Packaged: 2018-07-19 15:57:04 UTC; Biometrikus

solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary
maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available
in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.

in the package as well, see Puhr, Heinze, Nold, Lusa and Geroldinger (2017) <doi:10.1002/sim.7273>.
Packaged: 2020-09-16 06:21:02 UTC; Biometrikus

Title: Exact logistic regression including Firth correction
Description: Exact logistic regression including Firth correction for

Title: Logistic Regression with Misclassification in Dependent
Description: Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.
Packaged: 2017-05-31 00:54:15 UTC; johnnyzhang

Maintainer: Andrew J. Landgraf <andland@gmail.com>
Description: Dimensionality reduction techniques for binary data including logistic

Description: Dimensionality reduction techniques for binary data including
Suggests: rARPACK (>= 0.10-0), testthat (>= 0.11.0), knitr, rmarkdown

Title: Adjusted Relative Risk from Logistic Regression
Description: Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.

Title: Functional Principal Components Logistic Regression
Author: Carmen Lucia Reina <carmenlureina@gmail.com> Ana Maria Aguilera <aaguiler@ugr.es>
Description: Functions for fitting a functional principal components logit regression model
in four different situations: ordinary and filtered functional principal components
of functional predictors, included in the model according to their variability
explanation power, and according to their prediction ability by stepwise methods. The
proposed methods were developed in Escabias et al (2004)