Designing multi-arm multi-stage studies with normal endpoints and known variance.

Thomas Jaki <jaki.thomas@gmail.com>, Dominic Magirr <d.magirr@gmail.com>, Philip Pallmann <p.pallmann@lancaster.ac.uk>

Designing multi-arm multi-stage studies with (asymptotically) normal endpoints and known variance.

Thomas Jaki <thomas.jaki@pm.me>, Dominic Magirr <d.magirr@gmail.com>, Philip Pallmann <philip.pallmann@gmx.de>

Thomas Jaki <thomas.jaki@pm.me>, Dominique-Laurent Couturier <dominique.couturier@mrc-bsu.cam.ac.uk>, Dominic Magirr <d.magirr@gmail.com>, Philip Pallmann <philip.pallmann@gmx.de>

Calculation of Minimum Averaged Mean Squared Error (MAMSE) weights.
This package calculates the nonparametric adaptive MAMSE weights for univariate, right-censored or multivariate data. The MAMSE weights can be used in a weighted likelihood or to define a mixture of empirical distribution functions.

Calculation of Minimum Averaged Mean Squared Error (MAMSE)
weights.
This package calculates the nonparametric adaptive MAMSE
weights for univariate, right-censored or multivariate data.
The MAMSE weights can be used in a weighted likelihood or to
define a mixture of empirical distribution functions.

Calculation of Minimum Averaged Mean Squared Error (MAMSE)
Weights
Calculates the nonparametric adaptive MAMSE
weights for univariate, right-censored or multivariate data.
The MAMSE weights can be used in a weighted likelihood or to
define a mixture of empirical distribution functions. The package
includes functions for the MAMSE weighted Kaplan-Meier estimate
and for MAMSE weighted ROC curves.

Matrix Analysis and Normalization by Concordant Information
Enhancement
High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach.