The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed 'MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The 'MantaID' model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. 'MantaID' supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and APIs were also developed for 'MantaID' to improve applicability. To our knowledge, 'MantaID' is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.

This is the core package for the many packages universe.
    It includes functions to help researchers work with and contribute to 
    event datasets on global governance.

Frequently one needs a convenient way to build and tune
             several models in one go.The goal is to provide a number of machine learning convenience 
             functions. It provides the ability to build, tune and obtain predictions of 
             several models in one function. The models are built using functions from
             'caret' with easier to read syntax.
             Kuhn(2014) <arXiv:1405.6974>.

Computes indirect effects, conditional effects, and conditional
  indirect effects in a structural equation model or path model after model
  fitting, with no need to define any user parameters or label any paths in
  the model syntax, using the approach presented in Cheung and Cheung
  (2023) <doi:10.3758/s13428-023-02224-z>. Can also form bootstrap
  confidence intervals by doing bootstrapping only once and reusing the
  bootstrap estimates in all subsequent computations. Supports bootstrap
  confidence intervals for standardized (partially or completely) indirect
  effects, conditional effects, and conditional indirect effects as described
  in Cheung (2009) <doi:10.3758/BRM.41.2.425> and Cheung, Cheung, Lau, Hui,
  and Vong (2022) <doi:10.1037/hea0001188>. Model fitting can be done by
  structural equation modeling using lavaan() or regression using lm().