An interactive editor built on 'rhandsontable' to allow the 
  interactive viewing, entering, filtering and editing of data in R 
  <https://dillonhammill.github.io/DataEditR/>.

Automated data exploration process for analytic tasks and predictive modeling, so
    that users could focus on understanding data and extracting insights. The package scans and
    analyzes each variable, and visualizes them with typical graphical techniques. Common
    data processing methods are also available to treat and format data.

Real life data is muddy, fuzzy and unpredictable. This makes data manipulations tedious and bringing the data to right shape alone is a major chunk of work. Functions in this package help us get an understanding of dataframes to dramatically reduces data coding time.

Automatic generation of finite state machine models of dynamic 
    decision-making that both have strong predictive power and are 
    interpretable in human terms. We use an efficient model representation and 
    a genetic algorithm-based estimation process to generate simple 
    deterministic approximations that explain most of the structure of complex 
    stochastic processes. We have applied the software to empirical data, and 
    demonstrated it's ability to recover known data-generating processes by 
    simulating data with agent-based models and correctly deriving the 
    underlying decision models for multiple agent models and degrees of
    stochasticity.

Tools to simulate genetic distance matrices, align and compare them via
    multidimensional scaling (MDS) and Procrustes, and evaluate imputation with
    the Bootstrapping Evaluation for Structural Missingness Imputation (BESMI)
    framework. Methods align with Zhu et al. (2025) <doi:10.3389/fpls.2025.1543956>
    and the associated software resource Zhu (2025) <doi:10.26188/28602953>.

Creates synthetic data doubles from real datasets for prototyping,
    teaching, 'shiny' development, and AI-assisted programming. Provides data
    profiling, role detection, configurable synthesis, utility comparison, and
    disclosure-risk warnings. Synthetic outputs are intended to reduce direct
    disclosure risk, not to guarantee privacy.