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----
-title: Draft guide to adding a new unmarked function
-author: Ken Kellner
----
-
-This guide uses the recently developed `gdistremoval` function for examples, mainly because most of the relevant code is in a single file instead of spread around.
-
-`unmarked` uses S4 for objects and methods - if you aren't familiar with S4 you may want to consult a book or tutorial such as [this one](https://kasperdanielhansen.github.io/genbioconductor/html/R_S4.html).
-
-# Write the likelihood function
-
-* Should be an R function that takes as input a vector of parameter values, the response variable, design matrices, and other settings/required data
-* The output should be the negative log-likelihood
-* Should be written so it can be used with the `optim()` function
-* Examples: 
-* After writing the R version, in most cases it will be very useful to also write a C++ version
-* The C++ version could use Rcpp/RcppArmadillo or TMB
-* Examples: [C++](https://github.com/rbchan/unmarked/blob/master/src/nll_gdistremoval.cpp), [TMB](https://github.com/rbchan/unmarked/blob/master/src/TMB/tmb_gdistremoval.hpp)
-* `gdistremoval` doesn't have an R version of the likelihood function, but here's the R version for single season occupancy `occu`: [R Example](https://github.com/rbchan/unmarked/blob/c82e63947d7df7dfc896066e51dbf63bda3babf4/R/occu.R#L65)
-
-# Design the unmarkedFrame object for the model
-
-* Most model types in unmarked have their own `unmarkedFrame`, a specialized kind of data frame
-* This is an S4 object which contains, at a minimum, the response (y), and site covariates, and may also include observation covariates, primary period covariates, and other info related to study design (such as distance breaks)
-* In some cases you may be able to use an existing `unmarkedFrame` type
-* [Example](https://github.com/rbchan/unmarked/blob/c82e63947d7df7dfc896066e51dbf63bda3babf4/R/gdistremoval.R#L1)
-* Also need to write some S4 methods for the `unmarkedFrame`, such as how to subset it with `[`
-* [Examples](https://github.com/rbchan/unmarked/blob/c82e63947d7df7dfc896066e51dbf63bda3babf4/R/gdistremoval.R#L67)
-
-# Start a draft of the fitting function
-
-* R formulas for each submodel (e.g. state, detection). We have found over time it is better to have separate arguments per formula instead of a combined formula e.g. the way `occu` does it.
-* The `unmarkedFrame`
-* Other model-specific settings, such as key functions or parameterizations to use
-* [Example](https://github.com/rbchan/unmarked/blob/c82e63947d7df7dfc896066e51dbf63bda3babf4/R/gdistremoval.R#L257)
-
-# Write the getDesign method for the model
-
-* Most models have their own `getDesign` function, an S4 method
-* The purpose of this function is to convert the information in the `unmarkedFrame` into a format usable by the likelihood function
-* As such it usually generates design matrices from formulas and components of the `unmarkedFrame`
-* It often also has code to handle missing values, such as by dropping sites that don't have measurements, or giving the user warnings if covariates are missing, etc.
-* Frequently the most tedious and difficult part of the work adding a new function
-* [Example](https://github.com/rbchan/unmarked/blob/c82e63947d7df7dfc896066e51dbf63bda3babf4/R/gdistremoval.R#L177)
-
-# Write a draft of the complete fitting function process
-
-* Simulate some data using your model
-* Construct the `unmarkedFrame`
-* Provide formulas, `unmarkedFrame`, other options to your draft fitting function
-* Process them with `getDesign`
-* Pass results from `getDesign` as inputs to your likelihood function
-* Optimize the likelihood function
-* Check the resulting parameter estimates for accuracy
-
-# Output processing
-
-* Output from `optim` should be organized unto `unmarkedEstimate` (S4) objects
-* One `unmarkedEstimate` per submodel (e.g. state, detection)
-* These objects include the parameter estimates and other info about link functions etc.
-* [Example](https://github.com/rbchan/unmarked/blob/c82e63947d7df7dfc896066e51dbf63bda3babf4/R/gdistremoval.R#L429)
-* Draft a new `unmarkedFit` S4 object type for your model
-* The main component of these objects is a list of the `unmarkedEstimates`
-* [Example definition](https://github.com/rbchan/unmarked/blob/c82e63947d7df7dfc896066e51dbf63bda3babf4/R/gdistremoval.R#L255), [Example creation of object](https://github.com/rbchan/unmarked/blob/c82e63947d7df7dfc896066e51dbf63bda3babf4/R/gdistremoval.R#L466)
-* Return this from your fitting function
-* Test everything out
-
-# unmarkedFit Methods
-
-* Develop methods specific to your `unmarkedFit` type for operating on the output
-* Required methods include: `fitted`, `residuals`, `getP`, `ranef`, `simulate`, and `plot`
-* Make sure your model type works with other `unmarked` functions such as `parboot`, `nonparboot` etc.
-* [Examples](https://github.com/rbchan/unmarked/blob/c82e63947d7df7dfc896066e51dbf63bda3babf4/R/gdistremoval.R#L476)
-
-# Write tests
-
-* For your `unmarkedFrame`, fitting function, and methods code
-* Should be fast, but cover all the key configurations
-* Using `testthat` package
-* [Example](https://github.com/rbchan/unmarked/blob/master/tests/testthat/test_gdistremoval.R)
-
-# Add to unmarked
-
-* Fork the `unmarked` [repository](https://github.com/rbchan/unmarked) on Github
-* Make a new branch with your new function as the name
-* Add the new code
-* Send a pull request on Github
-* Probably fix a few things
-* Merged and done!