Packaged: 2017-08-02 05:00:50 UTC; Visitante

Packaged: 2017-01-01 03:39:52 UTC; Visitante

Description: Functions to calculate exact critical values, statistical power, expected time to signal, and required sample sizes for performing exact sequential analysis. All these	calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled.

Maintainer: Ivair Ramos Silva <ivair@ufop.edu.br>

Title: Observational Database Study Planning using Exact Sequential
Authors@R: c(person("Judith", "Maro", role=c("aut", "cre"), email = "judy_maro@harvardpilgrim.org"), person("Laura", "Hou", role="aut", email="laura_hou@harvardpilgrim.org"))
Author: Judith Maro [aut, cre], Laura Hou [aut]
Description: Functions to be used in conjunction with the 'Sequential' package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(...), accepts parameters for simulation of a simple exposure pattern and for the 'Sequential' package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the 'Sequential' package on these populations, which allows for the exploration of outcome misclassification in data.

Title: Computation and visualization of socioeconomic indices and
Authors@R: c(person("Benoit", "Lalloue", role = c("aut", "cre"), email
= "benoit.lalloue@ehesp.fr"), person("Severine", "Deguen", role
= c("ths", "aut"), email = "severine.deguen@ehesp.fr"),
person("Jean-Marie", "Monnez", role =c("ths", "aut"), email =
Description: This package allows computing and visualizing
socioeconomic indices and categories distributions from
datasets of socioeconomic variables (These tools were developed
as part of the EquitArea Project, a public health program).
Depends: FactoMineR, class, R (>= 3.0.0)