Packaged: 2020-03-12 23:07:30 UTC; danielnoble

Title: Automated Intuitive Package for Meta-Ensemble Learning
Description: Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/> of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.
Imports: caret, ggplot2, graphics, e1071, gbm, randomForest
Packaged: 2020-11-14 13:57:10 UTC; AASS Guest

Title: Likelihood inference in meta-analysis and meta-regression
Author: Annamaria Guolo and Cristiano Varin
Maintainer: Annamaria Guolo <annamaria.guolo@univr.it> and Cristiano
Description: first- and higher-order likelihood inference in

Title: Likelihood Inference in Meta-Analysis and Meta-Regression Models
Description: First- and higher-order likelihood inference in

Packaged: 2018-05-29 09:45:40 UTC; cristianovarin

Description: Combines either p-values or modified effect sizes from different studies to find differentially expressed genes
Packaged: Thu Feb 26 14:07:02 2009; ugengma

Description: Combines either p-values or modified effect sizes from
different studies to find differentially expressed genes
Packaged: 2011-08-03 08:14:11 UTC; Guillemette

Suggests: GEOquery, org.Hs.eg.db, VennDiagram, annaffy, hgu133plus2.db,
Description: Combines either p-values or modified effect sizes from different
studies to find differentially expressed genes

Description: Combination of either p-values or modified effect sizes from different
studies to find differentially expressed genes.

Title: Bayesian Mixture Analysis for Metagenomic Community Profiling
Imports: data.table (>= 1.9.2), Matrix, gtools, Rmpi, ggplot2
Description: metaMix resolves complex metagenomic mixtures by analysing
deep sequencing data, using a Mixture Model based approach.
The use of parallel Monte Carlo Markov chains for the exploration
of the species space enables the identification of the set
of species more likely to contribute to the mixture.