Prefer free functions to methods

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: monty
 Title: Monte Carlo Models
-Version: 0.2.39
+Version: 0.2.40
 Authors@R: c(person("Rich", "FitzJohn", role = c("aut", "cre"),
                     email = "rich.fitzjohn@gmail.com"),
              person("Wes", "Hinsley", role = "aut"),

R/packer.R

@@ -2,7 +2,7 @@
 ##' unstructured vector of numbers (the vector being updated by an
 ##' MCMC for example) and a structured list of named values, which is
 ##' easier to program against.  This is useful for the bridge between
-##' model parameters and a models's implementation, but it is also
+##' model parameters and a model's implementation, but it is also
 ##' useful for the state vector in a state-space model.  We refer to
 ##' the process of taking a named list of scalars, vectors and arrays
 ##' and converting into a single vector "packing" and the inverse

R/rng.R

@@ -297,7 +297,7 @@ monty_rng <- R6::R6Class(
     ##'
     ##' @param size The number of trials (zero or more, length 1 or n)
     ##'
-    ##' @param prob The mean probability of sucess on each trial
+    ##' @param prob The mean probability of success on each trial
     ##'   (between 0 and 1, length 1 or n)
     ##' 
     ##' @param rho The dispersion parameter (between 0 and 1, length 1 or n)

R/runner-callr.R

@@ -4,7 +4,7 @@
 ##' this is the same as "worker" processes.  Unless your chains take a
 ##' few seconds to run, this will be slower than running with the
 ##' default serial runner ([monty_runner_serial]), however for long
-##' running chains, the speedup will typically scale with workers
+##' running chains, the speed-up will typically scale with workers
 ##' added, so long as your chains divide neatly over workers.
 ##'
 ##' @title Run MCMC chains in parallel with `callr`

R/runner.R

@@ -54,7 +54,7 @@ monty_runner_serial <- function(progress = NULL) {
 ##' Run MCMC chains in parallel (at the same time).  This runner uses
 ##' the `parallel` package to distribute your chains over a number of
 ##' worker processes on the same machine.  Compared with
-##' [monty_runner_callr] (Whch is similar to the "worker" support in
+##' [monty_runner_callr] (Which is similar to the "worker" support in
 ##' `mcstate` version 1), this is very simple.  In particular we do
 ##' not report back any information about progress while a chain is
 ##' running on a worker or even across chains.  There's also no

README.md

@@ -1,7 +1,7 @@
 # monty
 
 <!-- badges: start -->
-[![Project Status: Concept – Minimal or no implementation has been done yet, or the repository is only intended to be a limited example, demo, or proof-of-concept.](https://www.repostatus.org/badges/latest/concept.svg)](https://www.repostatus.org/#concept)
+[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)
 [![R-CMD-check](https://github.com/mrc-ide/monty/actions/workflows/R-CMD-check.yaml/badge.svg?branch=main)](https://github.com/mrc-ide/monty/actions/workflows/R-CMD-check.yaml)
 [![codecov.io](https://codecov.io/github/mrc-ide/monty/coverage.svg?branch=main)](https://codecov.io/github/mrc-ide/monty?branch=main)
 <!-- badges: end -->
@@ -10,8 +10,6 @@
 
 See the [package vignette](https://mrc-ide.github.io/monty/articles/monty.html) for a basic introduction.
 
-The package is in an early, though rapid, stage of development and we cannot guarantee that interfaces will not change (in fact, we can guarantee that they **will** really).  However the core concepts have now solidified.
-
 The core of the package is built around some basic ideas:
 
 * you have some [statistical model](https://mrc-ide.github.io/monty/reference/monty_model.html) from which you would like to draw samples using MCMC ([`monty::monty_sample`](https://mrc-ide.github.io/monty/reference/monty_sample.html))

inst/WORDLIST

@@ -8,6 +8,8 @@ Poisson
 R's
 SEF
 VCV
+WIP
+burnin
 cli's
 codecov
 composable

vignettes/dsl.Rmd

@@ -52,14 +52,14 @@ We can draw samples from the model if we provide a `monty_rng` object
 
 ```{r}
 rng <- monty_rng$new()
-theta <- prior$direct_sample(rng)
+theta <- monty_model_direct_sample(prior, rng)
 theta
 ```
 
 We can compute the (log) density at a point in parameter space
 
 ```{r}
-prior$density(theta)
+monty_model_density(prior, theta)
 ```
 
 The computed properties for the model are: