Consolidating `extract_samples()` And `get_samples()` Functions

Hey guys! Let's dive into a discussion about streamlining our code. Specifically, we're going to talk about the extract_samples() and get_samples() functions within the epiforecasts and EpiNow2 packages. These functions have some overlap in functionality, and we're exploring the best way to consolidate them for a cleaner, more efficient codebase. So buckle up, and let's get started!

Understanding the Current Functions

First, let’s break down what each function does right now. It's super important to understand the nuances of both extract_samples() and get_samples() to make an informed decision about how to proceed. Knowing the ins and outs of each function will help us consolidate them effectively, keeping the best aspects of both while eliminating redundancy. We don't want to throw the baby out with the bathwater, you know?

extract_samples(): The General Extractor

The extract_samples() function is designed to be a versatile tool. Its primary job is to work with any stanfit object, or its equivalent in the cmdstanr package. Think of it as a general-purpose extractor for samples from Bayesian models fit using Stan. What it spits out is a named list of arrays. This format is pretty flexible, making it suitable for various downstream analyses. Essentially, extract_samples() is your go-to function when you need raw samples from a Stan model.

get_samples(): The Specialized Method

On the other hand, get_samples() is a bit more specialized. Currently, it's implemented as an S3 method tailored specifically for objects of the <estimate_infections> class. This means it's designed to work with the output of a particular type of model, namely those produced by the estimate_infections function (likely within the EpiNow2 package). What makes get_samples() unique is that it returns a data.table, which is a powerful and efficient data structure for handling large datasets. It also adds fixed parameters (data passed to Stan) and dates to the output, making it immediately useful for further analysis in epidemiological contexts. In short, get_samples() provides a more processed and context-rich set of samples, ready for direct use in downstream tasks.

Identifying the Overlap and Redundancy

Okay, so we've got two functions that, at first glance, seem to do similar things. But let's dig a little deeper and pinpoint exactly where the overlap lies, and where we might be able to cut down on some redundancy. This is like decluttering your room – you want to keep the stuff you use and ditch the duplicates, right?

The core functionality of both functions revolves around extracting samples from a fitted Stan model. That's the big common ground. Both extract_samples() and get_samples() are, at their heart, sample-retrieval mechanisms. However, the key difference is in the how and the what else. extract_samples() is the bare-bones extractor, giving you the samples in their raw form. get_samples(), on the other hand, adds extra layers of processing and context, like those fixed parameters and dates we talked about. It's like the difference between getting flour versus a fully baked cake.

The redundancy comes into play because we essentially have two functions doing the same core task. While get_samples() adds valuable features, those features could potentially be incorporated into a more flexible version of extract_samples(), or perhaps handled as a post-processing step. Having two separate functions for such closely related tasks can lead to confusion, increased maintenance overhead, and a less streamlined user experience. Think of it like having two different keys for the same lock – it works, but it's not the most efficient system.

Proposed Solution: Streamlining for Efficiency

Alright, let's talk solutions! The main idea here is to make our lives easier by reducing redundancy and creating a more intuitive workflow. The initial suggestion is to consolidate the functionality of these two functions. Here’s the plan:

1. Make extract_samples() Internal: We could make extract_samples() an internal function. This means it wouldn't be directly exposed to the user. Think of it as the engine under the hood – it's still there, doing important work, but you don't need to fiddle with it directly.
2. Enhance get_samples() with Optional Arguments: We can then beef up get_samples() to handle the flexibility we need. The key is to add optional arguments that control its behavior. Specifically, we can add options for:
   • Date Handling: An argument to control whether or not dates are added to the output.
   • Fixed Parameters: An argument to include or exclude fixed parameters in the output.
   • Return Type: This is the big one! We can add an argument that lets the user choose the return type. One option would be the current data.table format. Another option would be to return the same format as the current extract_samples() (a named list of arrays). This essentially gives us the raw samples when we need them.

This approach gives us the best of both worlds. We have a single, user-facing function (get_samples()) that can handle both the raw extraction and the processed, context-rich output. It's like a Swiss Army knife for sample extraction!

Benefits of this Approach

• Reduced Redundancy: We eliminate one function, simplifying the codebase.
• Increased Flexibility: get_samples() becomes a more versatile tool, capable of handling different use cases.
• Improved User Experience: Users only need to learn one function for sample extraction.
• Easier Maintenance: A single function is easier to maintain and update than two.

Diving Deeper: Implementation Considerations

So, how do we actually do this? Let's get into the nitty-gritty of implementation. We need to think about the details to make sure this consolidation goes smoothly. This is where we put on our programming hats and start thinking about code!

Modifying get_samples()

The core of this change lies in modifying the get_samples() function. We'll need to carefully add those optional arguments without breaking any existing functionality. Think of it as renovating a house – you want to make improvements without causing the whole thing to collapse!

• Adding Optional Arguments: We’ll introduce new arguments with sensible defaults. For example:
  • add_dates = TRUE: A logical argument (TRUE or FALSE) to control whether dates are added. Defaults to TRUE to maintain current behavior.
  • include_fixed = TRUE: A logical argument to control whether fixed parameters are included. Defaults to TRUE.
  • `return_format = c(