This package also includes other classical community detection algorithms that are compared to CASCORE in our paper, such as Spectral Clustering On Ratios-of Eigenvectors (SCORE), normalized PCA, ordinary PCA and covariate-assisted spectral clustering (CASC) and ADMM.
Imports: stats, pracma, igraph, reshape2

Title: Detection of Statistically Significant Combinations of SNPs in
person("Matthew", "Baker", role = c("ctr")),
person("Mikołaj", "Rybiński", role = c("ctr")),
person("Uwe", "Schmitt", role = c("ctr")),
person("Dean", "Bodenham", role=c("aut", "cre", "cph"), email="deanbodenhambsse@gmail.com"),
person("Karsten", "Borgwardt", role=c("aut", "fnd", "cph"))
Description: A significant pattern mining-based toolbox for region-based genome-wide association studies and higher-order epistasis analyses, implementing the methods described in Llinares-López et al. (2017) <doi:10.1093/bioinformatics/btx071>.
Author: Felipe Llinares-López [aut, cph],

Authors@R: c(person("Jingyi (Catherine) Shi", "Developer", role = c("aut", "cre", "cph"), email = "jshi@math.msstate.edu"),
person("Jialin Zhang", "Dev", role = "ctb") )
Description: Contains Coverage Adjusted Standardized Mutual Information ('CASMI')-based functions. 'CASMI' is a fundamental concept of a series of methods. For more information about 'CASMI' and 'CASMI'-related methods, please refer to the corresponding publications (for example, a feature selection method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.3390/e21121179>, and a dataset quality measurement method, Shi, J., Zhang, J., & Ge, Y. (2019) <doi:10.1109/ICHI.2019.8904553>) or contact the package author.
Author: Jingyi (Catherine) Shi Developer [aut, cre, cph],
Maintainer: Jingyi (Catherine) Shi Developer <jshi@math.msstate.edu>

Title: 'caret' Applications for Spatial-Temporal Models
Authors@R: c(person("Hanna", "Meyer", email = "hanna.meyer@geo.uni-marburg.de", role = c("cre", "aut")),
person("Chris", "Reudenbach", role = c("ctb")),
person("Thomas", "Nauss", role = c("ctb")))
Author: Hanna Meyer [cre, aut], Chris Reudenbach [ctb], Thomas Nauss [ctb]
Description: Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. This package includes functions to improve spatial-temporal modelling tasks using 'caret'. It prepares data for Leave-Location-Out and Leave-Time-Out cross-validation which are target-oriented validation strategies for spatial-temporal models. To decrease overfitting and improve model performances, the package implements a forward feature selection that selects suitable predictor variables in view to their contribution to the target-oriented performance.