Nested loop cross validation for classification purposes for misclassification error rate estimation.
  The package supports several methodologies for feature selection: random forest, Student t-test, limma, 
  and provides an interface to the following classification methods in the 'MLInterfaces' package: linear, 
  quadratic discriminant analyses, random forest, bagging, prediction analysis for microarray, generalized 
  linear model, support vector machine (svm and ksvm). Visualizations to assess the quality of
  the classifier are included: plot of the ranks of the features, scores plot for a specific 
  classification algorithm and number of features, misclassification rate 
  for the different number of features and classification algorithms tested and ROC plot.
  For further details about the methodology, please check:
  Markus Ruschhaupt, Wolfgang Huber, Annemarie Poustka, and Ulrich Mansmann (2004) 
  <doi:10.2202/1544-6115.1078>.

Solve a system of nonlinear equations using a Broyden or a Newton method
             with a choice of global strategies such as line search and trust region.
             There are options for using a numerical or user supplied Jacobian,
             for specifying a banded numerical Jacobian and for allowing
             a singular or ill-conditioned Jacobian.

Six growth models are fitted using non-linear least squares. These are the Richards, the 3, 4 and 5 parameter logistic, the Gompetz and the Weibull growth models. Reference: Reddy T., Shkedy Z., van Rensburg C. J., Mwambi H., Debba P., Zuma K. and Manda, S. (2021). "Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach". BMC medical research methodology, 21(1), 1-11. <doi:10.1186/s12874-020-01165-x>.

Models for non-linear time series analysis and causality detection. The main functionalities of this package consist of an implementation of the classical causality test (C.W.J.Granger 1980) <doi:10.1016/0165-1889(80)90069-X>,  and a non-linear version of it based on feed-forward neural networks. This package contains also an implementation of the Transfer Entropy <doi:10.1103/PhysRevLett.85.461>, and the continuous Transfer Entropy using an approximation based on the k-nearest neighbors <doi:10.1103/PhysRevE.69.066138>. There are also some other useful tools, like the VARNN (Vector Auto-Regressive Neural Network) prediction model, the Augmented test of stationarity, and the discrete and continuous entropy and mutual information.