# SpaCOAP

High-Dimensional Spatial Covariate-Augmented Overdispersed Poisson Factor Model

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We introduce an efficient latent representation learning approach tailored specifically for high-dimensional, large-scale spatial count data, incorporating additional covariates for enhanced performance. To model correlations among variables measured at a shared spatial location, we utilize a covariate-augmented overdispersed Poisson factor model. We distinguish between high-dimensional covariates sharing similar attributes and those serving as control variables to enrich the representation learning process. To capture the spatial dependency of each variable across different locations, we apply a conditional autoregressive model to the latent factors. Furthermore, we propose a variational expectation-maximization algorithm to estimate the model parameters and latent factors, imposing a low-rank constraint on the high-dimensional regression coefficient matrix.

Check out Package Website for a more complete description of the methods and analyses.

# Installation

“SpaCOAP” depends on the ‘Rcpp’ and ‘RcppArmadillo’ package, which requires appropriate setup of computer. For the users that have set up system properly for compiling C++ files, the following installation command will work.

```
## Method 1:
if (!require("remotes", quietly = TRUE))
install.packages("remotes")
remotes::install_github("feiyoung/SpaCOAP")
## Method 2: install from CRAN
install.packages("SpaCOAP")
```

## Usage

For usage examples and guided walkthroughs, check the `vignettes`

directory of the repo.

## Simulated codes

For the codes in simulation study, check the `simu_code`

directory of the repo.

## News

SpaCOAP version 1.2 released! (2024-05-25)