Description: This is a collection of fast tools for application in quantitative genetics. For instance, the SNP matrix can be stored in a minimum of memory  and the calculation of the genomic relationship matrix is based on a rapid algorithm. It also contains the window scanning approach by Kabluchko and Spodarev (2009), <doi:10.1239/aap/1240319575> to detect anomalous genomic areas <doi:10.1186/s12864-018-5009-y>. Furthermore, the package is used in the Modular Breeding Program Simulator (MoBPS, <https://github.com/tpook92/MoBPS>, <https://www.mobps.de/>). The tools are based on SIMD (Single Instruction Multiple Data, <https://en.wikipedia.org/wiki/SIMD>) and OMP (Open Multi-Processing, <https://de.wikipedia.org/wiki/OpenMP>).
Packaged: 2021-09-20 20:25:48 UTC; schlather

Packaged: 2019-09-07 14:23:58 UTC; schlather

Title: Minimalist Async Evaluation Framework for R
Description: An extremely simple and lightweight method for concurrent /
parallel code execution, built on the 'nanonext' R package and 'NNG'

Description: Extremely simple and lightweight method for concurrent /
parallel code execution, built on 'nanonext' and 'NNG' (Nanomsg Next Gen)

Description: Simple and lightweight parallelism and concurrent code execution,
local or distributed across the network, built on 'nanonext' and 'NNG'

Description: Lightweight parallel code execution, local or distributed across
the network. Designed for simplicity, a 'mirai' evaluates an arbitrary
expression asynchronously, resolving automatically upon completion.
Built on 'nanonext' and 'NNG' (Nanomsg Next Gen) scalability protocols,
defaults to the optimal choice of abstract sockets, Unix domain sockets or

Package: mirf
Version: 1.0
Date: 2008-09-03
Title: MULTIPLE IMPUTATION AND RANDOM FORESTS FOR UNOBSERVABLE PHASE, HIGH-DIMENSIONAL DATA
Author: Yimin Wu, B. Aletta S. Nonyane and Andrea S. Foulkes
Maintainer: Yimin Wu <yiminwu@cs.umass.edu>
Depends: R (>= 2.5.1), haplo.stats, randomForest
Description: This package applies a combination of missing haplotype imputation via the EM algorithm of Excoffier and
Slatkin(1995) and modelling trait-haplotype associations via the Random Forest algorithm. The EM algorithm
is implemented by the function haplo.em (of the haplo.stats package) and the Random Forest algorithm is
implemented by the randomForest function (of the randomForest package). This method is described in the
published manuscript: B.A.S. Nonyane and A.S. Foulkes (2007) Multiple imputation and random forests (MIRF) for
unobservable high-dimensional data. The International Journal of Biostatistics 3(1): Article 12.
License: BSD
Citation:Support for developing this R package was provided by the National Institute of Allergy and Infectious Diseases (NIAID) research award (PI:Foulkes, R01 AI056983).
URL:http://www-unix.oit.umass.edu/~foulkes/
Packaged: Tue Sep  9 14:51:00 2008; Yimin