Function for growing survival trees ensemble ('Naz Gul', 'Nosheen Faiz', 'Dan Brawn', 'Rafal Kulakowski', 'Zardad Khan', and 'Berthold Lausen' (2020) <arXiv:2005.09043>) is given. 
             The trees are grown by the method of random survival forest ('Marvin Wright', 'Andreas Ziegler' (2017) <doi:10.18637/jss.v077.i01>). 
             The survival trees grown are assessed for both individual and collective performances. 
             The ensemble can give promising results on fewer survival trees selected in the final ensemble.

Inspired by 'S-PLUS' function objects.summary(), provides a
    function with the same name that returns data class, storage mode,
    mode, type, dimension, and size information for R objects in the
    specified environment. Various filtering and sorting options are also
    proposed.

Interact seamlessly with Open Target Genetics' GraphQL endpoint to query and retrieve tidy data tables, facilitating the analysis of genetic data. For more information about the Open Target Genetics API (<https://genetics.opentargets.org/api>).

Providing mean partition for ensemble clustering by optimal transport alignment(OTA), uncertainty measures for both partition-wise and cluster-wise assessment and multiple visualization functions to show uncertainty, for instance, membership heat map and plot of covering point set. A partition refers to an overall clustering result. Jia Li, Beomseok Seo, and Lin Lin (2019) <doi:10.1002/sam.11418>. Lixiang Zhang, Lin Lin, and Jia Li (2020) <doi:10.1093/bioinformatics/btaa165>.

Functions for creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin, M., Mahmoud, O., Adler, W., & Lausen, B. (2019). (2019) <doi:10.1007/s11634-019-00364-9>) and class membership probability estimation (Khan, Z, Gul, A, Mahmoud, O, Miftahuddin, M, Perperoglou, A, Adler, W & Lausen, B (2016) <doi:10.1007/978-3-319-25226-1_34>) are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Three different methods of tree selection for the case of classification are given. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.