Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1' and 'IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain.

Provides a framework for high-dimensional mediation analysis using transfer learning. The main function TransHDM() integrates large-scale source data to improve the detection power of potential mediators in small-sample target studies. It addresses data heterogeneity via transfer regularization and debiased estimation while controlling the false discovery rate. The package also includes utilities for data generation (gen_simData_homo(), gen_simData_hetero()), baseline methods such as lasso() and dblasso(), sure independence screening via SIS(), and model diagnostics through source_detection(). The methodology is described in Pan et al. (2025) <doi:10.1093/bib/bbaf460>.

Analyse data from longitudinal studies to characterise changes in values of
   semi-quantitative outcome variables within individual subjects, using high performance C++
   code to enable rapid processing of large datasets. A flexible methodology is available for
   codifying these state transitions.

Analyzes transition trajectories in event, sequence, and
    ordered data, focusing on how states follow one another, how far
    processes unfold, and where pathways branch or converge. Trajectories
    are modeled using variable-order prediction suffix trees (Ron,
    Singer, & Tishby, 1996) <doi:10.1023/A:1026490906255>, implemented in
    both frequency-based and prediction-based forms. The framework
    includes multiple pruning, validation, and smoothing techniques to
    ensure model robustness. Visualization options include transition
    trees, radial sunburst diagrams, transition heatmaps, and forward
    trajectory trees.