Drop legacy RNG interface

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: monty
 Title: Monte Carlo Models
-Version: 0.3.18
+Version: 0.3.19
 Authors@R: c(person("Rich", "FitzJohn", role = c("aut", "cre"),
                     email = "[email protected]"),
              person("Wes", "Hinsley", role = "aut"),

NAMESPACE

@@ -71,13 +71,9 @@ export(monty_random_real)
 export(monty_random_truncated_normal)
 export(monty_random_uniform)
 export(monty_random_weibull)
-export(monty_rng)
 export(monty_rng_create)
-export(monty_rng_distributed_pointer)
-export(monty_rng_distributed_state)
 export(monty_rng_jump)
 export(monty_rng_long_jump)
-export(monty_rng_pointer)
 export(monty_rng_set_state)
 export(monty_rng_state)
 export(monty_runner_callr)

R/dsl.R

@@ -117,7 +117,7 @@ monty_dsl_parse <- function(x, type = NULL, gradient = NULL, fixed = NULL) {
 ##'   provided!
 ##'
 ##' * `sample`: A function to sample from the distribution, given (as
-##'   a first argument) a rng object (see [monty_rng])
+##'   a first argument) a `monty_rng` object (see [monty_rng_create])
 ##'
 ##' @export
 ##' @examples

R/model.R

@@ -118,13 +118,13 @@ monty_model_properties <- function(has_gradient = NULL,
 ##'   `(-Inf, Inf)`.
 ##'
 ##' * `direct_sample`: A function to sample directly from the
-##'   parameter space, given a [monty_rng] object to sample from.
-##'   In the case where a model returns a posterior (e.g., in Bayesian
-##'   inference), this is assumed to be sampling from the prior.
-##'   We'll use this for generating initial conditions for MCMC where
-##'   those are not given, and possibly other uses.  If not given then
-##'   when using [monty_sample()] the user will have to provide a
-##'   vector of initial states.
+##'   parameter space, given a `monty_rng` object to sample from (see
+##'   [monty_rng_create]).  In the case where a model returns a
+##'   posterior (e.g., in Bayesian inference), this is assumed to be
+##'   sampling from the prior.  We'll use this for generating initial
+##'   conditions for MCMC where those are not given, and possibly
+##'   other uses.  If not given then when using [monty_sample()] the
+##'   user will have to provide a vector of initial states.
 ##'
 ##' * `gradient`: A function to compute the gradient of `density` with
 ##'   respect to the parameter vector; takes a parameter vector and
@@ -141,11 +141,11 @@ monty_model_properties <- function(has_gradient = NULL,
 ##'   look after their own stream, and that they may need many
 ##'   streams).  Models that provide this method are assumed to be
 ##'   stochastic; however, you can use the `is_stochastic` property
-##'   (via [monty_model_properties()]) to override this (e.g., to
-##'   run a stochastic model with its deterministic expectation).
-##'   This function takes a raw vector of random number state from
-##'   [monty_rng] and uses it to set the random number state for
-##'   your model; this is derived from the random number stream for a
+##'   (via [monty_model_properties()]) to override this (e.g., to run
+##'   a stochastic model with its deterministic expectation).  This
+##'   function takes a raw vector of random number state from a
+##'   `monty_rng` and uses it to set the random number state for your
+##'   model; this is derived from the random number stream for a
 ##'   particular chain, jumped ahead.
 ##'
 ##' * `get_rng_state`: A function to get the RNG state; must be
@@ -304,8 +304,8 @@ monty_model_gradient <- function(model, parameters, named = FALSE) {
 ##'
 ##' @inheritParams monty_model_gradient
 ##'
-##' @param rng Random number state, created by [monty_rng].  Use of
-##'   an RNG with more than one stream may or may not work as
+##' @param rng Random number state, created by [monty_rng_create].
+##'   Use of an RNG with more than one stream may or may not work as
 ##'   expected; this is something we need to tidy up (`mrc-5292`)
 ##'
 ##' @return A vector or matrix of sampled parameters

R/random.R

@@ -156,6 +156,20 @@ monty_rng_long_jump <- function(state, n = 1) {
 }
 
 
+## This was used previously, we have a variant of this in dust2, as
+## well.
+monty_rng_distributed_state <- function(seed = NULL, n_nodes = 1L) {
+  rng <- monty_rng_create(seed = seed)
+  ret <- vector("list", n_nodes)
+  for (i in seq_len(n_nodes)) {
+    ret[[i]] <- monty_rng_state(rng)
+    if (i < n_nodes) {
+      monty_rng_long_jump(rng)
+    }
+  }
+  ret
+}
+
 
 ##' @export
 print.monty_rng_state <- function(x, ...) {