Classification using LS-PLS for Logistic Regression
Caroline Bazzoli <caroline.bazzoli@univ-grenoble-alpes.fr>,
Sophie Lambert-Lacroix <sophie.lambert-lacroix@univ-grenoble-alpes.fr>,
Thomas Bouleau <tbouleau@gmail.com>
Fit logistic regression models using LS-PLS approaches
to analyse both clinical and genomic data. (C. Bazzoli and
S. Lambert-Lacroix. (2017) Classification using LS-PLS with
logistic regression based on both clinical and gene expression
variables <https://hal.archives-ouvertes.fr/hal-01405101>).

Companion to "Learning Statistics with R"
Functions that I have found useful for teaching purposes.

Functions that I have found useful for teaching purposes. purposes.

Companion package to "Learning Statistics with R"

A collection of tools intended to make introductory
statistics easier to teach, including wrappers for common
hypothesis tests and basic data manipulation. It accompanies
Navarro, D. J. (2015). Learning Statistics with R: A Tutorial
for Psychology Students and Other Beginners, Version 0.6.

A collection of tools intended to make introductory statistics easier to teach, including wrappers for common hypothesis tests and basic data manipulation. It accompanies Navarro, D. J. (2015). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.5. [Lecture notes] School of Psychology, University of Adelaide, Adelaide, Australia. ISBN: 978-1-326-18972-3. URL: http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/.

the accelerated failure time model to right censored data based on least-squares principle
Lin Huang <lh2123@columbia.edu>, Zhezhen Jin <zj7@columbia.edu>
Due to lack of proper inference procedure and software, the ordinary linear regression model is seldom used in practice for the analysis of right censored data. This paper presents an S-Plus/R program that implements a recently developed inference procedure (Jin, Lin and Ying, 2006)\cite{Jin} for the accelerated failure time model based on the least-squares principle.