The truncated factor model is a statistical model designed to handle specific data structures in data analysis. 'DTFM' is a powerful tool designed to efficiently process and analyze distributed datasets. The philosophy of the package is described in Guo et al. (2023) <doi:10.1007/s00180-022-01270-z>.

Enhances decision tree visualization by incorporating Generalized
    Association Plots (GAP) through matrix-based visualizations including
    confusion matrix maps, decision tree matrix maps, and predicted class
    membership maps based on supervised correlation and distance metrics.

The Discrete Transmuted Generalized Inverse Weibull (DTGIW) distribution is a new distribution for count data analysis. 
    The DTGIW is a discrete distribution based on Atchanut and Sirinapa (2021). <DOI: 10.14456/sjst-psu.2021.149>.
    In addition, a function for maximum likelihood estimation of the DTGIW distribution is provided.

Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
             modality, that measures diffusion of water in tissues like the human
             brain. The package contains R-functions to process diffusion-weighted
             data. The functionality includes diffusion tensor imaging (DTI),
             diffusion kurtosis imaging (DKI), modeling for high angular resolution
             diffusion weighted imaging (HARDI) using Q-ball-reconstruction and
             tensor mixture models, several methods for structural adaptive
             smoothing including POAS and msPOAS, and a streamline fiber tracking
             for tensor and tensor mixture models.
             The package provides functionality to manipulate and visualize results
             in 2D and 3D.

Create high-performance clinical reporting tables (TLGs) from
    ADaM-like inputs. The package provides a consistent, programmatic API
    to generate common tables such as demographics, adverse event incidence,
    and laboratory summaries, using 'data.table' for fast aggregation over
    large populations. Functions support flexible target-variable selection,
    stratification by treatment, and customizable summary statistics, and
    return tidy, machine-readable results ready to render with downstream
    table/formatting packages in analysis pipelines.