Rename and remove dust dependency

DESCRIPTION

@@ -31,7 +31,6 @@ Suggests:
     coda,
     decor,
     knitr,
-    dust,
     mockery,
     mvtnorm,
     numDeriv,
@@ -43,5 +42,3 @@ Suggests:
 Config/testthat/edition: 3
 Language: en-GB
 VignetteBuilder: knitr
-Remotes:
-    mrc-ide/dust

tests/testthat/helper-monty.R

@@ -94,22 +94,21 @@ random_array <- function(dim, named = FALSE) {
 }
 
 
-ex_dust_sir_likelihood <- function(n_particles = 100,
-                                   deterministic = FALSE,
-                                   save_trajectories = FALSE) {
+ex_sir_filter_likelihood <- function(n_particles = 100,
+                                     deterministic = FALSE,
+                                     save_trajectories = FALSE) {
   data <- data.frame(time      = c( 4,  8, 12, 16, 20, 24, 28, 32, 36),
                      incidence = c( 1,  0,  3,  5,  2,  4,  3,  7,  2))
   sir_filter_monty(data, n_particles, deterministic, save_trajectories)
 }
 
 
-ex_dust_sir <- function(...) {
-  testthat::skip_if_not_installed("dust")
+ex_sir_filter_posterior <- function(...) {
   prior <- monty_dsl({
     beta ~ Gamma(shape = 1, rate = 1 / 0.5)
     gamma ~ Gamma(shape = 1, rate = 1 / 0.5)
   })
-  ex_dust_sir_likelihood(...) + prior
+  ex_sir_filter_likelihood(...) + prior
 }
 
 

tests/testthat/test-combine.R

@@ -186,7 +186,7 @@ test_that("can combine gradients where parameters do not agree", {
 
 
 test_that("can combine a stochastic and deterministic model", {
-  ll <- ex_dust_sir_likelihood()
+  ll <- ex_sir_filter_likelihood()
   prior <- monty_dsl({
     beta ~ Gamma(shape = 1, rate = 1 / 0.5)
     gamma ~ Gamma(shape = 1, rate = 1 / 0.5)
@@ -199,7 +199,7 @@ test_that("can combine a stochastic and deterministic model", {
 
 
 test_that("can't disable stochastic model on combination", {
-  ll <- ex_dust_sir_likelihood()
+  ll <- ex_sir_filter_likelihood()
   prior <- monty_dsl({
     beta ~ Gamma(shape = 1, rate = 1 / 0.5)
     gamma ~ Gamma(shape = 1, rate = 1 / 0.5)
@@ -212,7 +212,7 @@ test_that("can't disable stochastic model on combination", {
 
 
 test_that("can't create model out of two stochastic halves", {
-  ll <- ex_dust_sir_likelihood()
+  ll <- ex_sir_filter_likelihood()
   expect_error(
     ll + ll,
     "Can't combine two stochastic models")

tests/testthat/test-sample.R

@@ -269,7 +269,7 @@ test_that("error if initial conditions do not have finite density", {
 
 test_that("can continue a stochastic model identically", {
   set.seed(1)
-  model <- ex_dust_sir()
+  model <- ex_sir_filter_posterior()
   vcv <- matrix(c(0.0006405, 0.0005628, 0.0005628, 0.0006641), 2, 2)
   sampler <- monty_sampler_random_walk(vcv = vcv)
   initial <- c(0.2, 0.1)

tests/testthat/test-sampler-adaptive.R

@@ -92,7 +92,7 @@ test_that("can continue adaptive sampler", {
 
 test_that("can't use adaptive sampler with stochastic models", {
   set.seed(1)
-  m <- ex_dust_sir()
+  m <- ex_sir_filter_posterior()
   sampler <- monty_sampler_adaptive(initial_vcv = diag(c(0.01, 0.01)))
   expect_error(
     monty_sample(m, sampler, 30, n_chains = 3),

tests/testthat/test-sampler-hmc.R

@@ -216,7 +216,7 @@ test_that("can't use hmc with models that lack gradients", {
 
 test_that("can't use hmc with stochastic models", {
   set.seed(1)
-  m <- ex_dust_sir()
+  m <- ex_sir_filter_posterior()
   sampler <- monty_sampler_hmc(epsilon = 0.1, n_integration_steps = 10)
   expect_error(
     monty_sample(m, sampler, 30, n_chains = 3),

tests/testthat/test-sampler-random-walk.R

@@ -26,7 +26,7 @@ test_that("validate sampler against model on initialisation", {
 
 test_that("can draw samples from a random model", {
   set.seed(1)
-  m <- ex_dust_sir()
+  m <- ex_sir_filter_posterior()
   vcv <- matrix(c(0.0006405, 0.0005628, 0.0005628, 0.0006641), 2, 2)
   sampler <- monty_sampler_random_walk(vcv = vcv)
   res <- monty_sample(m, sampler, 20)
@@ -36,7 +36,7 @@ test_that("can draw samples from a random model", {
 
 
 test_that("can observe a model", {
-  m <- ex_dust_sir(save_trajectories = TRUE)
+  m <- ex_sir_filter_posterior(save_trajectories = TRUE)
   vcv <- matrix(c(0.0006405, 0.0005628, 0.0005628, 0.0006641), 2, 2)
   sampler <- monty_sampler_random_walk(vcv = vcv)
 
@@ -46,18 +46,14 @@ test_that("can observe a model", {
   expect_setequal(names(res),
                   c("pars", "density", "initial", "details", "observations"))
   expect_equal(names(res$observations),
-               c("trajectories", "state"))
+               "trajectories")
   expect_equal(dim(res$observations$trajectories),
-               c(2, 151, 20, 3)) # states, time steps, samples, chains
-  expect_equal(dim(res$observations$state),
-               c(5, 20, 3)) # states, samples, chains
-  expect_equal(res$observations$state[c(2, 4), , ],
-               res$observations$trajectories[, 151, , ])
+               c(2, 9, 20, 3)) # states, time steps, samples, chains
 })
 
 
 test_that("can continue observed models", {
-  m <- ex_dust_sir(save_trajectories = TRUE)
+  m <- ex_sir_filter_posterior(save_trajectories = TRUE)
   vcv <- matrix(c(0.0006405, 0.0005628, 0.0005628, 0.0006641), 2, 2)
   sampler <- monty_sampler_random_walk(vcv = vcv)