---
title: "distr6"
output: rmarkdown::html_vignette
date: "`r Sys.Date()`"
vignette: >
  %\VignetteIndexEntry{distr6}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

distr6 is a unified, self-contained and scalable interface to probability distributions in R. Making use of the R6 paradigm, distr6 implements a fully object-oriented (OO) interface complete with distribution construction, full inheritance and more complex design patterns. The API is built to be scalable and intuitive, ensuring that every distribution has the same interface and that more complex properties are abstracted from the core functionality. A full set of tutorials can be found [here](https://alan-turing-institute.github.io/distr6/index.html). In this introductory vignette we briefly demonstrate how to construct a distribution, view and edit its parameters and evaluate different in-built methods. The [website](https://alan-turing-institute.github.io/distr6/index.html) covers more complex use-cases including composite distributions and decorators for numeric methods.


## Getting Started

We think the best place to get started is to pick a probability distribution and work through constructing the distribution via different parameterisations and querying the distribution for different methods. Below is a running example with the Normal distribution.

```{r, include=FALSE}
knitr::opts_chunk$set(collapse = T, comment = "#>")
options(tibble.print_min = 4L, tibble.print_max = 4L)
library("distr6")
set.seed(42)
```

### Construction and Parameterisation
All distributions are constructed using R6 dollar sign notation
The default Normal distribution is the Standard Normal parameterised with mean and var
```{r}
Normal$new()
```

But we could also parameterise with standard deviation or precision.
Note that whichever we choose is clearly printed.
```{r}
Normal$new(mean = 2, sd = 2)
Normal$new(mean = 3, prec = 0.5)
```

But all parameters are available to us via the parameters method. Note how all available parameters are displayed, but only the ones chosen in construction are shown in the print method.
```{r}
N <- Normal$new()
N$print()
N$parameters()
```

### Parameters in distr6
Parameters are accessed with getParameterValue and edited with setParameterValue
```{r}
N$setParameterValue(list(prec = 2))
N$getParameterValue("prec")
```

Note how all parameters that are related also update
```{r}
N$parameters()
```

To view the functions that relate these parameters add the following
```{r}
N$parameters()$print(hide_cols = NULL)
```

The line above introduces 'method chaining', this occurs when one method is added to another.
As another example, let's edit and then access another parameter in the Normal distribution
```{r}
N$setParameterValue(list(var = 3))$getParameterValue("var")
```

### Representing a distribution
In keeping with R conventions, distributions have a print and summary method to view key details.
We have already seen how the print method displays the distribution short_name and the
parameterisation.
```{r}
N$print()
```

The summary method can also show basic statistics and distribution properties
and traits. Adding the argument full = F, suppresses the output slightly.
```{r}
N$summary()
N$summary(full = F)
```

All distributions are also comprised of properties and traits. Traits are ways of
describing a class whereas properties describe an object. In simpler terms, this means that a
trait is present independent of the distribution's parameterisation whereas a property depends
on the constructed parameters.
```{r}
N$properties
N$traits
```

### d/p/q/r
distr6 is intended not to replace R stats distributions but to be a different way of interfacing 
them. All distributions in R stats can be found in distr6 and all their d/p/q/r functions which
refer to density/cumulative distribution/quantile/random are all available in distr6. Continuing
our Normal distribution example:
```{r}
N$pdf(2) # dnorm(2)
N$cdf(2) # pnorm(2)
N$quantile(0.42) # qnorm(2)
N$rand(2) # rnorm(2)
```

distr6 makes it easy to query these results by only requiring the distribution to be constructed once and then the specific parameterisation can be forgotten. In the case of the Normal distribution this may not seem like a big difference to R stats but now look at the difference when we construct a distribution without default parameters

```{r}
B <- Beta$new(shape1 = 0.582, shape2 = 1.2490)
B$pdf(2) # dbeta(2, 0.582, 1.2490)
B$cdf(2) # pbeta(2, 0.582, 1.2490)
B$quantile(0.42) # qbeta(2, 0.582, 1.2490)
B$rand(2) # rbeta(2, 0.582, 1.2490)
```

Finally distr6 includes log/log.p and lower.tail arguments to be consistent with R stats.

```{r}
N$cdf(3, lower.tail = FALSE, log.p = TRUE) == pnorm(3, lower.tail = FALSE, log.p = TRUE)
```

### Mathematical and Statistical Results
The final part of this tutorial looks at how to access mathematical and statistical
results for probability distributions. This is another advantage of distr6 as it collects
not only the results for the 17 distributions in R stats but also for all others implemented in
distr6. Continuing with the Normal distribution:
```{r}
N$mean()
N$variance()
N$entropy() # Note default is base 2
N$mgf(2)
N$cf(1)
```

For a full list of methods available use the help documentation for any distribution
```{r, eval=FALSE, echo=FALSE, message=FALSE, warning=FALSE}
?Normal
```


### Listing in distr6
Instead of having to worry about remembering every distribution in R, distr6 provides a way of
listing all of these, and filtering by traits or package. We only show the first 5 rows of this to save space.
```{r}
head(listDistributions())
head(listDistributions(simplify = TRUE))
# Lists discrete distributions only
head(listDistributions(filter = list(valuesupport = "discrete")))

# Multiple filters can be used, note this is case-insensitive
head(listDistributions(filter = list(VaLueSupport = "continuous", package = "distr6")))
```

### R6, S3 and Magrittr
As a final point, distr6 allows the use of R6 or S3 to call methods, which means that the package
magrittr can also be used for 'piping'. Returning to the Normal distribution
```{r}
library(magrittr)
N$print()
print(N)
N %>% print()

N$pdf(2)
pdf(N, 2)
N %>% pdf(2)
```

## Further Documentation

 * [Tutorials](https://alan-turing-institute.github.io/distr6/articles/webs/intro_to_r6.html)
 * [Extension guidelines](https://alan-turing-institute.github.io/distr6/articles/webs/preliminaries.html)
 * [Project Wiki](https://github.com/alan-turing-institute/distr6/wiki)
