update spelling and add WORDLIST

inst/WORDLIST

@@ -0,0 +1,47 @@
+analyse
+behaviour
+colours
+conf
+csv
+customisation
+customisations
+Customise
+devtools
+epi
+epicurve
+epicurves
+EPIET
+EVD
+Fitzjohn
+ggplot
+github
+grey
+Höhle
+http
+iso
+ISOweek
+isoweeks
+Jombart
+Kamvar
+knitr
+modelled
+modelling
+Modelling
+POSIXct
+pre
+pred
+Programme
+randomised
+repidemicsconsortium
+rmarkdown
+Thibaut
+timespan
+toc
+VignetteEncoding
+VignetteEngine
+VignetteIndexEntry
+visualise
+www
+Www
+yyyy
+Zhian

vignettes/customize_plot.Rmd

@@ -68,7 +68,7 @@ i.hosp
 
 # The `plot.incidence` function
 
-When calling `plot` on an *incidence* object, the function `plot.incidence` is implicitely used. To access its documentation, use `?plot.incidence`. In this section, we illustrate existing customizations.
+When calling `plot` on an *incidence* object, the function `plot.incidence` is implicitly used. To access its documentation, use `?plot.incidence`. In this section, we illustrate existing customisations.
 
 
 ## Default behaviour
@@ -92,15 +92,20 @@ args(incidence:::plot.incidence)
 
 ### The default palette
 
-A color palette is a function which outputs a specified number of colors. By default, the color used in *incidence* is called `incidence_pal1`. Its behaviour is different from usual palettes, in the sense that the first 4 colours are not interpolated:
+A color palette is a function which outputs a specified number of colors. By
+default, the color used in *incidence* is called `incidence_pal1`. Its
+behaviour is different from usual palettes, in the sense that the first 4
+colours are not interpolated:
+
 ```{r, incidence_pal1,   fig.height = 8}
-par(mfrow = c(3,1), mar = c(4,2,1,1))
+par(mfrow = c(3, 1), mar = c(4,2,1,1))
 barplot(1:2, col = incidence_pal1(2))
 barplot(1:4, col = incidence_pal1(4))
 barplot(1:20, col = incidence_pal1(20))
 ```
 
-This pallette also has a light and a dark version:
+This palette also has a light and a dark version:
+
 ```{r, pal2, fig.height = 8}
 par(mfrow = c(3,1))
 barplot(1:20, col = incidence_pal1_dark(20), main = "palette:  incidence_pal1_dark")
@@ -158,7 +163,7 @@ Numerous tweaks for *ggplot2* are documented online. In the following, we merely
 ### Changing date format
 
 By default, the dates indicated on the *x*-axis of an incidence plot may not have the suitable format.
-The package *scales* can be used to change the way dates are labelled (see `?strptime` for possible formats):
+The package *scales* can be used to change the way dates are labeled (see `?strptime` for possible formats):
 
 ```{r, scales1}
 library(scales)

vignettes/incidence_class.Rmd

@@ -131,7 +131,7 @@ get_dates(i.date, position = "center", count_days = TRUE)
 The `$counts` component contains the actual incidence, i.e. counts of events
 for the defined bins. It is a `matrix` of `integers` where rows correspond to
 time intervals, with one column for each group for which incidence is computed
-(a single, unamed column if no groups were provided). If groups were provided,
+(a single, unnamed column if no groups were provided). If groups were provided,
 columns are named after the groups. We illustrate the difference comparing the
 two objects `i` and `i.sex`:
 

vignettes/incidence_fit_class.Rmd

@@ -37,7 +37,7 @@ An `incidence_fit` object contains three elements:
    - `r` The growth rate
    - `r.conf` the confidence interval of `r`
    - `pred` a data frame containing the predictions of the model using the true
-      dates (`dates`), their numberic version used in the model (`dates.x`), the
+      dates (`dates`), their numeric version used in the model (`dates.x`), the
       predicted value (`fit`), and the lower (`lwr`) and upper (`upr`) bounds of
       the associated confidence interval.
    - `doubling` the predicted doubling time in days (only if `r` is positive)
@@ -48,7 +48,7 @@ An `incidence_fit` object contains three elements:
 
 Internally, when `fit()` is run, these elements are constructed by
 function `incidence:::extract_info()`. First we need to setup data. We will use 
-simulated ebola outbreak data from the *outbreaks* package over weekly intervals
+simulated Ebola outbreak data from the *outbreaks* package over weekly intervals
 and calculate the fit for the first 20 weeks:
 
 ```{r fit_dates}
@@ -226,7 +226,7 @@ get_info(fl$fit, "doubling.conf", na.rm = FALSE)
 
 Above, we showed the example of a basic `incidence_fit_list` class with two
 objects representing the fits before and after the peak of an epicurve. However,
-it is often useful evaulate fits for different groups separately. Here, we will
+it is often useful evaluate fits for different groups separately. Here, we will
 construct an incidence object, but define groups by gender:
 
 ```{r incidence_by_gender}

vignettes/overview.Rmd

@@ -75,7 +75,7 @@ The main functions of the package include:
 - **`fit_optim_split`**: finds the optimal date to split the time series in two,
   typically around the peak of the epidemic.
 
-- **`[`**: lower-level subsetting of *incidence* objects, permiting to specify
+- **`[`**: lower-level subsetting of *incidence* objects, permitting to specify
   which dates and groups to retain; uses a syntax similar to matrices,
   i.e. `x[i, j]`, where `x` is the *incidence* object, `i` a subset of dates,
   and `j` a subset of groups.
@@ -173,7 +173,7 @@ plot(i.7.hosp, stack=TRUE) +
 
 
 ## Handling `incidence` objects
-`incidence` objects can be manipulated easily. The `[` operator implements subetting of dates (first argument) and groups (second argument). 
+`incidence` objects can be manipulated easily. The `[` operator implements subsetting of dates (first argument) and groups (second argument). 
 For instance, to keep only the peak of the distribution:
 ```{r, middle}
 i[100:250]