Sensitivity analysis in structural equation modeling using
    influence measures and diagnostic plots. Support leave-one-out casewise
    sensitivity analysis presented by Pek and MacCallum (2011)
    <doi:10.1080/00273171.2011.561068> and approximate casewise influence
    using scores and casewise likelihood. An introduction to
    the package can be found in Cheung and Lai (2026)
    <doi:10.1080/00273171.2026.2634293>.

Specifying 'lavaan' models manually can be time consuming when
    multiple similar models are required. The 'semFromKeys' package streamlines
    the process of running 'lavaan' models by generating model code from simple
    keys lists and running entire collections of models at once.
    The package was inspired by the process used in the code for
    Bainbridge, T. F., Ludeke, S. G., & Smillie, L. D. (2022)
    <doi:10.1037/pspp0000395>.
    The package also optionally checks that identical models have not been run
    on the same data, which saves time when code needs to be run again.

A framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by 'spacyr'. For further reference, see: Stuhler (2022) <doi: 10.1177/00491241221099551>.

Estimate networks and causal relationships in complex systems through
  Structural Equation Modeling. This package also includes functions for importing,
  weight, manipulate, and fit biological network models within the
  Structural Equation Modeling framework as outlined in the Supplementary Material of
  Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.