tag:crantastic.org,2005:/authors/6163Latest activity for Pariya Behrouzi2019-10-15T15:22:01Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/937332019-10-15T15:22:01Z2019-10-15T15:22:01Znetgwas was upgraded to version 1.11<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/89101">1.11</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on copula graphical models for accomplishing the three interrelated goals in genetics and genomics in an unified way: (1) linkage map construction, (2) constructing linkage disequilibrium networks, and (3) exploring high-dimensional genotype-phenotype network and genotype- phenotype-environment interactions networks. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is considerably larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent methodological developments in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <doi:10.1093/bioinformatics/bty777>. NOTICE proper functionality of 'netgwas' requires that the 'RBGL' package is installed from 'bioconductor'; for installation instruction please refer to the 'RBGL' web page given below.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/877072019-04-26T10:22:03Z2019-04-26T10:22:03ZnutriNetwork was released<a href="/packages/nutriNetwork">nutriNetwork</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Statistical tool for learning the structure of direct associations among variables for continuous data, discrete data and mixed discrete-continuous data. The package is based on the copula graphical model in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/872172019-04-12T14:22:06Z2019-04-12T14:22:06Znetgwas was upgraded to version 1.10<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/82914">1.10</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. We use conditional independence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <arXiv:1710.01063>. NOTICE proper functionality of 'netgwas' requires that the 'RBGL' package is installed from 'bioconductor'; for installation instruction please refer to the 'RBGL' web page given below.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/865722019-03-26T15:22:00Z2019-03-26T15:22:00Znetgwas was upgraded to version 1.9<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/82298">1.9</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. We use conditional independence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <arXiv:1710.01063>. NOTICE proper functionality of 'netgwas' requires that the 'RBGL' package is installed from 'bioconductor'; for installation instruction please refer to the 'RBGL' web page given below.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/849092019-02-18T15:02:27Z2019-02-18T15:02:27Zpcgen was upgraded to version 0.2.0<a href="/packages/pcgen">pcgen</a> was <span class="action">upgraded</span> to version <a href="/packages/pcgen/versions/80720">0.2.0</a><br /><h3>Package description:</h3><p>Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm, with specific conditional independence tests and modified orientation rules. pcgen extends the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of direct genetic effects.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/848612019-02-16T23:42:08Z2019-02-16T23:42:08Znetgwas was upgraded to version 1.8.1<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/80675">1.8.1</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. We use conditional independence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <arXiv:1710.01063>. NOTICE proper functionality of 'netgwas' requires that the 'RBGL' package is installed from 'bioconductor'; for installation instruction please refer to the 'RBGL' web page given below.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/785062018-08-13T10:22:14Z2018-08-13T10:22:14Znetgwas was upgraded to version 1.7.0<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/74860">1.7.0</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. For this purpose, we use conditional (in)dependence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <arXiv:1710.01063>. NOTICE proper functionality of 'netgwas' requires that the 'RBGL' package is installed from 'bioconductor'; for installation instruction please refer to the 'RBGL' web page given below.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/781442018-08-10T16:52:08Z2018-08-10T16:52:08Zpcgen was released<a href="/packages/pcgen">pcgen</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Implements the pcgen algorithm, which is a modified version of the standard pc-algorithm, with specific conditional independence tests and modified orientation rules. pcgen extends the approach of Valente et al. (2010) <doi:10.1534/genetics.109.112979> with reconstruction of direct genetic effects.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/750822018-05-16T16:02:01Z2018-05-16T16:02:01Znetgwas was upgraded to version 1.5.0<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/71684">1.5.0</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. For this purpose, we use conditional (in)dependence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <arXiv:1710.00894> and Behrouzi and Wit (2017) <arXiv:1710.01063>. NOTICE proper functionality of 'netgwas' requires that the 'RBGL' package is installed from 'bioconductor'; for installation instruction please refer to the 'RBGL' web page given below.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/734402018-04-06T17:02:08Z2018-04-06T17:02:08Znetgwas was upgraded to version 0.1.4.2<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/70118">0.1.4.2</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. For this purpose, we use conditional (in)dependence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <arXiv:1710.00894> and Behrouzi and Wit (2017) <arXiv:1710.01063> .</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/724802018-03-13T11:42:03Z2018-03-13T11:42:03Znetgwas was upgraded to version 0.1.3.2<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/69210">0.1.3.2</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. For this purpose, we use conditional (in)dependence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <arXiv:1710.00894> and Behrouzi and Wit (2017) <arXiv:1710.01063> .</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/713382018-02-07T16:01:59Z2018-02-07T16:01:59Znetgwas was upgraded to version 0.1.3.1<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/68131">0.1.3.1</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. For this purpose, we use conditional (in)dependence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <arXiv:1710.00894> and Behrouzi and Wit (2017) <arXiv:1710.01063> .</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/713342018-02-07T14:21:55Z2018-02-07T14:21:55Znetgwas was upgraded to version 0.1.3.0<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/68127">0.1.3.0</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. For this purpose, we use conditional (in)dependence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <arXiv:1710.00894> and Behrouzi and Wit (2017) <arXiv:1710.01063> .</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/703272018-01-12T15:41:35Z2018-01-12T15:41:35Znetgwas was upgraded to version 0.1.2.0<a href="/packages/netgwas">netgwas</a> was <span class="action">upgraded</span> to version <a href="/packages/netgwas/versions/67176">0.1.2.0</a><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on undirected graphical models for accomplishing three important and interrelated goals in genetics: (1) linkage map construction, (2) reconstructing intra- and inter-chromosomal conditional interactions (linkage disequilibrium) networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-phenotype-environment interactions network. For this purpose, we use conditional (in)dependence relationships between variables. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent developments in Behrouzi and Wit (2017) <arXiv:1710.00894> and Behrouzi and Wit (2017) <arXiv:1710.01063> .</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/690752017-12-01T18:41:25Z2017-12-01T18:41:25Znetgwas was released<a href="/packages/netgwas">netgwas</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>A multi-core R package that contains a set of tools based on copula graphical models for accomplishing the three interrelated goals in genetics and genomics in an unified way: (1) linkage map construction, (2) constructing linkage disequilibrium networks, and (3) exploring high-dimensional genotype-phenotype network and genotype- phenotype-environment interactions networks. The netgwas package can deal with biparental inbreeding and outbreeding species with any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes), tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where number of variables p is considerably larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output. The package is implemented the recent methodological developments in Behrouzi and Wit (2017) <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <doi:10.1093/bioinformatics/bty777>. NOTICE proper functionality of 'netgwas' requires that the 'RBGL' package is installed from 'bioconductor'; for installation instruction please refer to the 'RBGL' web page given below.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/570242016-11-23T09:00:39Z2016-11-23T09:00:39Zepistasis was upgraded to version 0.0.1-1<a href="/packages/epistasis">epistasis</a> was <span class="action">upgraded</span> to version <a href="/packages/epistasis/versions/54629">0.0.1-1</a><br /><h3>Package description:</h3><p>An efficient multi-core package to reconstruct an underlying network of genomic signatures of high-dimensional epistatic selection from partially observed genotype data. The phenotype that we consider is viability. The network captures the conditional dependent short- and long-range linkage disequilibrium structure of genomes and thus reveals aberrant marker-marker associations that are due to epistatic selection. We target on high-dimensional genotype data where number of variables (markers) is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/565132016-11-07T16:40:32Z2016-11-07T16:40:32Zepistasis was released<a href="/packages/epistasis">epistasis</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>An efficient multi-core package to reconstruct an underlying network of genomic signatures of high-dimensional epistatic selection from partially observed genotype data. The phenotype that we consider is viability. The network captures the conditional dependent short- and long-range linkage disequilibrium structure of genomes and thus reveals aberrant marker-marker associations that are due to epistatic selection. We target on high-dimensional genotype data where number of variables (markers) is larger than number of sample sizes (p >> n). The computations is memory-optimized using the sparse matrix output.</p>crantastic.org