Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) <doi:10.5220/0005595502260234>. Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package.

Provides Stable Diffusion image generation using the 'ggmlR'
    library, with no 'Python' or external API dependencies. Supports
    text-to-image and image-to-image generation for SD 1.x, SD 2.x, 'SDXL',
    Flux, and 'FLUX.2'. A single sd_generate() function handles the entire pipeline,
    including sampling and high-resolution output. Features multi-GPU support,
    a 'Shiny' GUI, and runs on CPU or 'Vulkan' GPU across Linux, macOS, and
    Windows.

Provides an efficient framework for 
   high-dimensional linear and diagonal discriminant analysis with 
   variable selection.  The classifier is trained using James-Stein-type 
   shrinkage estimators and predictor variables are ranked using 
   correlation-adjusted t-scores (CAT scores).  Variable selection error 
   is controlled using false non-discovery rates or higher criticism.

Functions and Datasets from Lohr, S. (1999), Sampling:
        Design and Analysis, Duxbury.

We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2023), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <doi:10.1080/01621459.2021.1945459>. Some optional functionality uses the archived R packages ‘huge’ and ‘pfa’, which are not available from CRAN’s main repositories. Users who need this optional functionality can obtain them from the CRAN Archive as follows: ‘huge’ at <https://cran.r-project.org/src/contrib/Archive/huge/>; ‘pfa’ at <https://cran.r-project.org/src/contrib/Archive/pfa/>.