in Partially Linear Endogenous Models" <arXiv:2101.12525> and Emmenegger and
Buehlmann (2021) <arXiv:2108.13657> "Double Machine Learning for Partially
Linear Mixed-Effects Models with Repeated Measurements".
First part: our goal is to perform inference for the linear parameter in partially
Second part: our goal is to estimate and perform inference for the linear
coefficient in a partially linear mixed-effects model
with DML. Machine learning algorithms allows us to incorporate more
complex interaction structures and high-dimensional variables.
Imports: glmnet, lme4, matrixcalc, methods, splines, randomForest

Title: Dyadic Mixed Model for Pedigree Data
Description: Dyadic mixed model analysis with multi-trait responses and
pedigree-based partitioning of individual variation into a range of
environmental and genetic variance components for individual and
Depends: MASS, Matrix, nadiv, robustbase, pls

maternal effects. Method documented in dmmOverview.pdf; it is an
implementation of dispersion mean model described by
Searle et al. (1992) "Variance Components", Wiley, NY.

Title: DiST - Diffusion Direction Smoothing and Tracking
Authors@R: c(person("Seungyong", "Hwang", email="syhwang@ucdavis.edu", role=c("aut", "cre")),
Description: It provides functions to apply the deterministic tracking algorithm - DiST (Wong et al 2016) <doi:10.1214/15-AOAS880> and to plot tractography results.
Suggests: rmarkdown, knitr, testthat (>= 3.0.0), covr
Packaged: 2021-06-08 19:31:44 UTC; seungyong

Author: Leo Lahti, Olli-Pekka Huovilainen, and Abhishek Tripathi
Description: Probabilistic dependency modeling toolkit.
show-methods.R utilities.R set.M.R set.beta.R get.W.R
Depends: mvtnorm, methods, graphics, MASS, Matrix, ellipse

Author: Leo Lahti and Olli-Pekka Huovilainen

Depends: R (>= 2.15.0), methods, mvtnorm, Matrix, MASS

Depends: R (>= 2.15.0), methods, mvtnorm, MASS
Packaged: 2013-12-11 20:30:37 UTC; antagomir

Description: For checking the dataset from EDC(Electronic Data Capture) in clinical trials.
'dmtools' can check laboratory, dates, WBCs(White Blood Cells) count and rename the dataset.
Laboratory - does the investigator correctly estimate the laboratory analyzes?
Dates - do all dates correspond to the protocol's timeline?
WBCs count - do absolute equal (all * relative) / 100?
If the clinical trial has different lab reference ranges, 'dmtools' also can help.
Imports: magrittr (>= 1.5), dplyr (>= 0.8.3), readxl (>= 1.3.1), purrr