c(person(given = "Max",
family = "Welz",
role = c("aut", "cre"),
email = "welz@ese.eur.nl",
comment = c(ORCID = "0000-0003-2945-1860")),
person(given = "Andreas",
family = "Alfons",
role = "aut",
email = "alfons@ese.eur.nl",
comment = c(ORCID = "0000-0002-2513-3788")),
person(given = "Mert",
family = "Demirer",
role = "aut",
email = "mdemirer@mit.edu"),
person(given = "Victor",
family = "Chernozhukov",
role = "aut",
email = "vchern@mit.edu"))
Max Welz [aut, cre] (<https://orcid.org/0000-0003-2945-1860>),
Andreas Alfons [aut] (<https://orcid.org/0000-0002-2513-3788>),
Mert Demirer [aut],
Victor Chernozhukov [aut]
Generic Machine Learning Inference on heterogeneous treatment effects in randomized experiments as proposed in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arXiv:1712.04802>. This package's workhorse is the 'mlr3' framework of Lang et al. (2019) <doi:10.21105/joss.01903>, which enables the specification of a wide variety of machine learners. The main functionality, GenericML(), runs Algorithm 1 in Chernozhukov, Demirer, Duflo and Fernández-Val (2020) <arXiv:1712.04802> for a suite of user-specified machine learners. All steps in the algorithm are customizable via setup functions. Methods for printing and plotting are available for objects returned by GenericML(). Parallel computing is supported.
sandwich, lmtest, splitstackshape, stats, parallel
glmnet, ranger, rpart, e1071, testthat (>= 3.0.0)

sandwich, lmtest, splitstackshape, stats, parallel, abind

glmnet, ranger, rpart, e1071, xgboost, kknn, DiceKriging,
testthat (>= 3.0.0)

Identify Favorable Subsets of Germplasm Collections
Ryan C. Graebner and Alfonso Cuesta-Marcos
Finds subsets of sets of genotypes with a high mean Polymorphism Information Content (PIC), and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.

Finds subsets of sets of genotypes with a high Heterozygosity, and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.