Title: Multiple Sequence Alignment for R Shiny
Authors@R: c(person("Benedikt", "Rauscher", email = "b.rauscher@dkfz-heidelberg.de", role = c("aut", "cph")),
person('Zachary', 'Charlop-Powers', role=c('cre', 'aut'), email='zach.charlop.powers@gmail.com'))
Description: Visualises multiple sequence alignments dynamically within the
Packaged: 2016-12-05 23:12:24 UTC; zachpowers

Packaged: 2017-02-24 22:47:14 UTC; zachpowers

Suggests: Biostrings, knitr, rmarkdown, testthat (>= 3.0.0)

Description: Visualizes multiple sequence alignments dynamically within the

Title: Multivariate Fay Herriot Models for Small Area Estimation
Author: Novia Permatasari, Azka Ubaidillah
Description: Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accomodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.

Description: Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.