We extend two general methods of moment estimators to panel vector 
    autoregression models (PVAR) with p lags of endogenous variables, predetermined 
    and strictly exogenous variables. This general PVAR model contains the first 
    difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, 
    Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator 
    by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also 
    provide specification tests (Hansen overidentification test, lag selection 
    criterion and stability test of the PVAR polynomial) and classical structural 
    analysis for PVAR models such as orthogonal and generalized impulse response 
    functions, bootstrapped confidence intervals for impulse response analysis and 
    forecast error variance decompositions.

Visualizes panel data. It has four 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; (4) it displays the network structure of multi-way fixed effects as a k-partite graph, identifying connected components, singletons, and duplicate observations. 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).