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>.

Functions to pipe data from 'R' to 'DataGraph', a graphing and analysis application for mac OS. Create a live connection using either '.dtable' or '.dtbin' files that can be read by 'DataGraph'. Can save a data frame, collection of data frames and sequences of data frames and individual vectors. For more information see <https://community.visualdatatools.com/datagraph/knowledge-base/r-package/>.

Functions to import multiple files of multiple data file types ('.xlsx', '.xls', '.csv', '.txt')
             from a given directory into R data frames.

Data screening is an important first step of any statistical
    analysis. dataMaid auto generates a customizable data report with a thorough
    summary of the checks and the results that a human can use to identify possible
    errors. It provides an extendable suite of test for common potential
    errors in a dataset.