Supports staggered difference-in-differences designs with
    nonlinear outcomes for both panel and repeated cross-section data.
    Implements estimators for staggered treatment adoption with binary,
    count, and other nonlinear outcomes, extending Callaway and Sant'Anna
    (2021) <doi:10.1016/j.jeconom.2020.12.001> to settings with nonlinear
    outcome models such as logit, probit, and Poisson. For panel data,
    units are followed over time and 'idname' identifies repeated
    observations. For repeated cross-section data, observations are
    independent within each time period; 'idname' is optional and may
    identify survey records or households, but the estimator does not
    require the same units to appear across periods. Repeated cross-section
    estimation includes pooled quasi-maximum likelihood approaches motivated
    by Wooldridge (2023) <doi:10.1093/ectj/utad016>, with optional weighting
    and clustered inference. Methods also draw on Roth and Sant'Anna (2023)
    <doi:10.3982/ECTA19402> and Sant'Anna and Zhao (2020)
    <doi:10.1016/j.jeconom.2020.06.003>.

Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. <doi:10.1109/TVCG.2017.2744018>.

Function and data sets in the book entitled "Nonlinear Time Series Analysis with R Applications" B.Guris (2020). The book will be published in Turkish and the original name of this book will be "R Uygulamali Dogrusal Olmayan Zaman Serileri Analizi". It is possible to perform nonlinearity tests, nonlinear unit root tests, nonlinear cointegration tests and estimate nonlinear error correction models by using the functions written in this package. The Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) type unit root tests can be performed using the functions written. In addition, cointegration tests using the Momentum Threshold Autoregressive (MTAR), the Smooth Threshold Autoregressive (STAR) and the Self Exciting Threshold Autoregressive (SETAR) models can be applied. It is possible to estimate nonlinear error correction models. The Granger causality test performed using nonlinear models can also be applied.