Imports: DEoptim, Rcpp (>= 0.12.16), dplyr (>= 1.0.0), parallel,

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>.

person("Lixiong", "Yang", email = "ylx@lzu.edu.cn",role ="aut"),
person("Junchang", "Zhao",email = "zhaojch19@lzu.edu.cn", role = c("aut","cre")))
Description: 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 .
Imports: rlang, gbm ,lmtest ,nnet , sandwich,randomForest,caret,ISLR

Title: Daily Based Morgan--Morgan--Finney (DMMF) Soil Erosion Model
Authors@R: c(person("Kwanghun", "Choi", role = c("aut", "cre"), email="kwanghun.choi@yahoo.com"), person("Sebastian", "Arnhold", role = c("dtc", "ctb")), person("Bjoern", "Reineking", role = c("ths", "ctb")))
Author: Kwanghun Choi [aut, cre], Sebastian Arnhold [dtc, ctb], Bjoern Reineking [ths, ctb]
Description: Implements the daily based Morgan--Morgan--Finney (DMMF) soil erosion model for estimating surface runoff and sediments budgets from a field or a catchment on a daily basis.
Depends: R (>= 3.2.3), raster, sp, rgdal, methods

Title: Daily Based Morgan-Morgan-Finney (DMMF) Soil Erosion Model
Description: Implements the daily based Morgan-Morgan-Finney (DMMF) soil erosion model (Choi et al., 2017 <doi:10.3390/w9040278>) for estimating surface runoff and sediment budgets from a field or a catchment on a daily basis.

Package: DMR
Type: Package
Title: Delete or Merge Regressors for linear model selection.
Version: 2.0
Date: 2013-01-30
Author: Aleksandra Maj, Agnieszka Prochenka, Piotr Pokarowski
Maintainer: Aleksandra Maj <aleksandra.lucja.maj@gmail.com>
Depends: R (>= 1.8.0), magic
Description: A backward selection procedure called delete or merge
regressors (DMR) combines deleting continuous variables with
merging levels of factors. The method assumes greedy search
among linear models with set of constraints of two types:
either a parameter for a continuous variable is set to zero or
parameters corresponding to two levels of a factor are
compared. DMR is a stepwise regression procedure, where in each
step a new constraint is added according to ranking of the
hypotheses based on squared t-statistics. As a result a nested
family of linear models is obtained and the final decision is
made according to minimization of the generalized information
criterion (GIC, default BIC). The main function of the package
is DMR, which is based on hierarchical clustering. Moreover,
other functions for extensions of DMR method are given, such as
stepDMR which is based on recalculation of t-statistics in each
step and function DMR4glm for generalized linear models.
License: GPL-2
LazyLoad: yes
Packaged: 2013-02-21 11:44:32 UTC; am248424
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-02-21 13:19:28