README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# NVAR: Nonlinear Vector Autoregression Models

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Estimate nonlinear vector autoregression models (also known as the 
    next generation reservoir computing) for nonlinear dynamic systems. The 
    algorithm was described by Gauthier et al. (2021) <doi:10.1038/s41467-021-25801-2>.

## Installation

You can install the development version of NVAR from [GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("Sciurus365/NVAR")
```

## Example

This is an example for the Lorenz model.

```{r example, warning = FALSE, message = FALSE}
library(NVAR)

testdata <- nonlinearTseries::lorenz()
testdata <- tibble::as_tibble(testdata)
t1 <- NVAR(data = testdata, vars = c("x", "y", "z"), s = 2, k = 2, p = 2, alpha = 1e-3)
t1_sim <- sim_NVAR(t1, length = 5000)


realdata <- nonlinearTseries::lorenz(time = seq(0, 100, by = .01)) %>% tibble::as_tibble()

library(ggplot2)
ggplot(realdata) +
  geom_line(aes(x = 1:10001, y = x), color = "red", alpha = 0.4) +
  geom_line(aes(x = 1:10001, y = x), data = t1_sim, color = "blue", alpha = 0.4) +
  geom_vline(xintercept = 5000) +
  theme_bw() +
  xlim(c(4900, 8000)) +
  labs(x = "time", y = "x")

# Red line: real data.
# Blue line: simulated data with the NVAR.
# Black vertical line: when the simulation starts.
```