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ANOVA Bayesian Bioinformatics Bioinformatics & Biostatistics Biostatistics C++ Cancer Chemoinformatics Classification Clustering Copynumber DNA Ecology Economics Finance GUI Genetic Algorithms Genetics HTML Machine Learning Machine learning Mixture Multiple Comparisons Multivariate Analysis Multivariate Regression Multivariate Techniques Next generation Sequencing ODE Phylogeny R R-Forge Rcmdr Regression SNP Statistics Time series Visualization Whole-genome bioinformatics biostatistics break detection categorical change detection classification clustering community ecology data mining database datasets dissimilarity distance distributions diversity dynamic systems ecological models ecology econometrics economics epidemiology finance gene expression generalized linear models genetics graphics high dimentional data likelihood linear models linear programming machine learning microarray missing data missing values mixed effect models mixed model model comparison model estimation model selection movement multivariate multivariate regression multivariate statistics non-parametrics nonlinear models nonparametric optimization parametric model permutation tests phylogeny plotting political analysis prediction psychology raster regression remote sensing reporting robust robust statistics simulation soil spatial spatial autocorrelation spatial classes spatial data spatial methods spatial point patterns spatial regression spatio-temporal species distribution models survival teaching text mining time series visualization

5 projects in result set.
Low Rank Gaussian Process Regression - Fit a Low Rank Gaussian Process Regression / Linear Mixed Model for large datasets. These models are widely used in statistical genetics as a test of association while correcting for the confounding effects of kinship and population structure.
Tags: C++, Genetics, high performance computing, R, Regression, parallel computing

Activity Percentile: 0
Activity Ranking: 0
Registered: 2013-12-19 15:12

Survival analysis with BART - Bayesian additive regression trees (BART) have been shown to provide flexible nonparametric modeling of covariates to binary and continuous outcomes. This R package extends BART to time-to-event outcomes with right censoring.
Tags: nonproportional hazards, Bayesian nonparametric, parallel computing, survival

Activity Percentile: 0
Activity Ranking: 0
Registered: 2016-01-06 21:54

cgen - Parallel Genomic Prediction and GWAS in R using Eigen and Rcpp
Tags: C++, Genetics, mixed model, openmp, parallel computing, Bioinformatics, Bioinformatics & Biostatistics, Biostatistics, Gibbs sampling, Regression

Activity Percentile: 0
Activity Ranking: 0
Registered: 2013-05-29 18:34

parallelstructure - Package to provide a R framework to make use of multi-core computers when running analysis in the population genetics software STRUCTURE.
Tags: population genetics, parallel computing

Activity Percentile: 0
Activity Ranking: 0
Registered: 2013-03-26 16:28

timebart: survival analysis with BART - Bayesian additive regression trees (BART) have been shown to provide flexible nonparametric modeling of covariates to binary and continuous outcomes. This R package extends BART to time-to-event outcomes with right censoring.
Tags: Bayesian nonparametric, nonproportional hazards, parallel computing, survival

Activity Percentile: 0
Activity Ranking: 0
Registered: 2016-07-14 21:42

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