c(
person(given = "Ian G.", family = "Mackenzie", email="ian.mackenzie@uni-tuebingen.de", role = c("cre", "aut")),
person(given = "Carolin", family = "Dudschig", email="carolin.dudschig@uni-tuebingen.de", role = c("aut"))
)
Ian G. Mackenzie [cre, aut],
Carolin Dudschig [aut]

DEoptim, Rcpp (>= 0.12.16), dplyr (>= 1.0.0), parallel,
pbapply, shiny, tidyr

DMC model simulation detailed in Ulrich, R., Schroeter, H., Leuthold, H., & Birngruber, T. (2015).
Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.
Cognitive Psychology, 78, 148-174. Ulrich et al. (2015) <doi:10.1016/j.cogpsych.2015.02.005>.
Decision processes within choice reaction-time (CRT) tasks are often modelled using evidence accumulation models (EAMs),
a variation of which is the Diffusion Decision Model (DDM, for a review, see Ratcliff & McKoon, 2008).
Ulrich et al. (2015) introduced a Diffusion Model for Conflict tasks (DMC). The DMC model combines common
features from within standard diffusion models with the addition of superimposed controlled and automatic activation.
The DMC model is used to explain distributional reaction time (and error rate) patterns in common behavioural
conflict-like tasks (e.g., Flanker task, Simon task). This R-package implements the DMC model and provides functionality
to fit the model to observed data. Further details are provided in the following paper:
Mackenzie, I. G., & Dudschig, C. (2021). DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction
time and error rate data. Methods in Psychology, 100074. <doi:10.1016/j.metip.2021.100074>.

c(
person("Lixiong", "Yang", email = "ylx@lzu.edu.cn",role ="aut"),
person("Junchang", "Zhao",email = "zhaojch19@lzu.edu.cn", role = c("aut","cre")))
Yang(2020,<doi:10.1016/j.jeconom.2020.01.018>) come up with Double Machine Learning model ,it is based on this model, using four machine learning methods-- bagging, Boosting, random forest and neural network, and then  based on the four models   for two different combinations of the integrated model -- linear model combination and random forest .
Lixiong Yang [aut],
Junchang Zhao [aut, cre]
rlang, gbm ,lmtest ,nnet , sandwich,randomForest,caret,ISLR