Description: Contains data, code, and figures from Hill et al. 2018a (Journal of Experimental Marine Biology and Ecology; <DOI: 10.1016/j.jembe.2018.07.006>) and Hill et al. 2018b (Data In Brief <DOI: 10.1016/j.dib.2018.09.133>). Datasets document plant allometry, stem heights, nutrient and stable isotope content, and sediment denitrification enzyme assays. The data and analysis offer an examination of nitrogen uptake and allocation in two salt marsh plant species.

Title: Solve Nonlinear Optimization with Nonlinear Constraints
Author: Xianyan Chen <xychen@uga.edu>, Xiangrong Yin <yinxiangrong@uky.edu>
Description: Optimization for nonlinear objective and constraint functions.  Linear or nonlinear equality and inequality constraints are allowed.  It accepts the input parameters as a constrained matrix.

Authors@R: c(person("Youssef", "Hmamouche", role = c("aut", "cre"), email = "hmamoucheyussef@gmail.com"), person("Sylvain", "Barthelemy", role = c("cph"), email = "sylbarth@gmail.com"))
Description: The main functionalities of this package are about time series forecasting and causality detection.  In particular, it provides a neural network Vector Auto-Regressive, the classical Granger causality test C.W.J.Granger (1980) <doi:10.1016/0165-1889(80)90069-X>,  and  a non-linear version of it.

Description: Models for time series forecasting and causality detection.  The main functionalities of this package consist of a neural network Vector Auto-Regressive model, the classical Granger causality test C.W.J.Granger (1980) <doi:10.1016/0165-1889(80)90069-X>,  and  a non-linear version of it based on feedforward neural networks.

Description: Models for time series forecasting and causality detection.  The main functionalities of this package consist of a neural network Vector Auto-Regressive model, 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.

Description: 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 Augmented test of stationarity, and the discrete and continuous entropy and mutual information.