Multiple Testing Approach using Average Power Function (APF) and
Bayes FDR Robust Estimation
c(person("Nicolò", "Margaritella", email =
"N.Margaritella@sms.ed.ac.uk", role = c("cre", "aut")),
person("Piero", "Quatto", role = "aut"))
Nicolò Margaritella [cre, aut],
Piero Quatto [aut]
Implements a multiple testing approach to the
choice of a threshold gamma on the p-values using the
Average Power Function (APF) and Bayes False Discovery
Rate (FDR) robust estimation. Function apf_fdr()
estimates both quantities from either raw data or
p-values. Function apf_plot() produces smooth graphs
and tables of the relevant results. Details of the methods
can be found in Quatto P, Margaritella N, et al. (2019)
<doi:10.1177/0962280219844288>.

Auto-Adaptive Parentage Inference Software Tolerant to Missing
Parents
Ronan Griot, François Allal, Romain Morvezen, Sophie Brard-Fudulea, Pierrick Haffray, Florence Phocas and Marc Vandeputte
Parentage assignment package.
Parentage assignment is performed based on observed average Mendelian transmission probability distributions.
The main function of this package is the function APIS(), which is the parentage assignment function.
foreach, parallel, doParallel, ggplot2, gridExtra

foreach, parallel, doParallel, ggplot2, gridExtra, methods

An Approach for Machine-Learning Modelling
We include
1) data cleaning including variable scaling, missing values and unbalanced variables identification and removing, and strategies for variable balance improving;
2) modeling based on random forest and gradient boosted model including feature selection, model training, cross-validation and external testing.
For more information, please see H2O.ai (Oct. 2016). R Interface for H2O, R package version 3.10.0.8. <https://github.com/h2oai/h2o-3>; Zhang W (2016). <doi:10.1016/j.scitotenv.2016.02.023>.
Xinlei Deng [aut, cre, cph],
Wangjian Zhang [aut],
Shao Lin [aut]

We include
1) data cleaning including variable scaling, missing values and unbalanced variables identification and removing, and strategies for variable balance improving;
2) modeling based on random forest and gradient boosted model including feature selection, model training, cross-validation and external testing.
For more information, please see Deng X (2021). <doi:10.1016/j.scitotenv.2020.144746>; H2O.ai (Oct. 2016). R Interface for H2O, R package version 3.10.0.8. <https://github.com/h2oai/h2o-3>; Zhang W (2016). <doi:10.1016/j.scitotenv.2016.02.023>.