Nonparametric Estimation for Multistate Models
Nicole Ferguson <nicole.ferguson@louisville.edu>, Guy Brock
<guy.brock@louisville.edu>, Somnath Datta
<somnath.datta@louisville.edu>
R (>= 2.0), methods, class, graph, lattice
Nonparametric estimation for right censored, left
truncated time to event data in multistate models

Nicole Ferguson <nicole.ferguson@kennesaw.edu>, Somnath Datta
<somnath.datta@louisville.edu>, Guy Brock
<guy.brock@louisville.edu>
Nonparametric estimation for right censored, left
truncated time to event data in multistate models.

Nicole Ferguson <nicole.ferguson@kennesaw.edu>, Somnath Datta <somnath.datta@louisville.edu>, Guy Brock <guy.brock@louisville.edu>
Nonparametric estimation for right censored, left truncated time to event data in multistate models.

Wolfgang A. Obermeier [cre, aut],
Lukas W. Lehnert [cre, aut],
Joerg Bendix [aut]
The new methodology "moving subset analysis" provides functions to investigate the effect of environmental conditions on the CO2 fertilization effect within longterm free air carbon enrichment (FACE)  experiments. In general, the functionality is applicable to derive the influence of a third variable (forcing experiment-support variable) on the relation between a dependent and an independent variable.

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'))
Visualises multiple sequence alignments dynamically within the
Shiny web application framework.
Benedikt Rauscher [aut, cph],
Zachary Charlop-Powers [cre, aut]

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

Visualizes multiple sequence alignments dynamically within the
Shiny web application framework.

Multivariate Fay Herriot Models for Small Area Estimation
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>.