Provides implementation of the generic composite similarity measure
    (GCSM) described in Liu et al. (2020) <doi:10.1016/j.ecoinf.2020.101169>. The
    implementation is in C++ and uses 'RcppArmadillo'. Additionally, implementations
    of the structural similarity (SSIM) and the composite similarity measure based
    on means, standard deviations, and correlation coefficient (CMSC), are included.

Multiple matrices/tensors can be specified and decomposed simultaneously by Probabilistic Latent Tensor Factorisation (PLTF). See the reference section of GitHub README.md <https://github.com/rikenbit/gcTensor>, for details of the method.

Provides complete detailed preprocessing of two-dimensional gas chromatogram (GCxGC) samples. Baseline correction, smoothing, peak detection, and peak alignment. Also provided are some analysis functions, such as finding extracted ion chromatograms, finding mass spectral data, targeted analysis, and nontargeted analysis with either the 'National Institute of Standards and Technology Mass Spectral Library' or with the mass data. There are also several visualization methods provided for each step of the preprocessing and analysis.

Geographical detectors for measuring spatial stratified heterogeneity,
             as described in Jinfeng Wang (2010) <doi:10.1080/13658810802443457> and 
             Jinfeng Wang (2016) <doi:10.1016/j.ecolind.2016.02.052>. Includes the
             optimal discretization of continuous data, four primary functions of 
             geographical detectors, comparison of size effects of spatial unit and 
             the visualizations of results. To use the package and to refer the 
             descriptions of the package, methods and case datasets, please cite 
             Yongze Song (2020) <doi:10.1080/15481603.2020.1760434>. The model has 
             been applied in factor exploration of road performance and multi-scale 
             spatial segmentation for network data, as described in 
             Yongze Song (2018) <doi:10.3390/rs10111696> and 
             Yongze Song (2020) <doi:10.1109/TITS.2020.3001193>, respectively.