Calculate several understandability metrics of BPMN models. BPMN stands for business process modelling notation and is a language for expressing business processes into business process diagrams. Examples of these understandability metrics are: average connector degree, maximum connector degree, sequentiality, cyclicity, diameter, depth, token split, control flow complexity, connector mismatch, connector heterogeneity, separability, structuredness and cross connectivity. See R documentation and paper on metric implementation included in this package for more information concerning the metrics.

A framework for estimating difference-in-differences with
 unpoolable data, based on Karim, Webb, Austin, and Strumpf (2024)
 <doi:10.48550/arXiv.2403.15910>. Supports common or staggered adoption, multiple
 groups, and the inclusion of covariates. Also computes p-values for the
 aggregate average treatment effect on the treated via the
 randomization inference procedure described in MacKinnon and Webb (2020)
 <doi:10.1016/j.jeconom.2020.04.024>.

Detects values imported from spreadsheets that were auto-converted 
    to Excel date serials and reconstructs the originally intended day.month
    decimals (for example, '30.3' that Excel displayed as '30/03/2025').
    The functions work in a vectorized manner, preserve non-serial values, and 
    support both the 1900 and 1904 date systems.

A variational mapping approach that reveals and expands future temporal dynamics from folded high-dimensional geometric distance spaces, unfold turns a set of time series into a 4D block of pairwise distances between reframed windows, learns a variational mapper that maps those distances to the next reframed window, and produces horizon-wise predictive functions for each input series. In short: it unfolds the future path of each series from a folded geometric distance representation.

Use syntax inspired by the package 'glue' to extract matched substrings in a more intuitive and compact way than by using standard regular expressions.