Merge pull request #65 from mrc-ide/mrc-5709

Package tidyup

DESCRIPTION

@@ -1,8 +1,11 @@
 Package: monty
 Title: Monte Carlo Models
-Version: 0.2.0
+Version: 0.2.1
 Authors@R: c(person("Rich", "FitzJohn", role = c("aut", "cre"),
                     email = "[email protected]"),
+             person("Wes", "Hinsley", role = "aut"),
+             person("Ed", "Knock", role = "aut"),
+             person("Marc", "Baguelin", role = "aut"),
              person("Imperial College of Science, Technology and Medicine",
                     role = "cph"))
 Description: Experimental sources for the next generation of mcstate,

R/dsl-differentiate-expr.R

@@ -1,14 +1,14 @@
-##' Differentiate expressions in the monty dsl.  This function is
+##' Differentiate expressions in the monty DSL.  This function is
 ##' exported for advanced use, and really so that we can use it from
 ##' odin.  But it has the potential to be generally useful, so while
 ##' we'll tweak the interface quite a lot over the next while it is
 ##' fine to use if you can handle some disruption.
 ##'
 ##' R already has support for differentiating expressions using [D],
 ##' which is useful for creating derivatives of simple functions to
-##' pass into nonlinear optimisation.  We need something a bit more
-##' flexible for differentiating models in the monty dsl
-##' ([monty_dsl]) and also in the related odin dsl.
+##' pass into non-linear optimisation.  We need something a bit more
+##' flexible for differentiating models in the monty DSL
+##' ([monty_dsl]) and also in the related odin DSL.
 ##'
 ##' # Differences to [D()]
 ##'
@@ -33,7 +33,7 @@
 ##' * `differentiate`: A function that can differentiate an expression
 ##'   with respect to a variable (as a string).
 ##'
-##' * `maths`: Some mathmatical utilities for constructing
+##' * `maths`: Some mathematical utilities for constructing
 ##'   expressions.  This will be documented later, but the most useful
 ##'   bits on here are the function elements `times`, `plus` and
 ##'   `plus_fold`.
@@ -147,7 +147,7 @@ derivative <- list(
     maths$times(differentiate(a, name), call("exp", a))
   },
   log1p = function(expr, name) {
-    ## log(1 + x)
+    ## > log(1 + x)
     a <- maths$rewrite(expr[[2]])
     maths$divide(differentiate(a, name), maths$plus(1, a))
   },
@@ -223,8 +223,8 @@ derivative <- list(
     ## 'k' the way that the density is defined in R.
     ##
     ## We can consider this as:
-    ## lfactorial(n) - lfactorial(k) - lfactorial(n - k)
-    ## ^--a            ^--b            ^--c
+    ## > lfactorial(n) - lfactorial(k) - lfactorial(n - k)
+    ## > ^--a            ^--b            ^--c
     ##
     ## and then apply the chain rule as:
     n <- maths$rewrite(expr[[2]])

R/dsl-distributions.R

@@ -1,4 +1,4 @@
-##' Report information about supported distributions in the dsl.  This
+##' Report information about supported distributions in the DSL.  This
 ##' is primarily intended for use in packages which use
 ##' [monty_dsl_parse_distribution], as this function reports
 ##' information about which distributions and arguments would succeed

R/dsl-preprocess.R

@@ -67,7 +67,7 @@ preprocess_detect <- function(x, type, call = NULL) {
     } else if (length(x) != 1L || grepl("([\n;=]|<-| ~ )", x)) {
       ## Our heuristic above is a little fragile; it will think that
       ##
-      ## a~f(1)
+      ## > a~f(1)
       ##
       ## is a filename, but we need the spaces there to avoid windows
       ## short paths getting considered to be code.  Parentheses would

R/dsl.R

@@ -69,7 +69,7 @@ monty_dsl_parse <- function(x, type = NULL, gradient = NULL) {
 
 ##' Parse an expression as if it were a call to one of monty's
 ##' distribution functions (e.g., `Normal`, `Poisson`).  This will
-##' fill in any defaults, disambiguate where mulitple
+##' fill in any defaults, disambiguate where multiple
 ##' parameterisations of the distribution are available, and provide
 ##' links through to the C++ API.  This function is designed for use
 ##' from other packages that use monty, and is unlikely to be

R/model.R

@@ -74,8 +74,8 @@ monty_model_properties <- function(has_gradient = NULL,
 ##'   could be anything really.  Models can return `-Inf` if things
 ##'   are impossible, and we'll try and cope gracefully with that
 ##'   wherever possible.  If the property `allow_multiple_parameters`
-##'   is `TRUE`, then this function must be able to handle the 
-##'   argument parameter being a matrix,  and return a vector 
+##'   is `TRUE`, then this function must be able to handle the
+##'   argument parameter being a matrix,  and return a vector
 ##'   of densities.
 ##'
 ##' * `parameters`: A character vector of parameter names.  This
@@ -138,7 +138,7 @@ monty_model_properties <- function(has_gradient = NULL,
 ##'   (especially if we explore more complex nestings) but at present
 ##'   parameters with group 0 affect everything (so are accepted or
 ##'   rejected as a whole), while parameters in groups 1 to `n` are
-##'   indepenent (for example, changing the parameters in group 2 does
+##'   independent (for example, changing the parameters in group 2 does
 ##'   not affect the density of parameters proposed in group 3).
 ##'
 ##' @title Create basic model
@@ -265,7 +265,7 @@ monty_model_gradient <- function(model, parameters, named = FALSE) {
 ##'
 ##' @param rng Random number state, created by [monty_rng].  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)
+##'   expected; this is something we need to tidy up (`mrc-5292`)
 ##'
 ##' @return A vector or matrix of sampled parameters
 ##'

R/packer.R

@@ -88,7 +88,7 @@
 ##' sort of output.  Assume you have a matrix `m` with 3 rows and 2
 ##' columns; this means that we have two sets of parameters or state
 ##' (one per column) and 3 states within each; this is the format that
-##' mcmc parameters will be in for example.
+##' MCMC parameters will be in for example.
 ##'
 ##' The first would to be return a list where the `i`th element is the
 ##' result of unpacking the `i`th parameter/state vector.  You can do
@@ -112,7 +112,7 @@
 ##' matrices (one column per set) and so on.
 ##'
 ##' This approach generalises to higher dimensional input, though we
-##' suspect you'll spend a bit of time headscratching if you use it.
+##' suspect you'll spend a bit of time head-scratching if you use it.
 ##'
 ##' We do not currently offer the ability to pack this sort of output
 ##' back up, though it's not hard.  Please let us know if you would

R/runner-simultaneous.R

@@ -40,10 +40,10 @@ monty_runner_simultaneous <- function(progress = NULL) {
     rng_state <- lapply(rng, function(r) r$state())
     ## TODO: get the rng state back into 'rng' here, or (better) look
     ## at if we should just be using seed instead here perhaps?
-    ## rng_state <- matrix(res$internal$state$rng, ncol = n_chains)
-    ## for (i in seq_len(n_chains)) {
-    ##   rng[[i]]$set_state(rng_state[, i]) # not supported!
-    ## }
+    ## > rng_state <- matrix(res$internal$state$rng, ncol = n_chains)
+    ## > for (i in seq_len(n_chains)) {
+    ## >   rng[[i]]$set_state(rng_state[, i]) # not supported!
+    ## > }
     monty_run_chains_simultaneous(pars, model, sampler, observer,
                                   n_steps, progress, rng_state)
   }

R/runner.R

@@ -49,7 +49,7 @@ 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 the "worker"
-##' support in mcstate version 1 this is very simple and we'll improve
+##' support in `mcstate` version 1 this is very simple and we'll improve
 ##' it over time.  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 support to warn you if your number of

R/sampler-adaptive.R

@@ -223,7 +223,7 @@ monty_sampler_adaptive <- function(initial_vcv,
 calc_scaling_increment <- function(n_pars, acceptance_target,
                                    log_scaling_update) {
   if (log_scaling_update) {
-    A <- -stats::qnorm(acceptance_target / 2)
+    A <- -stats::qnorm(acceptance_target / 2) # nolint
 
     scaling_increment <-
       (1 - 1 / n_pars) * (sqrt(2 * pi) * exp(A^2 / 2)) / (2 * A) +