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).

c(
person(
given = "Tyler",
family = "Richardett",
role = c("aut", "cre"),
email = "tyler.richardett@gmail.com"
),
person(
given = "American Soccer Analysis",
role = "cph",
email = "americansocceranalysis@gmail.com"
)
)
Provides a wrapper around the same API <https://app.americansocceranalysis.com/api/v1/__docs__/>
that powers the American Soccer Analysis app.
Tyler Richardett [aut, cre],
American Soccer Analysis [cph]

Isolation Forest-Based Presence-Only Species Distribution
Modeling
This is a package 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) <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://dl.acm.org/doi/abs/10.5555/3295222.3295230>, 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>.
checkmate, dplyr, ecospat, fastshap, ggplot2, isotree,
methods, ncdf4, outliertree, patchwork, raster, rlang, ROCit,
sf, stars, stats, stringr, tidyselect, utils