- add salix dropsite

salix/HEADER.html

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+<h2>ICTP Spring College Course on Model-based Inference in Ecology &amp; Epidemiology</h2>

salix/Makefile

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+PUBSITE = kinglab.eeb.lsa.umich.edu:/var/www/html/ICTP_Spring_College
+
+REXE = R --vanilla
+RSCRIPT = Rscript --vanilla
+RCMD = $(REXE) CMD
+PDFLATEX = pdflatex
+BIBTEX = bibtex
+MAKEIDX = makeindex
+CP = cp
+RM = rm -f
+
+default: index.html
+
+publish: index.html
+	rsync -avz --delete-after --exclude=cache --exclude=figure --chmod=a+rX,go-w $+ $(PUBSITE)
+
+%.html: %.Rmd
+	PATH=/usr/lib/rstudio/bin/pandoc:$$PATH \
+	Rscript --vanilla -e "rmarkdown::render(\"$*.Rmd\",output_format=\"html_document\")"
+
+%.html: %.md
+	PATH=/usr/lib/rstudio/bin/pandoc:$$PATH \
+	Rscript --vanilla -e "rmarkdown::render(\"$*.md\",output_format=\"html_document\")"
+
+%.R: %.Rmd
+	Rscript --vanilla -e "library(knitr); purl(\"$*.Rmd\",output=\"$*.R\")"
+
+clean:
+	$(RM) *.o *.so *.log *.aux *.out *.nav *.snm *.toc *.bak
+	$(RM) Rplots.ps Rplots.pdf
+
+fresh: clean
+	$(RM) cache figure

salix/README.html

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+<a href="http://kinglab.eeb.lsa.umich.edu/ICTP_Spring_College/">Back to main page</a>

salix/index.Rmd

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+---
+title: 'Model-based Inference in Ecology and Epidemiology'
+date: ICTP Spring College 9--20 May 2016
+output: html_document
+
+---
+
+```{r knitr-opts,include=FALSE,purl=FALSE,cache=FALSE}
+library(knitr)
+require(plyr)
+require(reshape2)
+require(magrittr)
+
+prefix <- "syllabus"
+opts_chunk$set(
+  progress=TRUE,
+  prompt=FALSE,tidy=FALSE,highlight=TRUE,
+  strip.white=TRUE,
+  warning=FALSE,
+  message=FALSE,
+  error=FALSE,
+  echo=FALSE,
+  cache=FALSE,
+  cache.extra=rand_seed,
+  results='markup',
+  fig.show='asis',
+  size='small',
+  fig.lp="fig:",
+  fig.path=paste0("figure/",prefix,"-"),
+  cache.path=paste0("cache/",prefix,"-"),
+  fig.pos="h!",
+  fig.align='center',
+  fig.height=4,fig.width=6.83,
+  dpi=300,
+  dev='png',
+  dev.args=list(bg='transparent')
+  )
+
+options(
+  keep.source=TRUE,
+  stringsAsFactors=FALSE,
+  encoding="UTF-8"
+  )
+```
+
+### Instructor
+
+Prof.&nbsp;Aaron A. King, Ph.D.  
+Departments of Ecology & Evolutionary Biology and Mathematics  
+University of Michigan  
+Email: [email protected]  
+
+### Perspective
+
+Ecological and epidemiological systems are particularly interesting from the physical point of view.
+Their complexity and high-dimensionality makes it natural to approach them as stochastic, nonlinear dynamical systems and within this context, many questions of both intrinsic interest and practical concern can be formulated.
+To answer these questions, it is necessary to rigorously confront hypothetical models with data.
+In this regard, time-series data are of particular value inasmuch as they have the potential to express the characteristic signatures of causal mechanisms.
+This course will take students into the heart of these issues via an introduction to ecological and epidemiological stochastic dynamical systems models using a series of examples with real data.
+Students will learn how to formulate questions as models and answer the questions using state-of-the-art inference algorithms.
+
+### Course objectives
+
+1. to introduce partially observed Markov process (POMP) models as tools for scientific investigation
+1. to give students the ability to formulate POMP models of their own
+1. to teach efficient approaches for performing scientific inference using POMP models
+1. to familiarize students with the **pomp** package
+1. to give students opportunities to work with such inference methods
+1. to provide documented examples for student re-use
+
+### Prerequisites
+
+- Familiarity with deterministic dynamics (discrete-time maps, ordinary differential equations) and probability.
+- Some programming experience, in any language.
+- Completion of the [**R** tutorial](http://kinglab.eeb.lsa.umich.edu/R_Tutorial) before the beginning of the course.
+- A sense of humor.
+
+### Format and expectations
+
+The course will be taught using a mixture of lectures and computational laboratory exercises.
+Students are expected to complete assigned readings before class meetings, keep up with assigned homework, participate fully in discussions, and work on course activities during class meetings.
+
+### Course website
+
+Materials and readings will be posted on the [course website](http://kingaa.github.io/short-course).
+
+### Computing in **R**
+
+We will make extensive use of the open-source **R** statistical computing environment and the **pomp** package for inference based on partially-observed Markov process models.
+
+Students with laptops should install **R** and **RStudio** on their computers before the first day of the course.
+[Instructions for doing so can be found here](http://kingaa.github.io/short-course/prep/preparation.html).
+
+The course does not assume familiarity with **R**, but students should work through the [**R** tutorial](http://kinglab.eeb.lsa.umich.edu/R_Tutorial) before the course commences.
+In particular, students should work through the the exercises in the tutorial.
+
+### Readings
+
+The following papers serve as background for some of the central issues:
+
+- S. N. Wood (2010) Statistical inference for noisy nonlinear ecological dynamic systems. *Nature*, **466**:&nbsp;1102--1104. [DOI:&nbsp;10.1038/nature09319](http://dx.doi.org/10.1038/nature09319).
+- A. A. King, E. L. Ionides, M. Pascual, and M. J. Bouma (2008) Inapparent infections and cholera dynamics. *Nature*, **454**:&nbsp;877--880. [DOI:&nbsp;10.1038/nature07084](http://dx.doi.org/10.1038/nature07084)
+- S. Shrestha, A. A. King, and P. Rohani (2011) Statistical Inference for Multi-Pathogen Systems. *PLoS&nbsp;Comput.&nbsp;Biol.*, **7**:&nbsp;e1002135. [DOI:&nbsp;10.1371/journal.pcbi.1002135](http://dx.doi.org/10.1371/journal.pcbi.1002135)
+
+A good reference for modeling in infectious disease epidemiology is:
+
+- Keeling, M. & Rohani, P. *Modeling infectious diseases in humans and animals*. Princeton University Press, 2008.
+
+The **pomp** package is described and illustrated in
+
+- A. A. King, D. Nguyen, and E. L. Ionides (2016) Statistical Inference for Partially Observed Markov Processes via the R Package pomp. *J.&nbsp;Stat.&nbsp;Soft.*, **69**:&nbsp;1--43. [DOI:&nbsp;10.18637/jss.v069.i12](http://dx.doi.org/10.18637/jss.v069.i12)
+
+### Tentative schedule
+
+```{r sched1,echo=FALSE}
+begin.date <- as.Date("2016-05-06")
+data.frame(date=begin.date+cumsum(c(rep(c(3,1,1,1,1),times=2)))) -> dates
+
+read.csv(colClasses=c(Date="Date"),comment.char="#",
+         text='
+Date,Topic
+2016-05-09,Ecological and epidemiological dynamics; introduction to **R**
+2016-05-10,Partially-observed Markov processes; introduction to **pomp**
+2016-05-11,Deterministic dynamics; trajectory matching
+2016-05-12,Stochastic simulation
+2016-05-13,Likelihood for POMP models
+2016-05-16,Iterated filtering
+2016-05-17,Case study: influenza in a closed population
+2016-05-18,Diagnostics
+2016-05-19,Case study: measles and extrademographic stochasticity
+2016-05-20,Case study: pertussis and natural immune boosting
+') %>%
+  mutate(Topic=mapvalues(as.character(Topic),NA,""),
+         Date=format(Date,"%b %e")) %>% 
+  kable()
+```
+
+----------------------