Merge pull request #43 from poissonconsulting/b-vignette

Correct links etc.

DESCRIPTION

@@ -61,4 +61,3 @@ Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
-Config/Needs/website: rmarkdown

vignettes/bisonpic-methods.Rmd

@@ -298,7 +298,7 @@ To ensure the model relates correctly to the day the census estimate was complet
 
 The census data was incorporated using a normal distribution as follows,
 
-\[C_a \sim \text{Normal} \biggl( \sum_{j=1}^J \mathbf{n}_{j,a}, \sigma_{C_a} \biggr) \space T[0, ]\]
+\[C_a \sim \text{Normal} \biggl( \sum_{j=1}^J \mathbf{n}_{j,a}, \sigma_{C_a} \biggr) \text{ T[0, ]}\]
 
 where $C_a$ is the census estimate in the $a^{th}$ aerial survey, $\sum_{j=1}^J \mathbf{n}_{j,a}$ is the total expected abundance on the day of the $a^{th}$ study year, and $\sigma_{C_a}$ is the standard deviation of the census estimate (calculated as the product of the census estimate $C_a$ and its associated CV).
 The normal distribution is lower-truncated at 0 to ensure that the abundance estimates remain positive.
@@ -307,7 +307,7 @@ The normal distribution is lower-truncated at 0 to ensure that the abundance est
 
 The data on the proportion of calves in the herd was incorporated similarly, 
 
-\[P_a \sim \text{Normal}(p_{F0,a} + p_{M0,a}, \sigma_{P_a}) \space T[0, 1]\]
+\[P_a \sim \text{Normal}(p_{F0,a} + p_{M0,a}, \sigma_{P_a}) \text{ T[0, 1]}\]
 
 where $P_a$ is the proportion of calves estimate in the $a^{th}$ aerial survey, $p_{F0,a} + p_{M0,a}$ is the expected proportion of calves on the day of the $a^{th}$ aerial survey, and $\sigma_{P_a}$ is the standard deviation in the estimate of the proportion of calves (calculated as the product of the proportion of calves estimate $P_a$ and its associated CV).
 The normal distribution is truncated at 0 and 1 to ensure that the estimates remain on the probability scale.
@@ -316,7 +316,7 @@ The normal distribution is truncated at 0 and 1 to ensure that the estimates rem
 
 The model is fitted in Stan [@carpenter_stan_2017]. 
 By default, [`bisonpictools`](https://github.com/poissonconsulting/bisonpictools) uses "report" mode and a thinning rate of 10 to save 500 MCMC samples from each of three chains (after discarding the first halves).
-To aid with debugging, other analysis modes ("debug" and "quick") can be set (see the [user guide](https://poissonconsulting.github.io/bisonpictools/user-guide.html) for more detail).
+To aid with debugging, other analysis modes ("debug" and "quick") can be set (see the [user guide](https://poissonconsulting.github.io/bisonpicsuite/articles/bisonpic-user-guide.html) for more detail).
 The user can increment the thinning rate as required to achieve convergence.
 The model-fitting process is automated in the [`runbisonpic`](https://github.com/poissonconsulting/runbisonpic) Shiny app.
 

vignettes/bisonpictools-getting-started.Rmd

@@ -55,15 +55,15 @@ Other packages in this suite include:
 
 ## Getting Help
 
-To get additional information on a particular function just type `?` followed by the name of the function in the R console. For example, ?bpt_analyse brings up the R documentation for the `bisonpictools` analysis function.
+To get additional information on a particular function just type `?` followed by the name of the function in the R console. For example, `?bpt_analyse()` brings up the R documentation for the `bisonpictools` analysis function.
 
 For more information on using R the reader is referred to [R for Data Science](https://r4ds.had.co.nz/) [@wickham_r_2016].
 
 If you discover a bug in `bisonpictools` please file an [issue](https://github.com/poissonconsulting/bisonpictools/issues) with a [reprex](https://reprex.tidyverse.org/) (reproducible example).
 
 ## Data 
 
-The easiest way to prepare the data for use with the `bisonpictools` functions is to download the templated excel spreadsheet from the [shinybisonpic website](https://poissonconsulting.shinyapps.io/shinybisonpic-dev).
+The easiest way to prepare the data for use with the `bisonpictools` functions is to download the templated excel spreadsheet from the [shinybisonpic website](https://poissonconsulting.shinyapps.io/shinybisonpic/).
 Click the **Download Template** button, and populate each sheet with your data.
 
 - `event_data` contains the camera trap event data, including the date and time of each event, location of the camera trap, and the number of individuals in each class
@@ -148,8 +148,8 @@ bpt_plot_ratios(
 
 ### `shinybisonpic`
 
-The [`shinybisonpic` Shiny app](https://poissonconsulting.shinyapps.io/shinybisonpic-dev/) can be used to explore the locations of camera traps and the the ratios of different classes of wood bison in camera trap observations in a user-friendly manner.
-Refer to the [user guide](https://poissonconsulting.github.io/bisonpicsuite/bisonpic-user-guide.html) for more information on how to use the `shinybisonpic` app.
+The [`shinybisonpic` Shiny app](https://poissonconsulting.shinyapps.io/shinybisonpic) can be used to explore the locations of camera traps and the the ratios of different classes of wood bison in camera trap observations in a user-friendly manner.
+Refer to the [user guide](https://poissonconsulting.github.io/bisonpicsuite/articles/bisonpic-user-guide.html) for more information on how to use the `shinybisonpic` app.
 
 ## Data Analysis
 
@@ -350,7 +350,7 @@ remotes::install_github("poissonconsulting/runbisonpic")
 runbisonpic::launch_runbisonpic()
 ```
 
-See the [user guide](https://poissonconsulting.github.io/bisonpicsuite/bisonpic-user-guide.html) for more guidance on how to use the `runbisonpic` app.
+See the [user guide](https://poissonconsulting.github.io/bisonpicsuite/articles/bisonpic-user-guide.html) for more guidance on how to use the `runbisonpic` app.
 
 ## Going beyond `bisonpictools`