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%"
)
```

# perimetry

R package for visual field analysis. 

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perimetry will simplify visual field analysis based on current concepts and understanding of visual field data.

## Features 
## Installation
NOT YET ON CRAN

Development version on github (here!) 

``` r
# install.packages("devtools")
devtools::install_github("tjebo/perimetry")
```
## Example

```{r example}
library(perimetry)
```

# tidyfields
A framework for the analysis of visual field (perimetry) data in R.


## Ideas

### 1. Input

### 2. Central object: tidyfield object
```{r}
str(tidyfield_mock)
```


### 3. "Mind the gap" function
Function that completes inputs, that are not stored in the original device output, but that are 'imputable'

E.g., Response [y/n] not saved in MAIA files, but imputable from the staircase values

### 4. Coordinate system
Fixed XY
- xy converted to deg. in visual field space
- Ideal for binocular anlyses

Flipped XY ("left-eye convention")
- Ideal for normal data comparison

> Maybe, both should be included in tidyfield object
> Some convention is needed throughout the package, i.e., each X and Y should have a suffix that specifies the meaning (e.g., "Yvsdeg" for visual field space and degrees )

### Normal data

- Systematic comparison of spatial interpolation techniques necessary

### Output statistics 

Spatially-ignorant metrics (will be spatially-weighted)
- Mean Sensitivity [dB]
- Mean Deviation [dB]
- PSD [dB]
- Mean Loss [dB]
- Sqrt. of Loss Variance [dB]

Spatially-adjusted metrics 
- Vol. [dB*sr]
- Loss [dB*sr]
- PSD [dB*sr]