesian Small Area Estimation_. R package version 1.2, <https://CRAN.R-project.org/package=hbsae>."}
{"ESTIMATOR":"Area-level hierarchical Bayesian prediction with half-Cauchy prior based on the Fay-Herriot model","SHORTNAME":"Area-level HB","PACKAGE":"hbsae","OUTNAME":"hbsaeA","OUTSENAME":"hbsaeA.se","ESTIMATOR_TYPE":"small area","ESTIMATOR_DATA":"indirect","DESCRIPTION":"Model-based estimator that uses the same model as the area-level EBLUP, but with parameters fit with a hierarchical Bayesian approach. We put a half-Cauchy prior on the between-area variance.  ","Citation":"Boonstra, H. 2022. _hbsae: Hierarchical Bayesian Small Area Estimation_. R package version 1.2, <https://CRAN.R-project.org/package=hbsae>."}