uGMAR
R package for Gaussian and Student's t mixture autoregression analysis (also available on CRAN)
Science Score: 26.0%
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○Scientific vocabulary similarity
Low similarity (15.6%) to scientific vocabulary
Last synced: 10 months ago
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Repository
R package for Gaussian and Student's t mixture autoregression analysis (also available on CRAN)
Basic Info
- Host: GitHub
- Owner: saviviro
- Language: R
- Default Branch: master
- Homepage: https://CRAN.R-project.org/package=uGMAR
- Size: 17.9 MB
Statistics
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 1
- Releases: 0
Created about 7 years ago
· Last pushed about 1 year ago
Metadata Files
Readme
Changelog
README.Rmd
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# uGMAR
uGMAR provides tools for estimating and analyzing Gaussian mixture autoregressive (GMAR), Student's t mixture Autoregressive (StMAR) and Gaussian and Student's t mixture autoregressive (G-StMAR) models, including functions for unconstrained and constrained maximum likelihood estimation of the model parameters, quantile residual based model diagnostics, simulation from the processes, and forecasting.
Installation
------------
You can install the released version of uGMAR from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("uGMAR")
```
And the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("saviviro/uGMAR")
```
Example
-------
This is a basic example how to estimate GSMAR model and further analyze it.. The example data is simulated from a GMAR p=1, M=2 process. The estimation process is computationally demanding and takes advantage of parallel computing.
``` r
## Estimate a GMAR(1, 2) model and examine the estimates
data(simudata, package="uGMAR")
fit <- fitGSMAR(data=simudata, p=1, M=2, model="GMAR", ncalls=10, seeds=1:10)
fit
summary(fit) # Approximate standard errors in brackets
plot(fit)
get_gradient(fit) # The first order condition
get_soc(fit) # The second order condition (eigenvalues of approximated Hessian)
profile_logliks(fit) # Plot the profile log-likelihood functions
## Quantile residual diagnostics
quantile_residual_plot(fit)
diagnostic_plot(fit)
qrt <- quantile_residual_tests(fit)
## Simulate a sample path from the estimated process
sim <- simulate(fit, nsim=100)
plot.ts(sim$sample)
## Forecast future values of the process
predict(fit, n_ahead=10, pi=c(0.95, 0.8))
# Estimate a GMAR(1, 2) model with the autoregressive coefficients restricted
# to be the same in both regimes:
fitr <- fitGSMAR(data=simudata, p=1, M=2, model="GMAR", restricted=TRUE,
ncalls=10, seeds=1:10)
# Test with likelihood ratio tests whether the AR parameters are the same in
# both regimes (see also the function 'Wald_test'):
LR_test(fit, fitr)
# Conditional mean and variance plots:
cond_moment_plot(fit, which_moment="mean")
cond_moment_plot(fit, which_moment="variance")
```
References
----------
- Kalliovirta L., Meitz M. and Saikkonen P. 2015. Gaussian Mixture Autoregressive model for univariate time series. *Journal of Time Series Analysis*, **36**(2), 247-266.
- Meitz M., Preve D., Saikkonen P. 2023. A mixture autoregressive model based on Student's t-distribution. *Communications in Statistics - Theory and Methods* **52**(2), 499-515.
- \item Virolainen S. 2022. A mixture autoregressive model based on Gaussian and Student's t-distributions. *Studies in Nonlinear Dynamics & Econometrics*, **26**(4), 559-580.
Owner
- Login: saviviro
- Kind: user
- Repositories: 3
- Profile: https://github.com/saviviro
GitHub Events
Total
- Push event: 5
Last Year
- Push event: 5
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Virolainen | s****o@a****i | 159 |
| saviviro | s****3@g****m | 154 |
| Savi Virolainen | s****n@h****i | 9 |
| saviviro | 4****o | 7 |
Committer Domains (Top 20 + Academic)
helsinki.fi: 1
ad.helsinki.fi: 1
Issues and Pull Requests
Last synced: over 2 years ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
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Top Labels
Issue Labels
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Packages
- Total packages: 1
-
Total downloads:
- cran 612 last-month
- Total docker downloads: 21,613
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 30
- Total maintainers: 1
cran.r-project.org: uGMAR
Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models
- Documentation: http://cran.r-project.org/web/packages/uGMAR/uGMAR.pdf
- License: GPL-3
-
Latest release: 3.6.0
published about 1 year ago
Rankings
Downloads: 20.0%
Forks count: 28.8%
Average: 29.1%
Dependent packages count: 29.8%
Stargazers count: 31.7%
Dependent repos count: 35.5%
Maintainers (1)
Last synced:
11 months ago
Dependencies
DESCRIPTION
cran
- R >= 3.4.0 depends
- Brobdingnag >= 1.2 imports
- gsl >= 1.9 imports
- parallel * imports
- pbapply >= 1.3 imports
- stats >= 3.3.2 imports
- knitr * suggests
- rmarkdown * suggests
- testthat * suggests