R Packages
To install a package on GitHub, run the following code on the R console. Note that the following code may need to be run with administrative privileges.
install.packages("devtools") ## if the devtools package is not installed
library(devtools)
install_github("michioyamamoto/grc") ## Replace the "grc" with another package name.- fkms: Functional K-means Clustering
- R functions for the functional k-means clustering developed by Yamamoto and Terada (2024), which estimates an optimal cluster structure of longitudinal data. This package efficiently estimates cluster structures even for irregular data with measurement points varying across individuals or sparse data that include subjects with extremely limited observations. This is available on GitHub.
- R functions for the functional k-means clustering developed by Yamamoto and Terada (2024), which estimates an optimal cluster structure of longitudinal data. This package efficiently estimates cluster structures even for irregular data with measurement points varying across individuals or sparse data that include subjects with extremely limited observations. This is available on GitHub.
- fgrc: Functional Generalized Reduced Clustering
- R functions for the functional generalized reduced clustering developed by Yamamoto and Hwang (2017), which estimates an optimal cluster structure of functions as well as an optimal subspace for clustering simultaneously. This package also includes functions for functional principal component k-means (FPCK; Yamamoto, 2012) and functional factorial k-means (FFKM; Yamamoto and Terada, 2014). All of these methods can find clusters from multivariate functional data. This is available on GitHub. Note that “grc” package must be installed beforehand in order to install this package.
- R functions for the functional generalized reduced clustering developed by Yamamoto and Hwang (2017), which estimates an optimal cluster structure of functions as well as an optimal subspace for clustering simultaneously. This package also includes functions for functional principal component k-means (FPCK; Yamamoto, 2012) and functional factorial k-means (FFKM; Yamamoto and Terada, 2014). All of these methods can find clusters from multivariate functional data. This is available on GitHub. Note that “grc” package must be installed beforehand in order to install this package.
- grc: Generalized Reduced Clustering
- R functions for the generalized reduced clustering developed by Yamamoto and Hwang (2014), which estimates an optimal subspace of multi-dimensional variables for identifying a cluster structure of objects. This is available on GitHub.
- R functions for the generalized reduced clustering developed by Yamamoto and Hwang (2014), which estimates an optimal subspace of multi-dimensional variables for identifying a cluster structure of objects. This is available on GitHub.
- cbird: Clustering of Multivariate Binary Data with Dimension Reduction via L1-Regularized Likelihood Maximization
- R functions for the clustering of multivariate binary data with reducing the dimensionality (CLUSBIRD) proposed by Yamamoto and Hayashi (2015), which estimates cluster structure in multivariate binary data as well as low-dimensional scores for subjects based on the estimated cluster structure. The package is available on GitHub.
- R functions for the clustering of multivariate binary data with reducing the dimensionality (CLUSBIRD) proposed by Yamamoto and Hayashi (2015), which estimates cluster structure in multivariate binary data as well as low-dimensional scores for subjects based on the estimated cluster structure. The package is available on GitHub.
- obliclus: A Cluster-Based Factor Rotation Technique
- Yamamoto and Jennrich (2013) proposed factor rotation techniques to identify a simple and well-clustered structure in a factor loading matrix. The package is available on GitHub.
- Yamamoto and Jennrich (2013) proposed factor rotation techniques to identify a simple and well-clustered structure in a factor loading matrix. The package is available on GitHub.
- fanc: Sparse Estimation via Non-Concave Penalized Likelihood in Factor Analysis Model
- Functions for the penalized maximum likelihood estimates of factor loadings and unique variances for various tuning parameters proposed by Hirose and Yamamoto (2014, 2015). This package also includes a graphical tool that outputs a path diagram, goodness-of-fit indices, and model selection criteria for each regularization parameter. The graphical tool has been introduced by Yamamoto, Hirose, and Nagata (2017). This is available on CRAN, and the maintainer is Dr. Kei Hirose.