Title: Optimal Discriminant Clustering(ODC) and Sparse Optimal
Description: To implement two clustering methods, ODC and SODC, for
clustering datasets using optimal scoring, can also be used as

Title: Making Sophisticated and Aesthetical Figures in R
Description: Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see <http://www.circos.ca>.

Title: Sparsity Oriented Importance Learning
Author: Chenglong Ye <yexxx323@umn.edu>, Yi Yang <yi.yang6@mcgill.ca>, Yuhong Yang <yyang@stat.umn.edu>
Depends: stats, glmnet, ncvreg, MASS, parallel, brglm
Description: Sparsity Oriented Importance Learning (SOIL) provides an objective and informative profile of variable importances for high dimensional regression and classification models.

Imports: stats, glmnet, ncvreg, MASS, parallel, brglm2
Description: Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).

Description: Parentage analysis using Bayes' theorem

Title: SOM Algorithm for the Analysis of Multivariate Environmental
person("Sabina", "Licen", role=c("aut", "cre"), email= "slicen@units.it"),
person("Marco", "Franzon", role=c("aut"), email = "franzonmarco94@gmail.com"),
person("Tommaso", "Rodani", role=c("aut"), email = "tommaso.rodani@gmail.com"),
person("Pierluigi", "Barbieri", role=c("aut"), email = "barbierip@units.it")
Description: Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf>  heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) <doi:10.4209/aaqr.2019.08.0414> are provided.
Imports: rlist, kohonen, shiny, dplyr, plyr, openair, colourpicker,