tag:crantastic.org,2005:/authors/8081Latest activity for Divyansh Agarwal2019-09-23T18:42:26Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/930132019-09-23T18:42:26Z2019-09-23T18:42:26Zmulticross was upgraded to version 2.0.0<a href="/packages/multicross">multicross</a> was <span class="action">upgraded</span> to version <a href="/packages/multicross/versions/88415">2.0.0</a><br /><h3>Package description:</h3><p>We introduce a nonparametric, graphical test based on optimal matching for assessing whether multiple unknown multivariate probability distributions are equal. This method is consistent, and does not make any distributional assumptions on the data. Our procedure combines data that belong to different classes or groups to create a graph on the pooled data, and then utilizes the number of edges connecting data points from different classes to examine equality of distributions among the classes. The functions available through this package implement the work described here: <arXiv:1906.04776>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/892032019-06-03T15:02:09Z2019-06-03T15:02:09Zmulticross was released<a href="/packages/multicross">multicross</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>We introduce a nonparametric, graphical test based on optimal matching for assessing whether multiple unknown multivariate probability distributions are equal. This method is consistent, and does not make any distributional assumptions on the data. Our procedure combines data that belong to different classes or groups to create a graph on the pooled data, and then utilizes the number of edges connecting data points from different classes to examine equality of distributions among the classes. The functions available through this package implement the work described here: <arXiv:1906.04776>.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/840462019-01-25T14:02:55Z2019-01-25T14:02:55ZSemblance was upgraded to version 1.1.0<a href="/packages/Semblance">Semblance</a> was <span class="action">upgraded</span> to version <a href="/packages/Semblance/versions/79902">1.1.0</a><br /><h3>Package description:</h3><p>We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that features probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/757502018-05-30T12:42:34Z2018-05-30T12:42:34ZSemblance was released<a href="/packages/Semblance">Semblance</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that features probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations.</p>crantastic.org