Visualizes panel data. It has three main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. For details, see <doi:10.18637/jss.v107.i07>.

Leading/lagging a panel, creating dummy variables,
             taking panel differences, looking for panel autocorrelations,
             and more. Implemented via a 'data.table' back end.

Tools to interact with the 'Pangaea' Database
    (<https://www.pangaea.de>), including functions for searching for data,
    fetching 'datasets' by 'dataset' 'ID', and working with the 'Pangaea'
    'OAI-PMH' service.

Provides access to word predictability estimates using large 
             language models (LLMs) based on 'transformer' architectures via 
             integration with the 'Hugging Face' ecosystem 
             <https://huggingface.co/>. The package 
             interfaces with pre-trained neural networks and supports both 
             causal/auto-regressive LLMs (e.g., 'GPT-2') and 
             masked/bidirectional LLMs (e.g., 'BERT') to compute the probability
             of words, phrases, or tokens given their linguistic context. For 
             details on GPT-2 and causal models, see Radford et al. (2019) 
             <https://storage.prod.researchhub.com/uploads/papers/2020/06/01/language-models.pdf>, 
             for details on BERT and masked models, see 
             Devlin et al. (2019) <doi:10.48550/arXiv.1810.04805>. By enabling a
             straightforward estimation of word predictability, the package 
             facilitates research in psycholinguistics, computational 
             linguistics, and natural language processing (NLP).

A central decision in a parametric regression is how to specify the relation between an dependent variable and each explanatory variable. This package provides a semi-parametric tool for comparing different transformations of an  explanatory variables in a parametric regression. The functions is relevant in a situation, where you would use a box-cox or Box-Tidwell transformations.  In contrast to the classic power-transformations, the methods in this package allows for  theoretical driven user input and the possibility to compare with a non-parametric transformation.