The portmanteau local feature discriminant approach first identifies the local 
    discriminant features and their differential structures, then constructs the 
    discriminant rule by pooling the identified local features together. This method 
    is applicable to high-dimensional matrix-variate data. 
    See the paper by Xu, Luo and Chen (2023, <doi:10.1007/s13171-021-00255-2>).

Functions for estimating probabilistic latent feature models with a disjunctive, conjunctive or additive mapping rule on (aggregated) binary three-way data.

Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.

Sequential Monte Carlo (SMC) inference for fully Bayesian
  Gaussian process (GP) regression and classification models by
  particle learning (PL) following Gramacy & Polson (2011) <doi:10.48550/arXiv.0909.5262>.
  The sequential nature of inference
  and the active learning (AL) hooks provided facilitate thrifty 
  sequential design (by entropy) and optimization
  (by improvement) for classification and
  regression models, respectively.
  This package essentially provides a generic
  PL interface, and functions (arguments to the interface) which
  implement the GP models and AL heuristics.  Functions for 
  a special, linked, regression/classification GP model and 
  an integrated expected conditional improvement (IECI) statistic 
  provide for optimization in the presence of unknown constraints.
  Separable and isotropic Gaussian, and single-index correlation
  functions are supported.
  See the examples section of ?plgp and demo(package="plgp") 
  for an index of demos.

Plots with high flexibility and easy handling, including
  informative regression diagnostics for many models.

Tools for both single and batch image manipulation and
    analysis (Olivoto, 2022 <doi:10.1111/2041-210X.13803>) and
    phytopathometry (Olivoto et al., 2022 <doi:10.1007/S40858-021-00487-5>).
    The tools can be used for the quantification of leaf area, object
    counting, extraction of image indexes, shape measurement, object
    landmark identification, and Elliptical Fourier Analysis of object
    outlines (Claude (2008) <doi:10.1007/978-0-387-77789-4>). The package
    also provides a comprehensive pipeline for generating shapefiles with
    complex layouts and supports high-throughput phenotyping of RGB,
    multispectral, and hyperspectral orthomosaics. This functionality
    facilitates field phenotyping using UAV- or satellite-based imagery.