Imports: tools, methods, utils, magrittr (>= 1.5), readxl (>= 1.2.0),
xml2, rlang (>= 0.3.1), rmarkdown, knitr, digest (>= 0.6),
kableExtra (>= 0.9.0), shiny, dplyr (>= 0.1.8)
Packaged: 2020-03-18 11:03:59 UTC; rainerkrug

Packaged: 2020-04-30 12:54:12 UTC; rainerkrug

Packaged: 2020-05-29 07:36:54 UTC; rainerkrug

Packaged: 2020-05-28 12:59:01 UTC; rainerkrug

kableExtra (>= 0.9.0), shiny, dplyr (>= 0.1.8), openxlsx
Packaged: 2020-09-11 13:49:22 UTC; rainerkrug

Packaged: 2020-09-16 07:14:19 UTC; rainerkrug

openxlsx, writexl, rappdirs, yaml, EML, stringr
Packaged: 2020-06-12 09:04:10 UTC; rainerkrug

kableExtra (>= 0.9.0), shiny, openxlsx, xfun
Packaged: 2022-03-23 10:38:42 UTC; rainerkrug

Packaged: 2022-04-12 07:53:00 UTC; rainerkrug

Packaged: 2022-04-12 13:02:56 UTC; rainerkrug

Packaged: 2022-08-22 07:14:23 UTC; rainerkrug

Description: The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.
Author: Yuan Tang <terrytangyuan@gmail.com>, Gao Tao <joegaotao@gmail.com>, Xiao Nan <road2stat@gmail.com>

Title: Double Machine Learning Algorithms
Description: Implementation of double machine learning (DML) algorithms in R,
based on Emmenegger and Buehlmann (2021) "Regularizing Double Machine Learning
in Partially Linear Endogenous Models" <arXiv:2101.12525>.
Our goal is to perform inference for the linear parameter in partially
linear models with confounding variables.
The standard DML estimator of the linear parameter has a two-stage least
squares interpretation, which can lead to a large variance and overwide
We apply regularization to reduce the variance of the estimator,
which produces narrower confidence intervals that are approximately valid.
Nuisance terms can be flexibly estimated with machine learning algorithms.
Imports: glmnet, matrixcalc, stats, splines, randomForest
Author: Corinne Emmenegger [aut, cre] (<https://orcid.org/0000-0003-0353-8888>),