R 4.0 compatibility (#21)

DESCRIPTION

@@ -1,14 +1,14 @@
 Package: fitR
 Title: Tool box for fitting dynamic infectious disease models to time series
-Version: 0.2
+Version: 0.2.1
 Authors@R: c(person(given="Anton", family="Camacho", email="[email protected]",
  role=c("aut")),person(given="Sebastian", family="Funk",
  email="[email protected]", role=c("aut", "cre")))
 Description: This package contains helper functions for model fitting and
  inference for infectious disease dynamics. It has been designed mainly as
  a teaching tool for a one-week course on the topic.
 Depends:
- R (>= 3.5.0),
+ R (>= 4.0.0),
  adaptivetau
 Imports:
  ggplot2,

NEWS.md

@@ -1,3 +1,7 @@
+# fitR 0.2.1
+
+* Replace all instances of `\(x)` with `function(x)` for R 4.0 compatibility
+
 # fitR 0.2
 
 * Complete restyled package using `lintr`

R/logLikelihood.r

@@ -137,7 +137,7 @@ rTrajObs <- function(fitmodel, theta, initState, times) {
  ## each row of traj. The parameter value theta as passed as
  ## extra argument to fitmodel$rPointObs
  obs <- split(traj, f = traj$time)
- obs <- map(obs, \(x) {
+ obs <- map(obs, function (x) {
  data.frame(
  time = unique(x$time),
  obs = fitmodel$rPointObs(x, theta = theta)

R/plot.r

@@ -105,7 +105,7 @@ plotTraj <- function(traj = NULL, stateNames = NULL, data = NULL,
  infected = eval(parse(text = paste(nonExtinct, collapse = "+")), traj)
  )
  dfPExt <- split(traj, f = traj[[timeColumn]])
- dfPExt <- map(dfPExt, \(df) {
+ dfPExt <- map(dfPExt, function(df) {
  tmp <- data.frame(value = sum(df$infected == 0) / nrow(df))
  tmp[[timeColumn]] <- unique(df[[timeColumn]])
  return(tmp)
@@ -129,7 +129,7 @@ plotTraj <- function(traj = NULL, stateNames = NULL, data = NULL,
  message("Compute confidence intervals")
 
  trajCI <- split(dfTraj, dfTraj[c(timeColumn, "state")])
- trajCI <- map(trajCI, \(df) {
+ trajCI <- map(trajCI, function (df) {
  tmp <- as.data.frame(
  t(quantile(df$value, prob = c(0.025, 0.25, 0.5, 0.75, 0.975)))
  )
@@ -352,7 +352,7 @@ plotSMC <- function(smc, fitmodel, theta, data = NULL, summary = TRUE,
  names(traj) <- seq_along(traj)
 
  traj <- map(traj, function(df) {
- obs <- apply(df, 1, \(x) fitmodel$rPointObs(x, theta = theta))
+ obs <- apply(df, 1, function (x) fitmodel$rPointObs(x, theta = theta))
  trajObs <- left_join(df, obs, by = "time")
 
  return(trajObs)
@@ -446,7 +446,7 @@ plotPosteriorDensity <- function(trace, prior = NULL, colour = NULL,
 
  if (inherits_any(trace, c("mcmc.list", "list"))) {
  ## convert to data farmes
- trace <- map(trace, \(x) {
+ trace <- map(trace, function(x) {
  as.data.frame(as.matrix(x))
  })
 
@@ -605,7 +605,7 @@ plotPosteriorFit <- function(trace, fitmodel, initState, data,
  if (inherits(trace, "mcmc")) {
  trace <- as.data.frame(trace)
  } else if (inherits(trace, "mcmc.list")) {
- trace <- map(trace, \(x) {
+ trace <- map(trace, function(x) {
  as.data.frame(as.matrix(x))
  })
  trace <- bind_rows(trace)

R/smc.r

@@ -71,7 +71,7 @@ particleFilter <- function(fitmodel, theta, initState, data, nParticles,
  currentStateParticles <- currentStateParticles[indexResampled]
 
  # propagate particles (this for loop could be parallelized)
- propagate <- map(currentStateParticles, \(currentState) {
+ propagate <- map(currentStateParticles, function (currentState) {
  # simulate from previous observation to current observation time
  traj <- fitmodel$simulate(
  theta = theta, initState = currentState, times = times
@@ -87,13 +87,13 @@ particleFilter <- function(fitmodel, theta, initState, data, nParticles,
  })
 
  # collect parallel jobs
- currentStateParticles <- map(propagate, \(x) {
+ currentStateParticles <- map(propagate, function (x) {
  x$state
  })
- weightParticles <- unlist(map(propagate, \(x) {
+ weightParticles <- unlist(map(propagate, function (x) {
  x$weight
  }))
- trajParticles <- map(seq_along(propagate), \(j) {
+ trajParticles <- map(seq_along(propagate), function (j) {
  rbind(trajParticles[[j]], c(dataPoint["time"], propagate[[j]]$state))
  })