Title: Estimation of the Effective Dose including Bootstrap confidence
Description: estimates the effective dose level for quantal bioassay
data by nonparametric techniques and gives a bootstrap

Title: Test changes of a grouped response relative to baseline.
Author: Peter N. Steinmetz <PeterNSteinmetz@steinmetz.org>
Maintainer: Peter N. Steinmetz <PeterNSteinmetz@steinmetz.org>
Description: Functions to test for changes of a response to a stimulus grouping relative

Title: Efficient Initials for Computing the Maximal Eigenpair
Description: An implementation for using efficient initials to compute the
maximal eigenpair in R. It provides two algorithms to find the efficient
initials under two cases: the tridiagonal matrix case and the general matrix
case. Besides, it also provides algorithms for the next to the maximal eigenpair under
Packaged: 2016-12-07 08:16:13 UTC; xiaojunmao

Packaged: 2017-06-15 14:19:59 UTC; xiaojunmao

Packaged: 2017-07-23 13:25:25 UTC; xiaojunmao

Packaged: 2017-08-02 20:46:42 UTC; xiaojunmao

maximal eigenpair in R. It provides three algorithms to find the efficient
case. Besides, it also provides two algorithms for the next to the maximal eigenpair under
Packaged: 2017-10-23 11:42:20 UTC; xiaojunmao

Title: Simple Presentation of Estimated Exponential Semi-Variograms
Authors@R: c(person("Julia", "Dyck", role = c("aut", "cre"), email = "j.dyck@uni-bielefeld.de"),
person("Odile", "Sauzet", role = "aut") )
Description: User friendly interface based on the R package 'geoR' to fit exponential parametric models to empirical semi-variograms in order to model the spatial correlation structure of health data. Geo-located health outcomes of survey participants may be used to model spatial effects on health in an ego-centred approach.
The package contains a range of functions to help explore the spatial structure of the data as well as visualize the fit of exponential models for various metaparameter combinations with respect to the number of lag intervals and maximal distance.
Furthermore, the outcome of interest can be adjusted for covariates by fitting a linear regression in a preliminary step before the semi-variogram fitting process.
Imports: geoR, SpatialTools, stats, lme4, grDevices, graphics, shiny,