An engaging collection of datasets from Portland Parks and Recreation. The city of Portland inventoried every tree in over 170 parks and along the streets in 96 neighborhoods.

Linear dynamic panel data modeling based on linear and
    nonlinear moment conditions as proposed by
    Holtz-Eakin, Newey, and Rosen (1988) <doi:10.2307/1913103>,
    Ahn and Schmidt (1995) <doi:10.1016/0304-4076(94)01641-C>,
    and Arellano and Bover (1995) <doi:10.1016/0304-4076(94)01642-D>.
    Estimation of the model parameters relies on the Generalized
    Method of Moments (GMM) and instrumental variables (IV) estimation,
    numerical optimization (when nonlinear moment conditions are
    employed) and the computation of closed form solutions (when
    estimation is based on linear moment conditions). One-step,
    two-step and iterated estimation is available. For inference
    and specification
    testing, Windmeijer (2005) <doi:10.1016/j.jeconom.2004.02.005>
    and doubly corrected standard errors
    (Hwang, Kang, Lee, 2021 <doi:10.1016/j.jeconom.2020.09.010>)
    are available. Additionally, serial correlation tests, tests for
    overidentification, and Wald tests are provided. Functions for
    visualizing panel data structures and modeling results obtained
    from GMM estimation are also available. The plot methods include
    functions to plot unbalanced panel structure, coefficient ranges
    and coefficient paths across GMM iterations (the latter is
    implemented according to the plot shown in
    Hansen and Lee, 2021 <doi:10.3982/ECTA16274>).
    For a more detailed description of the GMM-based functionality,
    please see Fritsch, Pua, Schnurbus (2021) <doi:10.32614/RJ-2021-035>.
    For more detail on the IV-based estimation routines,
    see Fritsch, Pua, and Schnurbus (WP, 2024) and
    Han and Phillips (2010) <doi:10.1017/S026646660909063X>.

These are useful tools and data sets for the study of quantitative 
    peace science. The goal for this package is to include tools and data sets
    for doing original research that mimics well what a user would have to previously
    get from a software package that may not be well-sourced or well-supported.
    Those software bundles were useful the extent to which they encourage replications 
    of long-standing analyses by starting the data-generating process from scratch. However, 
    a lot of the functionality can be done relatively quickly and more transparently
    in the R programming language.