Title: Auto-Adaptive Parentage Inference Software Tolerant to Missing
Author: Ronan Griot, François Allal, Romain Morvezen, Sophie Brard-Fudulea, Pierrick Haffray, Florence Phocas and Marc Vandeputte
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.
Imports: foreach, parallel, doParallel, ggplot2, gridExtra

Imports: foreach, parallel, doParallel, ggplot2, gridExtra, methods

Title: An Approach for Machine-Learning Modelling
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>.

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>.

Packaged: 2022-01-15 05:40:23 UTC; Xinlei Deng

Packaged: 2022-01-21 03:01:01 UTC; Xinlei Deng

Packaged: 2022-05-12 04:10:18 UTC; Xinlei Deng

Title: Augmented and Penalized Minimization Method L0
Description: Fit linear, logistic and Cox models regularized with L0, lasso (L1), elastic-net (L1 and L2), or net (L1 and Laplacian) penalty, and their adaptive forms, such as adaptive lasso / elastic-net and net adjusting for signs of linked coefficients. It solves L0 penalty problem by simultaneously selecting regularization parameters and performing hard-thresholding or selecting number of non-zeros. This augmented and penalized minimization method provides an approximation solution to the L0 penalty problem, but runs as fast as L1 regularization problem. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients. It could deal with very high dimensional data and has superior selection performance.