Science Score: 13.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
✓DOI references
Found 1 DOI reference(s) in README -
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.8%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
Repository
Nonlinear Vector Autoregression Models
Basic Info
- Host: GitHub
- Owner: Sciurus365
- License: gpl-3.0
- Language: R
- Default Branch: master
- Size: 48.8 KB
Statistics
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created almost 4 years ago
· Last pushed over 2 years ago
Metadata Files
Readme
License
README.Rmd
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# NVAR: Nonlinear Vector Autoregression Models
[](https://github.com/Sciurus365/NVAR/actions/workflows/R-CMD-check.yaml)
[](https://CRAN.R-project.org/package=NVAR)
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) .
## 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.
```
Owner
- Name: Jingmeng Cui
- Login: Sciurus365
- Kind: user
- Location: Groningen, Netherlands
- Company: @rijksuniversiteit-groningen
- Website: https://jingmeng-cui.netlify.app/
- Twitter: CUI_Jingmeng
- Repositories: 5
- Profile: https://github.com/Sciurus365
PhD student | University of Groningen | Dynamics in psychology | R | (a bit) chemistry, LaTeX, python
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Packages
- Total packages: 1
-
Total downloads:
- cran 206 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 1
- Total maintainers: 1
cran.r-project.org: NVAR
Nonlinear Vector Autoregression Models
- Homepage: https://github.com/Sciurus365/NVAR
- Documentation: http://cran.r-project.org/web/packages/NVAR/NVAR.pdf
- License: GPL (≥ 3)
-
Latest release: 0.1.0
published over 2 years ago
Rankings
Dependent packages count: 28.4%
Dependent repos count: 36.4%
Average: 49.9%
Downloads: 84.9%
Maintainers (1)
Last synced:
10 months ago
Dependencies
.github/workflows/R-CMD-check.yaml
actions
- actions/checkout v3 composite
- r-lib/actions/check-r-package v2 composite
- r-lib/actions/setup-pandoc v2 composite
- r-lib/actions/setup-r v2 composite
- r-lib/actions/setup-r-dependencies v2 composite
DESCRIPTION
cran
- dplyr * imports
- magrittr * imports
- purrr * imports
- rlang * imports
- stats * imports
- tibble * imports
- tidyr * imports
- ggplot2 * suggests
- nonlinearTseries * suggests
- testthat >= 3.0.0 suggests