The improved trimmed weighted Hochberg procedure provides increased statistical power and relaxes the dependence assumptions for familywise error rate control compared to the original weighted Hochberg procedure. This package computes the boundaries required for implementing the proposed methodology and includes sample size optimization methods.
    See Gou, J., Chang, Y., Li, T., and Zhang, F.(2025). Improved trimmed weighted Hochberg procedures with two endpoints and sample size optimization. Technical Report.

Two functions for running and then post-estimating an Interrupted Time Series Analysis model. This is a solution for running time series analyses on temporally short data. See English (2019) 'The its.analysis R package - Modelling short time series data' <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3398189> for an overview of the method.

GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999)
    as implemented in the R package 'mgcv' (Wood, S.N., 2006; 2011) is a nonlinear
    regression analysis which is particularly useful for time course data such as
    EEG, pupil dilation, gaze data (eye tracking), and articulography recordings,
    but also for behavioral data such as reaction times and response data. As time
    course measures are sensitive to autocorrelation problems, GAMMs implements
    methods to reduce the autocorrelation problems. This package includes functions
    for the evaluation of GAMM models (e.g., model comparisons, determining regions
    of significance, inspection of autocorrelational structure in residuals)
    and interpreting of GAMMs (e.g., visualization of complex interactions, and
    contrasts).

Provides a wrapper around the same API <https://app.americansocceranalysis.com/api/v1/__docs__/>
    that powers the American Soccer Analysis app.

Collection of R functions to do purely presence-only species
    distribution modeling with isolation forest (iForest) and its
    variations such as Extended isolation forest and SCiForest. See the
    details of these methods in references: Liu, F.T., Ting, K.M. and
    Zhou, Z.H. (2008) <doi:10.1109/ICDM.2008.17>, Hariri, S., Kind, M.C.
    and Brunner, R.J. (2019) <doi:10.1109/TKDE.2019.2947676>, Liu, F.T.,
    Ting, K.M. and Zhou, Z.H. (2010) <doi:10.1007/978-3-642-15883-4_18>,
    Guha, S., Mishra, N., Roy, G. and Schrijvers, O. (2016)
    <https://proceedings.mlr.press/v48/guha16.html>, Cortes, D. (2021)
    <doi:10.48550/arXiv.2110.13402>. 
    Additionally, Shapley values are used to explain
    model inputs and outputs. See details in references: Shapley, L.S.
    (1953) <doi:10.1515/9781400881970-018>, Lundberg, S.M. and Lee, S.I.
    (2017) <https://dm-gatech.github.io/CS8803-Fall2018-DML-Papers/shapley.pdf>, Molnar,
    C.  (2020) <ISBN:978-0-244-76852-2>, Štrumbelj, E. and Kononenko, I.
    (2014) <doi:10.1007/s10115-013-0679-x>. itsdm also provides functions
    to diagnose variable response, analyze variable importance, draw
    spatial dependence of variables and examine variable contribution. As
    utilities, the package includes a few functions to download
    bioclimatic variables including 'WorldClim' version 2.0 (see Fick,
    S.E. and Hijmans, R.J. (2017) <doi:10.1002/joc.5086>) and
    'CMCC-BioClimInd' (see Noce, S., Caporaso, L. and Santini, M. (2020)
    <doi:10.1038/s41597-020-00726-5>.