Compute the fixed effects dynamic panel threshold model suggested by Ramírez-Rondán (2020) <doi:10.1080/07474938.2019.1624401>, and dynamic panel linear model suggested by Hsiao et al. (2002) <doi:10.1016/S0304-4076(01)00143-9>, where maximum likelihood type estimators are used. Multiple thresholds estimation based on Markov Chain Monte Carlo (MCMC) is allowed, and model selection of linear model, threshold model and multiple threshold model is also allowed.

In the context of data quality assessment, this package provides a number of functions for evaluating data quality across various dimensions, including completeness, plausibility, concordance, conformance, currency, timeliness, and correctness. It has been developed based on two well-known frameworks—Michael G. Kahn (2016) <doi:10.13063/2327-9214.1244> and Nicole G. Weiskopf (2017) <doi:10.5334/egems.218>—for data quality assessment. Using this package, users can evaluate the quality of their datasets, provided that corresponding metadata are available.

A graphical user interface (GUI) to the functions implemented
    in the R package 'DQAstats'. Publication: Mang et al. (2021)
    <doi:10.1186/s12911-022-01961-z>.

Perform data quality assessment ('DQA') of electronic health
    records ('EHR'). Publication: Kapsner et al. (2021)
    <doi:10.1055/s-0041-1733847>.

Automates quality verification of recurring external dataset deliveries.
    For each new file arrival, it runs single-snapshot quality checks, compares
    the file to the previous delivery, writes a self-contained 'HTML' report, and
    records summary statistics in a local 'SQLite' database for long-term trend
    tracking. Supports 'CSV' and fixed-width formats. Custom organisation-specific
    checks can be supplied as plain R files.

A graphical user interface for the 'dqcheckr' package. Provides a
    point-and-click 'shiny' application for configuring dataset quality checks,
    running them against recurring file deliveries, and browsing historical
    check results — without writing any R code. The package is feature-complete
    and is now maintained for corrections only; configuration features are
    developed in 'dqcheckr' itself, which offers a script-based workflow that
    does not need this interface.