Code for effectively dealing with running CmdStan... from R.... because reasons...
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README.md

README.md

Stannis

There is an official R interface to Stan (mc-stan.org) and this is NOT it. Stannis uses git do download a CmdStan branch, stashes a build of it, and provides some wrappers on system2 calls to let you use it directly from R. It also has some basic facilities for reading output an manipulating it. If you have feedback please let me know (k@fawkes.io), I would like to make this a solid package that complements rstan in function but it will never have the slick look rstan is going for.

Quickstart

This needs git and Stannis installed, it runs an example:

target_directory = '<where-ever-you-put-stuff>'
setup_runs(target_director)
setwd(target_directory)
library(stannis)
run_cmdstan(file = 'fits.yaml')

Check out the output structures in output subdirectory, check out the fits.yaml file to get an idea of how to drive. Then read some more below. Every single function should have documentation.

Where does CmdStan go?

Usually on Linux it goes under ~/.local/share/stannis/cmdstan and some config files go to ~/.config/stannis. If you can't find it you can always run:

rappdirs::user_config_dir()

and

rappdirs::user_data_dir()

What's good about this package.

Running pre-specified models

To run models you write a .yaml file like the one below. Then in R you can do stannis::run_cmdstan(file = 'fits.yaml') and all the models listed under runs will run, with output going to target_dir. The defaults item is merged with each runs item prior to running the model.

defaults:
  project_id : "testy-test"
  method : 'sample'
  sample :
    num_chains : 3
    num_samples : 150
    num_warmup : 200
    save_warmup : 1
    thin : 1
    algorithm : "hmc"
    hmc :
      engine : "nuts"
      nuts :
        max_depth : 15
      static :
        int_time : 6.28
      metric : "diag_e"
      stepsize : 1
      stepsize_jitter : 0
  output :
    refresh : 1
  target_dir: "output"
  data_dir: "data"
  init_dir: "inits"
  model_dir: "models"
  binary_dir : "model-binaries"

runs:
  - model_name : "normal"
    method : 'sample'
    sample :
      num_warmup : 500
      num_samples : 300
  - model_name : "binomial"
    method : 'sample'
    sample :
      num_chains : 8
      num_warmup : 500
      num_samples : 300
    data :
      file : "coins.rdump"

The output goes into target_dir under a directory called fit-... where ... is a hash of the model file contents, the data, and the initial values item along with the project_id field. The field num_chains is not a CmdStan parameter but each time the model runs it creates that many new chains and each one gets its own subdirectory. This particular behavior might change in the future but for how I work this seems the best (run everything and pick up the pieces afterwards).

Analyzing output

Stan output can be big. CmdStan output can be even bigger as it's written out as text. Stannis doesn't really solve that right now but does let you read a set of data files (specified by a path and pattern):

samples <- stannis::read_file_set(root='.', pattern = '.*-output.csv')

This gets you all the samples, all the metadata including the diagonal of the mass matrix if it's available. You can do a few basic manipulations. Most importantly: 1) merge chains; 2) trim warmup; and 3) turn the wide-format that comes from CmdStan into a list of named parameters:

post_warmup <- stannis::trim_warmup(samples)
merged_samples <- stannis::merge_chains(post_warmup)
samples_in_arrays <- stannnis::array_set(merged_samples)

Why write this package

Sometimes rstan is hard to install on a cluster, sometimes you don't want a complicated output object, sometimes rstan is broken and you just want to process your output. Sometimes rstan barfs on the size of your input and you can't use it. This package will often work mostly because CmdStan streams stuff in a reliable if clunky format and has robust makefiles. Yey basics.

Dependencies

You need git installed, you need an up-to-date-enough git/make so they work reliably with the -C option. This package has some R dependencies (magrittr, dplyr) but I'll pull those out at some point as they're not really necessary.