tag:crantastic.org,2005:/authors/2915Latest activity for Atsushi Kawaguchi2019-09-01T13:22:14Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/922402019-09-01T13:22:14Z2019-09-01T13:22:14Zmsma was upgraded to version 2.0<a href="/packages/msma">msma</a> was <span class="action">upgraded</span> to version <a href="/packages/msma/versions/87728">2.0</a><br /><h3>Package description:</h3><p>Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/891492019-06-02T12:22:04Z2019-06-02T12:22:04Zmsma was upgraded to version 1.2<a href="/packages/msma">msma</a> was <span class="action">upgraded</span> to version <a href="/packages/msma/versions/84750">1.2</a><br /><h3>Package description:</h3><p>Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/745602018-05-04T04:02:03Z2018-05-04T04:02:03Zmsma was upgraded to version 1.1<a href="/packages/msma">msma</a> was <span class="action">upgraded</span> to version <a href="/packages/msma/versions/71175">1.1</a><br /><h3>Package description:</h3><p>Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/720772018-03-01T12:41:53Z2018-03-01T12:41:53Zmsma was upgraded to version 1.0<a href="/packages/msma">msma</a> was <span class="action">upgraded</span> to version <a href="/packages/msma/versions/68826">1.0</a><br /><h3>Package description:</h3><p>Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/461982016-01-01T20:51:24Z2016-01-01T20:51:24Zmsma was released<a href="/packages/msma">msma</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/440622015-10-01T11:52:00Z2015-10-01T11:52:00Zsanon was upgraded to version 1.5<a href="/packages/sanon">sanon</a> was <span class="action">upgraded</span> to version <a href="/packages/sanon/versions/43854">1.5</a><br /><h3>Package description:</h3><p>There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/367702014-09-04T06:12:11Z2014-09-04T06:12:11Zsanon was upgraded to version 1.4<a href="/packages/sanon">sanon</a> was <span class="action">upgraded</span> to version <a href="/packages/sanon/versions/34849">1.4</a><br /><h3>Package description:</h3><p>This package has several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/341082013-07-09T07:51:08Z2013-07-09T07:51:08Zsanon was upgraded to version 1.1<a href="/packages/sanon">sanon</a> was <span class="action">upgraded</span> to version <a href="/packages/sanon/versions/29159">1.1</a><br /><h3>Package description:</h3><p>This package has several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/322542013-04-30T04:31:30Z2013-04-30T04:31:30Zsanon was released<a href="/packages/sanon">sanon</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.</p>crantastic.org