tag:crantastic.org,2005:/authors/3162Latest activity for Joe Song2018-12-06T14:01:18Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/824232018-12-06T14:01:18Z2018-12-06T14:01:18ZFunChisq was upgraded to version 2.4.5-3<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/78423">2.4.5-3</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for model-free functional dependency using asymptotic chi-square or exact distributions. Functional chi-squares are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. A function index derived from the functional chi-square offers a new effect size measure for the strength of function dependency, a better alternative to conditional entropy in many aspects. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/807492018-10-23T06:01:20Z2018-10-23T06:01:20ZFunChisq was upgraded to version 2.4.5-2<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/77045">2.4.5-2</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for model-free functional dependency using asymptotic chi-square or exact distributions. Functional chi-squares are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/797892018-09-24T14:40:34Z2018-09-24T14:40:34ZCkmeans.1d.dp was upgraded to version 4.2.2<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/76118">4.2.2</a><br /><h3>Package description:</h3><p>Fast optimal univariate clustering and segementation by dynamic programming. Three types of problem including univariate k-means, k-median, and k-segments are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of within-cluster distances using respective metrics. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. Weighted k-means and unweighted k-segments algorithms can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. In contrast to heuristic methods, this package provides a powerful set of tools for univariate data analysis with guaranteed optimality. Use four spaces when indenting paragraphs within the Description.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/764592018-06-16T05:21:18Z2018-06-16T05:21:18ZFunChisq was upgraded to version 2.4.5-1<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/72955">2.4.5-1</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for model-free functional dependency using asymptotic chi-square or exact distributions. Functional chi-squares are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/717852018-02-20T19:41:00Z2018-02-20T19:41:00ZFunChisq was upgraded to version 2.4.5<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/68564">2.4.5</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for model-free functional dependency using asymptotic chi-square or exact distributions. Functional chi-squares are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/712832018-02-06T06:41:01Z2018-02-06T06:41:01ZFunChisq was upgraded to version 2.4.4<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/68079">2.4.4</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for model-free functional dependency using asymptotic chi-square or exact distributions. Functional chi-squares are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependency not possible with symmetrical Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/643452017-07-09T07:00:29Z2017-07-09T07:00:29ZCkmeans.1d.dp was upgraded to version 4.2.1<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/61543">4.2.1</a><br /><h3>Package description:</h3><p>Fast optimal univariate clustering and segementation by dynamic programming. Three types of problem including univariate k-means, k-median, and k-segments are solved with guaranteed optimality and reproducibility. The core algorithm minimizes the sum of within-cluster distances using respective metrics. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. Weighted k-means and unweighted k-segments algorithms can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. In contrast to heuristic methods, this package provides a powerful set of tools for univariate data analysis with guaranteed optimality.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/630472017-05-30T06:00:31Z2017-05-30T06:00:31ZCkmeans.1d.dp was upgraded to version 4.2.0<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/60292">4.2.0</a><br /><h3>Package description:</h3><p>A fast dynamic programming algorithmic framework to achieve optimal univariate k-means, k-median, and k-segments clustering. Minimizing the sum of respective within-cluster distances, the algorithms guarantee optimality and reproducibility. Their advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. Weighted k-means and unweighted k-segments algorithms can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful alternative to heuristic methods for univariate data analysis.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/621072017-05-02T17:00:59Z2017-05-02T17:00:59ZFunChisq was upgraded to version 2.4.3<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/59401">2.4.3</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for non-parametric functional dependencies using asymptotic chi-square or exact distributions. Functional chi-squares are asymmetric and functionally optimal, unique from other related statistics. Tests in this package reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependencies not possible with symmetrical Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/617902017-04-24T06:01:01Z2017-04-24T06:01:01ZFunChisq was upgraded to version 2.4.2<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/59099">2.4.2</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for non-parametric functional dependencies using asymptotic chi-square or exact statistics. Functional chi-squares are asymmetric and functional optimal, unique from other related statistics. These tests reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependencies not possible with symmetrical Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/598502017-03-13T18:05:08Z2017-03-13T18:05:08ZFunChisq was upgraded to version 2.4.0<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/57285">2.4.0</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for non-parametric functional dependencies using asymptotic chi-square or exact statistics. These tests reveal evidence for causality based on the causality-by-functionality principle. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependencies not possible with symmetrical Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/595242017-02-16T17:00:25Z2017-02-16T17:00:25ZCkmeans.1d.dp was upgraded to version 4.0.1<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/56977">4.0.1</a><br /><h3>Package description:</h3><p>A fast dynamic programming algorithm for optimal univariate clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. With optional weights, the algorithm can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful alternative to heuristic methods for univariate data analysis.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/594872017-02-15T15:00:26Z2017-02-15T15:00:26ZCkmeans.1d.dp was upgraded to version 4.0.0<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/56940">4.0.0</a><br /><h3>Package description:</h3><p>A fast dynamic programming algorithm for optimal univariate clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. With optional weights, the algorithm can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful alternative to heuristic methods for univariate data analysis.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/579472017-01-09T16:41:36Z2017-01-09T16:41:36ZCkmeans.1d.dp was upgraded to version 3.4.6-6<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/55469">3.4.6-6</a><br /><h3>Package description:</h3><p>A fast dynamic programming algorithm for optimal univariate clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. With optional weights, the algorithm can also optimally segment time series and perform peak calling. An auxiliary function generates histograms that are adaptive to patterns in data. This package provides a powerful alternative to heuristic methods for univariate data analysis.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/574022016-12-05T14:00:31Z2016-12-05T14:00:31ZCkmeans.1d.dp was upgraded to version 3.4.6-5<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/54953">3.4.6-5</a><br /><h3>Package description:</h3><p>A dynamic programming algorithm for optimal univariate k-means clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic clustering algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. With optional weights, the algorithm can also analyze 1-D signals for segmentation and peak calling. An auxiliary function can generate adaptive histograms to make patterns in data stand out. For univariate data analysis, the package provides a powerful alternative to heuristic clustering algorithms.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/560902016-10-22T10:40:33Z2016-10-22T10:40:33ZCkmeans.1d.dp was upgraded to version 3.4.6-4<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/53796">3.4.6-4</a><br /><h3>Package description:</h3><p>A dynamic programming algorithm for optimal univariate k-means clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic k-means algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. It provides an alternative to heuristic k-means algorithms for univariate clustering.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/555552016-10-05T07:40:22Z2016-10-05T07:40:22ZCkmeans.1d.dp was upgraded to version 3.4.6-3<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/53325">3.4.6-3</a><br /><h3>Package description:</h3><p>A dynamic programming algorithm for optimal univariate k-means clustering. Minimizing the sum of squares of within-cluster distances, the algorithm guarantees optimality and reproducibility. Its advantage over heuristic k-means algorithms in efficiency and accuracy is increasingly pronounced as the number of clusters k increases. It provides an alternative to heuristic k-means algorithms for univariate clustering.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/553182016-09-25T23:00:24Z2016-09-25T23:00:24ZCkmeans.1d.dp was upgraded to version 3.4.6-2<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/53096">3.4.6-2</a><br /><h3>Package description:</h3><p>A dynamic programming algorithm for optimal one-dimensional k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to heuristic k-means algorithms, this method guarantees optimality and reproducibility. Its advantage in efficiency and accuracy over k-means is increasingly pronounced as the number of clusters k increases.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/546342016-09-02T19:40:38Z2016-09-02T19:40:38ZFunChisq was upgraded to version 2.3.3<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/52431">2.3.3</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for non-parametric functional dependencies using asymptotic chi-square or exact statistics. These tests were motivated to reveal evidence for causality based on functional dependencies. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependencies not possible with symmetrical Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/546192016-09-02T01:20:37Z2016-09-02T01:20:37ZFunChisq was upgraded to version 2.3.2<a href="/packages/FunChisq">FunChisq</a> was <span class="action">upgraded</span> to version <a href="/packages/FunChisq/versions/52416">2.3.2</a><br /><h3>Package description:</h3><p>Statistical hypothesis testing methods for non-parametric functional dependencies using asymptotic chi-square or exact statistics. These tests were motivated to reveal evidence for causality based on functional dependencies. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependencies not possible with symmetric Pearson's chi-square or Fisher's exact tests.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/545212016-08-30T06:00:25Z2016-08-30T06:00:25ZCkmeans.1d.dp was upgraded to version 3.4.6-1<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/52324">3.4.6-1</a><br /><h3>Package description:</h3><p>A fast dynamic programming algorithm for optimal univariate k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to heuristic k-means algorithms, this method guarantees optimality and reproducibility. Its advantage in efficiency and accuracy over heuristic k-means clustering is increasingly pronounced as the number of clusters k increases.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/516932016-06-02T04:00:25Z2016-06-02T04:00:25ZCkmeans.1d.dp was upgraded to version 3.4.6<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/49928">3.4.6</a><br /><h3>Package description:</h3><p>A fast dynamic programming algorithm for optimal univariate k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to heuristic k-means algorithms, this method guarantees optimality and reproducibility. Its advantage in efficiency and accuracy over heuristic k-means clustering is increasingly pronounced as the number of clusters k increases.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/513762016-05-19T21:40:20Z2016-05-19T21:40:20ZCkmeans.1d.dp was upgraded to version 3.4.0-1<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/49627">3.4.0-1</a><br /><h3>Package description:</h3><p>A dynamic programming algorithm for optimal one-dimensional k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to heuristic k-means algorithms, this method guarantees optimality and reproducibility. Its advantage in efficiency and accuracy over k-means is increasingly pronounced as the number of clusters k increases.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/510162016-05-08T14:00:25Z2016-05-08T14:00:25ZCkmeans.1d.dp was upgraded to version 3.4.0<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/49293">3.4.0</a><br /><h3>Package description:</h3><p>A dynamic programming algorithm for optimal one-dimensional k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to the standard heuristic k-means algorithm, this algorithm guarantees optimality and repeatability.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/509202016-05-04T07:20:24Z2016-05-04T07:20:24ZCkmeans.1d.dp was upgraded to version 3.3.3<a href="/packages/Ckmeans-1d-dp">Ckmeans.1d.dp</a> was <span class="action">upgraded</span> to version <a href="/packages/Ckmeans-1d-dp/versions/49203">3.3.3</a><br /><h3>Package description:</h3><p>A dynamic programming algorithm for optimal one-dimensional k-means clustering. The algorithm minimizes the sum of squares of within-cluster distances. As an alternative to the standard heuristic k-means algorithm, this algorithm guarantees optimality and repeatability.</p>crantastic.org