Merge pull request #84 from annecori/windows_check_fix

Windows check fix

.Rbuildignore

@@ -3,3 +3,5 @@ fitRotavirus.R
 ^\.Rproj\.user$
 ^\.travis\.yml$
 ^codecov\.yml$
+^CRAN-RELEASE$
+^cran-comments\.md$

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: EpiEstim
-Version: 2.2-0
-Title: Estimate Time Varying Reproduction Numbers From Epidemic Curves
+Version: 2.2-1
+Title: Estimate Time Varying Reproduction Numbers from Epidemic Curves
 Authors@R: c(person("Anne", "Cori", email = "[email protected]", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-8443-9162")), 
   person("Simon", "Cauchemez", role = "ctb"),
   person("Neil M.", "Ferguson", role = "ctb", comment = c(ORCID = "0000-0002-1154-8093")),
@@ -17,7 +17,7 @@ Authors@R: c(person("Anne", "Cori", email = "[email protected]", role = c("a
   person("Rolina", "van Gaalen", role = "ctb")
   )
 Maintainer: Anne Cori <[email protected]>
-Description: This package provides tools to quantify transmissibility throughout
+Description: Tools to quantify transmissibility throughout
     an epidemic from the analysis of time series of incidence as described in
     Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) 
     <doi:10.1093/aje/kwh255>.

EpiEstim.Rproj

@@ -0,0 +1,17 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source

R/check_cdt_samples_convergence.R

@@ -13,7 +13,7 @@
 #' @return TRUE if the Gelman Rubin test for convergence was successful, FALSE 
 #' otherwise
 #' @details{
-#' This function splits an MCMC chain in two halfs and uses the Gelman-Rubin 
+#' This function splits an MCMC chain in two halves and uses the Gelman-Rubin 
 #' algorithm to assess convergence of the chain by comparing its two halves.
 #' }
 #' @seealso \code{\link{estimate_R}}

R/compatibility.R

@@ -102,7 +102,11 @@ DiscrSI <- function(k, mu, sigma) {
 
 #' @export
 OverallInfectivity <- function(I, SI.Distr) {
-  .Deprecated("overall_infectivity")
+  # We will eventually properly deprecate this, but it is being used in other 
+  # packages at the moment, so soft deprecate for now.
+  if (interactive()) {
+    .Deprecated("overall_infectivity")
+  }
   overall_infectivity(incid = I, si_distr = SI.Distr)
 }
 

R/data.R

@@ -5,7 +5,7 @@
 #' This data set gives:
 #'
 #' 1. the daily incidence of self-reported and laboratory-confirmed cases of 
-#'    influenza amongst children in a school in New York city during the 2009
+#'    influenza among children in a school in New York city during the 2009
 #'    H1N1 influenza pandemic (see source and references),
 #' 2. interval-censored serial interval data from the 2009 outbreak of H1N1 
 #'    influenza in a New York city school (see references).
@@ -123,7 +123,7 @@ NULL
 #'
 #' 1. the daily incidence of onset of acute respiratory illness
 #'    (ARI, defined as at least two symptoms among fever, cough, sore throat,
-#'    and runny nose) amongst children in a school in Pennsylvania during the
+#'    and runny nose) among children in a school in Pennsylvania during the
 #'    2009 H1N1 influenza pandemic (see source and references),
 #' 2. the discrete daily distribution of the serial interval for influenza, 
 #'    assuming a shifted Gamma distribution with mean 2.6 days, standard
@@ -361,7 +361,7 @@ NULL
 #'     observation corresponds to a pair of infector/infected individuals. EL
 #'     and ER columns contain the lower an upper bounds of the dates of
 #'     symptoms onset in the infectors. SL and SR columns contain the lower an
-#'     upper bounds of the dates of symptoms onset in the infected indiviuals.
+#'     upper bounds of the dates of symptoms onset in the infected individuals.
 #'     The type column has entries 0, 1, or 2, corresponding to doubly
 #'     interval-censored, single interval-censored or exact observations,
 #'     respectively, see Reich et al.  Statist. Med. 2009
@@ -401,7 +401,7 @@ NULL
 #' 1. the daily incidence of onset of symptoms of laboratory confirmed human
 #'    infections with MERS-CoV in Saudi Arabia between the beginning of July
 #'    2014 and the end of December 2015, and 
-#' 2. estimates of the mean and standrad deviation of the serial interval for
+#' 2. estimates of the mean and standard deviation of the serial interval for
 #'    MERS.
 #'
 #' @name mers_2014_15

R/discr_si.R

@@ -9,7 +9,7 @@
 #' \code{discr_si} computes the discrete distribution of the serial interval, 
 #' assuming that the serial interval is shifted Gamma distributed, with shift 1.
 #'
-#' @param k Positive integer, or vector of positive ingerers for which the 
+#' @param k Positive integer, or vector of positive interers for which the 
 #' discrete distribution is desired.
 #' @param mu A positive real giving the mean of the Gamma distribution.
 #' @param sigma A non-negative real giving the standard deviation of the Gamma 

R/estimate_r.R

@@ -101,7 +101,7 @@
 #'  alternative to the previous method, where the MCMC estimation of the serial
 #'  interval distribution could be run once, and the same estimated SI
 #'  distribution then used in estimate_R in different contexts, e.g. with
-#'  different time windows, hence avoiding to rerun the MCMC everytime
+#'  different time windows, hence avoiding to rerun the MCMC every time
 #'  estimate_R is called.}
 #' }
 #' 

R/init_mcmc_params.R

@@ -8,20 +8,20 @@
 #' \code{estimate_R}
 #' 
 #' \code{init_mcmc_params} Finds values of the serial interval distribution 
-#' parameters, used to initalise the MCMC estimation of the serial interval 
+#' parameters, used to initialise the MCMC estimation of the serial interval 
 #' distribution. Initial values are computed based on the observed mean and 
 #' standard deviation of the sample from which the parameters are to be 
-#' estiamted.
+#' estimated.
 #' 
 #' @param si_data the data on dates of symptoms of pairs of infector/infected
 #'   individuals to be used to estimate the serial interval distribution. This
 #'   should be a dataframe with 5 columns: \itemize{ \item{EL: the lower bound
 #'   of the symptom onset date of the infector (given as an integer)} \item{ER:
 #'   the upper bound of the symptom onset date of the infector (given as an
 #'   integer). Should be such that ER>=EL} \item{SL: the lower bound of the
-#'   symptom onset date of the infected indivdiual (given as an integer)} 
+#'   symptom onset date of the infected individual (given as an integer)} 
 #'   \item{SR: the upper bound of the symptom onset date of the infected
-#'   indivdiual (given as an integer). Should be such that SR>=SL} \item{type
+#'   individual (given as an integer). Should be such that SR>=SL} \item{type
 #'   (optional): can have entries 0, 1, or 2, corresponding to doubly
 #'   interval-censored, single interval-censored or exact observations, 
 #'   respectively, see Reich et al. Statist. Med. 2009. If not specified, this

R/make_config.R

@@ -4,7 +4,7 @@
 #' items as input, set defaults where arguments are
 #' missing, and return a list of settings. 
 #'
-#' @param ... Acceptables arguments for ... are:
+#' @param ... Acceptable arguments for ... are:
 #'
 #' \describe{
 #'
@@ -31,7 +31,7 @@
 #' serial interval (method "uncertain_si", see details).}
 #' 
 #' \item{std_si}{For method "parametric_si" and "uncertain_si" ; non negative
-#' real giving the stadard deviation of the serial interval
+#' real giving the standard deviation of the serial interval
 #' (method "parametric_si") or the average standard deviation of the serial
 #' interval (method "uncertain_si", see details).}
 #' 
@@ -165,7 +165,7 @@
 #' symmetric around the mean.
 #' For each pair \eqn{(\mu^{(k)},\sigma^{(k)})}, we then draw a sample of size
 #' \code{n2} in the posterior distribution of the reproduction number over each
-#' time window, conditionnally on this serial interval distribution.
+#' time window, conditionally on this serial interval distribution.
 #' After pooling, a sample of size \eqn{\code{n1}\times\code{n2}} of the joint
 #' posterior distribution of the reproduction number over each time window is
 #' obtained.
@@ -191,17 +191,17 @@
 #' \item{ER: the upper bound of the symptom onset date of the infector (given as
 #'  an integer). Should be such that ER>=EL}
 #' \item{SL: the lower bound of the symptom onset date of the infected
-#' indivdiual (given as an integer)}
+#' individual (given as an integer)}
 #' \item{SR: the upper bound of the symptom onset date of the infected
-#' indivdiual (given as an integer). Should be such that SR>=SL}
+#' individual (given as an integer). Should be such that SR>=SL}
 #' \item{type (optional): can have entries 0, 1, or 2, corresponding to doubly
 #' interval-censored, single interval-censored or exact observations,
 #' respectively, see Reich et al. Statist. Med. 2009. If not specified, this
 #' will be automatically computed from the dates}
 #' }
 #' Assuming a given parametric distribution for the serial interval distribution
 #'  (specified in si_parametric_distr),
-#' the posterior distribution of the serial interval is estimated directly fom
+#' the posterior distribution of the serial interval is estimated directly from
 #' these data using MCMC methods implemented in the package
 #' \code{coarsedatatools}.
 #' The argument \code{mcmc_control} is a list of characteristics which control
@@ -214,7 +214,7 @@
 #' For each element in the posterior sample of serial interval distribution,
 #' we then draw a sample of size \code{n2} in the posterior distribution of the
 #' reproduction number over each time window,
-#' conditionnally on this serial interval distribution.
+#' conditionally on this serial interval distribution.
 #' After pooling, a sample of size \eqn{\code{n1}\times\code{n2}} of the joint
 #' posterior distribution of
 #' the reproduction number over each time window is obtained.

R/make_mcmc_control.R

@@ -26,9 +26,9 @@
 #' \item{ER: the upper bound of the symptom onset date of the infector (given as
 #'  an integer). Should be such that ER>=EL}
 #' \item{SL: the lower bound of the symptom onset date of the infected
-#' indivdiual (given as an integer)}
+#' individual (given as an integer)}
 #' \item{SR: the upper bound of the symptom onset date of the infected
-#' indivdiual (given as an integer). Should be such that SR>=SL}
+#' individual (given as an integer). Should be such that SR>=SL}
 #' \item{type (optional): can have entries 0, 1, or 2, corresponding to doubly
 #' interval-censored, single interval-censored or exact observations,
 #' respectively, see Reich et al. Statist. Med. 2009. If not specified, this

R/plot.R

@@ -21,8 +21,8 @@
 #'   series plot, to add the incidence of imported cases.
 #'
 #' @param options_I For what = "incid" or "all". A list of graphical options:
-#'   \describe{ \item{col}{A colour or vector of colours used for plotting
-#'   incid. By default uses the default R colours.}  \item{transp}{A numeric
+#'   \describe{ \item{col}{A color or vector of colors used for plotting
+#'   incid. By default uses the default R colors.}  \item{transp}{A numeric
 #'   value between 0 and 1 used to monitor transparency of the bars
 #'   plotted. Defaults to 0.7.}  \item{xlim}{A parameter similar to that in
 #'   \code{par}, to monitor the limits of the horizontal axis} \item{ylim}{A
@@ -33,8 +33,8 @@
 #'   incidence plot}}
 #'
 #' @param options_R For what = "R" or "all". A list of graphical options:
-#'   \describe{ \item{col}{A colour or vector of colours used for plotting R. By
-#'   default uses the default R colours.}  \item{transp}{A numeric value between
+#'   \describe{ \item{col}{A color or vector of colors used for plotting R. By
+#'   default uses the default R colors.}  \item{transp}{A numeric value between
 #'   0 and 1 used to monitor transparency of the 95\%CrI. Defaults to 0.2.}
 #'   \item{xlim}{A parameter similar to that in \code{par}, to monitor the
 #'   limits of the horizontal axis} \item{ylim}{A parameter similar to that in
@@ -45,8 +45,8 @@
 #'   \describe{ \item{prob_min}{A numeric value between 0 and 1. The SI
 #'   distributions explored are only shown from time 0 up to the time t so that
 #'   each distribution explored has probability < \code{prob_min} to be on any
-#'   time step after t. Defaults to 0.001.}  \item{col}{A colour or vector of
-#'   colours used for plotting the SI. Defaults to black.}  \item{transp}{A
+#'   time step after t. Defaults to 0.001.}  \item{col}{A color or vector of
+#'   colors used for plotting the SI. Defaults to black.}  \item{transp}{A
 #'   numeric value between 0 and 1 used to monitor transparency of the
 #'   lines. Defaults to 0.25} \item{xlim}{A parameter similar to that in
 #'   \code{par}, to monitor the limits of the horizontal axis} \item{ylim}{A

R/wallinga_teunis.R

@@ -32,7 +32,7 @@
 #'   \code{t_start[i]<=t_end[i]}.} \item{method: One of "non_parametric_si" or
 #'   "parametric_si" (see details).} \item{mean_si: For method "parametric_si" ;
 #'   positive real giving the mean serial interval.} \item{std_si: For method
-#'   "parametric_si" ; non negative real giving the stadard deviation of the
+#'   "parametric_si" ; non negative real giving the standard deviation of the
 #'   serial interval.} \item{si_distr: For method "non_parametric_si" ; vector
 #'   of probabilities giving the discrete distribution of the serial interval,
 #'   starting with \code{si_distr[1]} (probability that the serial interval is

vignettes/demo.Rmd

@@ -73,7 +73,7 @@ number R. For this, we need to specify i) the time window(s) over which to
 estimate R and ii) information on the distribution of the 
 serial interval.
 
-For i), the default behaviour is to estimate R over weekly sliding windows. 
+For i), the default behavior is to estimate R over weekly sliding windows. 
 This can be changed through the `config$t_start` and `config$t_end` arguments 
 (see below, "Changing the time windows for estimation"). 
 For ii), there are several options, specified in the `method` argument. 
@@ -337,7 +337,7 @@ indicating the dates and the number of incident cases for each date.
 `estimate_R` accepts a range of formats for its `incid` argument. 
 
 This can be a table, as we've seen. In that case there needs to be a column 
-called `I` containing the numebr of incident cases. If a column `dates` is 
+called `I` containing the number of incident cases. If a column `dates` is 
 present it is used in the x-axes when producing plots:
 
 ```{r incid_table}