Title: Weighted Average Ensemble without Training Labels
Authors@R: c(person("Aviral", "Vijay", role = c("aut", "cre"), email = "aviral.vijay@gslab.com"), person("Sameer", "Mahajan", role = "aut", email = "sameer.mahajan@gslab.com"))
Description: 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.
Imports: caret (>= 6.0.78), dplyr, randomForest, ggplot2, rlist (>=
Suggests: testthat, knitr, rmarkdown, ClusterR

Title: Invariant Causal Prediction for Nonlinear Models
Description: 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',
Imports: methods, CondIndTests, data.tree, caTools, randomForest

Description: Code for 'nonlinear Invariant Causal Prediction' to estimate the

Title: R package for nonlinear time series analysis
Maintainer: Constantino A. Garcia <constantinoantonio.garcia@usc.es>
Description: R package for nonlinear time series analysis

Arya and David Mount. R wrapper is based on the ANN library,
copyright Samuel Kemp 2005-9 and Gregory Jefferis 2009-2013.

Maintainer: Constantino A. Garcia <constantino.garciama@ceu.es>
Description: Functions for nonlinear time series analysis. This package permits
the computation of the  most-used nonlinear statistics/algorithms
including generalized correlation dimension, information dimension,
largest Lyapunov exponent, sample entropy and Recurrence
Quantification Analysis (RQA), among others. Basic routines
for surrogate data testing are also included. Part of this work
was based on the  book "Nonlinear time series analysis" by
Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Imports: Matrix, rgl, tseries, zoo, Rcpp (>= 0.12.12), graphics, stats
Packaged: 2020-06-13 09:04:56 UTC; gaussllego
Author: Constantino A. Garcia [aut, cre],