Methods to enrich list-like R objects with extra components

When I develop a piece of statistical methodology it is not uncommon that it depends on functionality or quantities that a list-like core R object `object_x`

of a certain class could have but doesn’t. An example is objects of class `link-glm`

which only provide up to the the first derivatives of the link function. It is also not uncommon that the functionality or quantities that I need are hard-coded in another package in the R ecosystem. Prominent examples include implementations of gradients of the log-likelihood and information matrices for specific model classes.

In such cases, I either contact the developers and ask them to provide a generic which I can then use, or copy some of the code and adopt it to what I’m doing. Both options can be rather time-consuming, and particularly the latter is rarely bug-free.

I believe that

users and developers should have direct access to useful functionality and quantities in the R ecosystem, epsecially if these include implementations of complex statistical quantities

quantities and functionality that are specific to a list-like

`object_x`

should be components of`object_x`

The above motivated me to develop the **enrichwith** R package that allows `object_x`

to be enriched with components corresponding to the option `enrichment_option`

through the following simple call

The call is inspired by Donald Knuth’s literare programing paradigm.

The main objective of **enrichwith** is to allow users and developers to directly use the enrichment options that other developers have provided, through a *clean interface*, minimising the need to adopt source code of others.

The purpose of **enrichwith** is to provide:

*useful enrichment options*for core R objects, including objects of class`lm`

,`glm`

,`link-glm`

and`family`

(see, for example,`?enrich.glm`

)methods for producing

*customisable source code templates*for the structured implementation of methods to compute new components (see`?enrichwith`

and`?create_enrichwith_skeleton`

)generic methods for the

*easy enrichment*of the object with those components (see, for example,`?enrich`

and`?get_enrichment_options`

)

The vignettes illustrate the **enrichwith** functionality through comprehensive, step-by-step case studies. These also include illustrations from recent research of mine on methods for statistical learning and inference.

Get the development version from github with

`link-glm`

objectsObjects of class `link-glm`

have as components functions to compute the link function (`linkfun`

), the inverse link function (`linkinv`

), and the 1st derivative of the inverse link function (`mu.eta`

).

**enrichwith** comes with a built-in template with the methods for the enrichment of `link-glm`

objects with the 2nd and 3rd derivatives of the inverse link function.

The `get_enrichment_options`

method can be used to check what enrichment options are available for objects of class `link-glm`

.

```
library("enrichwith")
standard_link <- make.link("probit")
class(standard_link)
# [1] "link-glm"
get_enrichment_options(standard_link)
# -------
# Option: d2mu.deta
# Description: 2nd derivative of the inverse link function
# component compute_function
# 1 d2mu.deta compute_d2mu.deta
# -------
# Option: d3mu.deta
# Description: 3rd derivative of the inverse link function
# component compute_function
# 1 d3mu.deta compute_d3mu.deta
# -------
# Option: inverse link derivatives
# Description: 2nd and 3rd derivative of the inverse link function
# component compute_function
# 1 d2mu.deta compute_d2mu.deta
# 2 d3mu.deta compute_d3mu.deta
# -------
# Option: all
# Description: all available options
# component compute_function
# 1 d2mu.deta compute_d2mu.deta
# 2 d3mu.deta compute_d3mu.deta
```

According to the result of `get_enrichment_options`

, the object `standard_link`

can be enriched with the 2nd and 3rd derivative of the inverse link function through the option “inverse link derivatives”.

```
enriched_link <- enrich(standard_link, with = "inverse link derivatives")
class(enriched_link)
# [1] "enriched_link-glm" "link-glm"
cat(format(enriched_link$d2mu.deta), sep = "\n")
# function (eta)
# {
# -eta * pmax(dnorm(eta), .Machine$double.eps)
# }
cat(format(enriched_link$d3mu.deta), sep = "\n")
# function (eta)
# {
# (eta^2 - 1) * pmax(dnorm(eta), .Machine$double.eps)
# }
```

`enriched_link`

is now an “enriched” `link-glm`

object, which, as per the enrichment options above, has the extra components `d2mu.deta`

and `d3mu.deta`

, for the calculation of 2nd and 3rd derivatives of the inverse link function with respect to `eta`

, respectively.

`family`

objects