Title: Hierarchial Bayesian Analysis of Recognition Memory
Description: Contains functions for fitting hierarchial versions of
EVSD, UVSD, and DPSD to recognition memory confidence-ratings

Title: Hierarchical Bayesian Analysis of Recognition Memory
Description: Contains functions for fitting hierarchical versions of
EVSD, UVSD, DPSD, and our gamma signal detection model to

EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
our gamma signal detection model to recognition memory

Package: hbsae
Version: 1.0
Author: Harm Jan Boonstra
Maintainer: Harm Jan Boonstra <hjboonstra@gmail.com>
License: GPL-3
Title: Hierarchical Bayesian Small Area Estimation
Type: Package
LazyLoad: yes
Description: Functions to compute small area estimates based on a basic
area or unit-level model. The model is fit using restricted
maximum likelihood, or in a hierarchical Bayesian way. In the
latter case numerical integration is used to average over the
posterior density for the between-area variance.  The output
includes the model fit, small area estimates and corresponding
MSEs, as well as some model selection measures. Additional
functions provide means to compute aggregate estimates and
MSEs, to minimally adjust the small area estimates to
benchmarks at a higher aggregation level, and to graphically
compare different sets of small area estimates.
Date: 2012-09-05
Depends: R (>= 2.10)
Imports: Matrix, arm
Collate: 'fakedata.R' 'hbsae.R'
Packaged: 2012-09-05 09:26:15 UTC; mpet
Repository: CRAN
Date/Publication: 2012-09-05 13:03:20

Description: Functions to compute small area estimates based on a basic area or
unit-level model. The model is fit using restricted maximum likelihood, or
in a hierarchical Bayesian way. In the latter case numerical integration is
used to average over the posterior density for the between-area variance.
The output includes the model fit, small area estimates and corresponding
mean squared errors, as well as some model selection measures. Additional functions
provide means to compute aggregate estimates and mean squared errors, to minimally
adjust the small area estimates to benchmarks at a higher aggregation
level, and to graphically compare different sets of small area estimates.
Suggests: mcmcsae, survey, knitr, hypergeo, testthat