Helper functions to establish new TFP / greta distributions

A helper function to establish a new distribution. It would perhaps write onto the screen (as in datapasta::dpasta) write to to clipboard, or create a new "name.R" R file.

The idea is that you write in the details of the function into a function like, `use_new_distribution`, providing the name of the distribution, parameters, args, bounds, and whether it is in TFP.

There might also be some helper functions to help users find their function + args in TFP as well.

Perhaps methods could be written to establish new distributions based on existing functions from packages [like distributional](https://github.com/mitchelloharawild/distributional), but perhaps that might be a bit too much magic.

The templating could be done with [whisker](https://github.com/edwindj/whisker)

below is some pseudo code of how this might work.

```
# function to create template of R6 distribution

use_new_distribution <- function(name, # character
parameters, # list
dim, #expected dimension?
args, # extra arguments
bounds, # two parameters, e.g., c(0, Inf)
in_tfp){ # logical, is it in tensorflow?

"{name} <- R6Class("
"{name}_distribution"
inherit = distribution_node,
public = list(

initialize = function(parameter, dim, args) {

'{parameter <- as.greta_array(parameter)}'

# add the nodes as parents and parameters
dim <- check_dims('{parameter}', target_dim = dim)

# put other checks here
# see for example, 'check_positive', etc.
# e.g., check_positive(truncation)

#
self$bounds <- c(0, Inf)
super$initialize("{name}", dim, args)
# this happens for each one
'self$add_parameter(parameters, "parameters")'
},

# this part will be tricky if there isn't a tensorflow probability
# distribution already defined. Perhaps provide an example for both the
# TF distribution if it exists, and also if the tfp distribution doesn't
# exist. Perhaps also include a list data object in greta of the available
# TFP distributions/arguments?

if (in_tfp) {

tf_distrib = function('{parameters}', dag) {
tfp$distributions$'{name}'(concentration = parameters$shape,
rate = parameters$rate)
}

}

)
)

}

```

This is an example distribution, this is the `gamma_distribution` function

```
gamma_distribution <- R6Class(
"gamma_distribution",
inherit = distribution_node,
public = list(

initialize = function(shape, rate, dim, truncation) {

shape <- as.greta_array(shape)
rate <- as.greta_array(rate)

# add the nodes as parents and parameters
dim <- check_dims(shape, rate, target_dim = dim)
check_positive(truncation)
self$bounds <- c(0, Inf)
super$initialize("gamma", dim, truncation)
self$add_parameter(shape, "shape")
self$add_parameter(rate, "rate")
},

tf_distrib = function(parameters, dag) {
tfp$distributions$Gamma(concentration = parameters$shape,
rate = parameters$rate)
}

)
)
```