Weighted Average Ensemble without Training Labels
c(person("Aviral", "Vijay", role = c("aut", "cre"), email = "aviral.vijay@gslab.com"), person("Sameer", "Mahajan", role = "aut", email = "sameer.mahajan@gslab.com"))
It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions.
caret (>= 6.0.78), dplyr, randomForest, ggplot2, rlist (>=
0.4.6.1), glmnet, tidyverse, e1071, purrr, pROC (>= 1.13.0),
rlang (<= 0.3.0.1),
Aviral Vijay [aut, cre],
Sameer Mahajan [aut]

caret (>= 6.0.78), dplyr, randomForest, ggplot2, rlist (>=
0.4.6.1), glmnet, tidyverse, e1071, purrr, pROC (>= 1.13.0),
rlang (>= 0.2.1),

Invariant Causal Prediction for Nonlinear Models
Performs 'nonlinear Invariant Causal Prediction' to estimate the
causal parents of a given target variable from data collected in
different experimental or environmental conditions, extending
'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016),
<arXiv:1501.01332>, to nonlinear settings. For more details, see C. Heinze-Deml,
J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models',
<arXiv:1706.08576>.
methods, CondIndTests, data.tree, caTools, randomForest

Code for 'nonlinear Invariant Causal Prediction' to estimate the
causal parents of a given target variable from data collected in
different experimental or environmental conditions, extending
'Invariant Causal Prediction' from Peters, Buehlmann and Meinshausen (2016),
<arXiv:1501.01332>, to nonlinear settings. For more details, see C. Heinze-Deml,
J. Peters and N. Meinshausen: 'Invariant Causal Prediction for Nonlinear Models',
<arXiv:1706.08576>.

R package for nonlinear time series analysis

ANN library is copyright University of Maryland and Sunil
Arya and David Mount. R wrapper is based on the ANN library,
copyright Samuel Kemp 2005-9 and Gregory Jefferis 2009-2013.
See file COPYRIGHT for details