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) <doi:10.48550/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
  (2024) <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().

Converts results from the 'manymome' package,
  presented in Cheung and Cheung (2023)
  <doi:10.3758/s13428-023-02224-z>, to publication-ready
  tables.

Many tools for making, modifying, marking, measuring, 
   and motifs and memberships of many different types of networks.
   All functions operate with matrices, edge lists, and 'igraph', 'network', and 'tidygraph' objects,
   on directed, multiplex, multimodal, signed, and other networks.
   The package includes functions for importing and exporting, creating and generating networks,
   modifying networks and node and tie attributes,
   and describing networks with sensible defaults.

Comprehensively identifying states and state-like actors is difficult.
    This package provides data on states and state-like entities in the international system across time.
    The package combines and cross-references several existing datasets consistent with the aims and functions of the manydata package.
    It also includes functions for identifying state references in text, and for generating fictional state names.