commit 3739aaa756f3db336f8ef09e1cc548343f63b86f
Author: Alex Zarebski <aezarebski@gmail.com>
Date:   Tue Nov 8 10:45:12 2022 +0000

    update image files

commit ed438429961d4bae67efdd3b283bc7328cde8912
Author: Alex Zarebski <aezarebski@gmail.com>
Date:   Tue Nov 8 10:44:20 2022 +0000

    update docs and script to allow for possibility of not estimating prevalence

commit 4e9a6cc7527e82e7e6360a039076049e68e7e739
Author: Alex Zarebski <aezarebski@gmail.com>
Date:   Tue Nov 8 10:37:12 2022 +0000

    update materials to work with new versions

commit 73ed88f34b2b620541841939eecca43a6b8b517e
Author: Alex Zarebski <aezarebski@gmail.com>
Date:   Mon Nov 7 19:29:29 2022 +0000

    upgrade simulation xml to use remaster

Constant-Parameters-Canonical.org

@@ -4,7 +4,11 @@ Created by Alexander Zarebski
 
 Edited by [[https://github.com/BernardoGG][Bernardo Gutierrez]] on 12/08/2022
 
-Last edited using =BEAST v2.6.7= and =timtam v0.3.1=.
+Last edited using
+- =BEAST v2.7.1=
+- =remaster v1.0.0=
+- =timtam v0.4.0=
+
 
 This tutorial demonstrates how to use TimTam to estimate the reproduction number
 and prevalence of infection using a sequence alignment and a time series of
@@ -63,7 +67,9 @@ expected for an RNA virus. The origin time for our simulated epidemic took place
    [[https://github.com/aezarebski/timtam2/wiki/tutorial-0/aggregated-occurrences.csv][aggregated-occurrences.csv]] files there.
 2. Create an XML file using BEAUti.
    1. Load the sequence data into BEAUti
-   2. Parse the tip-dates using the default auto-configured values function.
+   2. Parse the tip-dates using the default auto-configured values function. In
+      order for this to work, you will need to adjust the formatter so that it
+      will take everything after the last underscore (i.e. =_=).
    3. Leave the site model tab with the default values.
    4. Set the clock rate to =0.001= in the clock model tab.
    5. Select the /Tim Tam Model/ on the tree prior tab.
@@ -86,6 +92,9 @@ expected for an RNA virus. The origin time for our simulated epidemic took place
    - Ensure the =originTime= is set to \(70.0\).
    - Remove all references to =historySizes= and =historyChecks=, these are used
      to estimate historical prevalence and are not needed for this example.
+     *WARNING* removing the history size checks will mean that you can't
+     estimate the prevalence, if you are interested in how to do this, read the
+     subsequent tutorials or look at the [[https://github.com/aezarebski/timtam2/wiki/tutorial-1/timtam.xml][timtam.xml]] which still has these included.
    - Copy and paste the two number series from the R script output as the values
      of =disasterTimes= and =disasterSizes= respectively, in the TimTam
      distribution node. You should recognise it by the =id=
@@ -115,12 +124,14 @@ Note how in the plot, the 95% HPD includes the real value of the basic reproduct
 number. We would expect to recover the true value of an estimated parameter credible
 interval when working with real-world data.
 
-The second figure shows the posterior distribution of the prevalence at the time
-of the last observation, i.e. the number of infectious people at the present time.
-Recall from the observation we made above that this number should be approximately
-2250 infectious cases at that time. How does the estimate from the figure below and
-the estimate from your own analysis compare to the true number of infectious cases
-at this last time point?
+If you have run the MCMC with the history size checks included, the
+post-processing will generate a second figure. The second figure shows the
+posterior distribution of the prevalence at the time of the last observation,
+i.e. the number of infectious people at the present time. Recall from the
+observation we made above that this number should be approximately 2250
+infectious cases at that time. How does the estimate from the figure below and
+the estimate from your own analysis compare to the true number of infectious
+cases at this last time point?
 
 [[https://raw.githubusercontent.com/wiki/aezarebski/timtam2/images/tutorial-1-prevalence.png]]