README.Rmd

---
output: github_document
editor_options: 
  chunk_output_type: console
---

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```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  message = FALSE,
  warning = FALSE
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```

# RRRR

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The R package *RRRR* provides methods for estimating online Robust Reduced-Rank Regression.

To cite package ‘RRRR’ in publications use:

>  Yangzhuoran Fin Yang and Ziping Zhao (2023). RRRR: Online Robust Reduced-Rank Regression Estimation. R package version 1.1.1. https://pkg.yangzhuoranyang.com/RRRR/.

## Installation

You can install the **stable** version on [R CRAN](https://CRAN.R-project.org/package=RRRR).

```{r cran-installation, eval = FALSE}
install.packages("RRRR")
```


You can install the **development** version from [Github](https://github.com/FinYang/RRRR) with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("FinYang/RRRR")
```

## Usage

The R package *RRRR* provides the following estimation methods.

1. Reduced-Rank Regression using Gaussian MLE: `RRR`
2. Robust Reduced-Rank Regression using Cauchy distribution and Majorisation-Minimisation: `RRRR`
3. Online Robust Reduced-Rank Regression: `ORRRR`
    - SMM: Stochastic Majorisation-Minimisation
    - SAA: Sample Average Approximation
4. Online update of the above model (except `RRR`): `update.RRRR`

See the vignette for a more detailed illustration.

```{r}
library(RRRR)
set.seed(2222)
data <- RRR_sim()
res <- ORRRR(y=data$y, x=data$x, z=data$z)
res
plot(res)

newdata <- RRR_sim(A = data$spec$A,
                   B = data$spec$B,
                   D = data$spec$D)
res2 <- update(res, newy=newdata$y, newx=newdata$x, newz=newdata$z)
res2
plot(res2)
```



## License

This package is free and open source software, licensed under GPL-3.