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kingaa · Sep 1, 2023 · aa272fd · aa272fd
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diff --git a/index.html b/index.html
diff --git a/index.md b/index.md
@@ -14,8 +14,8 @@ output:
 | &nbsp;                                                     | &nbsp;                                   |
 |:-----------------------------------------------------------|:-----------------------------------------|
 | Tutorial Introduction to **R**                             | [HTML](tutorial.html)                    |
-| Data munging with **plyr**, **reshape2**, and **magrittr** | [HTML](munging.html)&nbsp;[R](munging.R) |
 | Data visualization                                         | [HTML](viz.html)&nbsp;[R](viz.R)         |
+| Data munging with **plyr**, **reshape2**, and **magrittr** | [HTML](munging.html)&nbsp;[R](munging.R) |
 
 ----------------------
 
diff --git a/tutorial.Rmd b/tutorial.Rmd
@@ -866,7 +866,7 @@ Table: Logical operators.
 -----------------------------------
 
 For example, try
-```{r warning=TRUE}
+```{r warning=TRUE,error=TRUE}
 a <- c(1,2,3,4)
 b <- c(1,1,5,5)
 (a<b) | (a>3)

diff --git a/tutorial.bib b/tutorial.bib
@@ -144,7 +144,7 @@ @Manual{bbmle
 
 @Article{Bolker2009c,
   author           = {Bolker, Benjamin M. and Brooks, Mollie E. and Clark, Connie J. and Geange, Shane W. and Poulsen, John R. and Stevens, M. Henry H. and White, Jada-Simone S.},
-  journal          = {Trends in ecology \& evolution},
+  journal          = {Trends in Ecology and Evolution},
   title            = {Generalized linear mixed models: a practical guide for ecology and evolution},
   year             = {2009},
   number           = {3},
@@ -154,7 +154,7 @@ @Article{Bolker2009c
   doi              = {10.1016/j.tree.2008.10.008},
   file             = {:Bolker2009c.pdf:PDF},
   groups           = {[kingaa:]},
-  modificationdate = {2022-07-20T09:45:25},
+  modificationdate = {2023-09-01T09:24:46},
   owner            = {kingaa},
   publisher        = {Elsevier},
 }
@@ -568,6 +568,7 @@ @Article{Eisenberg2014
   title            = {Determining identifiable parameter combinations using subset profiling.},
   year             = {2014},
   month            = aug,
+  pmid             = {25173434},
   abstract         = {Identifiability is a necessary condition for successful parameter estimation of dynamic system models. A major component of identifiability analysis is determining the identifiable parameter combinations, the functional forms for the dependencies between unidentifiable parameters. Identifiable combinations can help in model reparameterization and also in determining which parameters may be experimentally measured to recover model identifiability. Several numerical approaches to determining identifiability of differential equation models have been developed, however the question of determining identifiable combinations remains incompletely addressed. In this paper, we present a new approach which uses parameter subset selection methods based on the Fisher Information Matrix, together with the profile likelihood, to effectively estimate identifiable combinations. We demonstrate this approach on several example models in pharmacokinetics, cellular biology, and physiology.},
   creationdate     = {2014-09-16T00:00:00},
   doi              = {10.1016/j.mbs.2014.08.008},
@@ -578,7 +579,6 @@ @Article{Eisenberg2014
   modificationdate = {2022-07-20T09:45:25},
   owner            = {kingaa},
   pii              = {S0025-5564(14)00163-1},
-  pmid             = {25173434},
   school           = {Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, United States. Electronic address: mhayash@umich.edu.},
 }
 
@@ -695,6 +695,7 @@ @Article{Fawcett2012
   month            = jul,
   number           = {29},
   pages            = {11735--11739},
+  pmid             = {22733777},
   volume           = {109},
   abstract         = {Most research in biology is empirical, yet empirical studies rely fundamentally on theoretical work for generating testable predictions and interpreting observations. Despite this interdependence, many empirical studies build largely on other empirical studies with little direct reference to relevant theory, suggesting a failure of communication that may hinder scientific progress. To investigate the extent of this problem, we analyzed how the use of mathematical equations affects the scientific impact of studies in ecology and evolution. The density of equations in an article has a significant negative impact on citation rates, with papers receiving 28\% fewer citations overall for each additional equation per page in the main text. Long, equation-dense papers tend to be more frequently cited by other theoretical papers, but this increase is outweighed by a sharp drop in citations from nontheoretical papers (35\% fewer citations for each additional equation per page in the main text). In contrast, equations presented in an accompanying appendix do not lessen a paper's impact. Our analysis suggests possible strategies for enhancing the presentation of mathematical models to facilitate progress in disciplines that rely on the tight integration of theoretical and empirical work.},
   creationdate     = {2014-09-23T00:00:00},
@@ -706,7 +707,6 @@ @Article{Fawcett2012
   modificationdate = {2022-07-20T09:45:23},
   owner            = {kingaa},
   pii              = {1205259109},
-  pmid             = {22733777},
   school           = {School of Biological Sciences, University of Bristol, Bristol BS8 1UG, United Kingdom. tim.fawcett@cantab.net},
 }
 
@@ -718,6 +718,7 @@ @Article{Filipe2001
   month            = jul,
   number           = {4},
   pages            = {603--624},
+  pmid             = {11497160},
   volume           = {63},
   abstract         = {Analytical methods for predicting and exploring the dynamics of stochastic, spatially interacting populations have proven to have useful application in epidemiology and ecology. An important development has been the increasing interest in spatially explicit models, which require more advanced analytical techniques than the usual mean-field or mass-action approaches. The general principle is the derivation of differential equations describing the evolution of the expected population size and other statistics. As a result of spatial interactions no closed set of equations is obtained. Nevertheless, approximate solutions are possible using closure relations for truncation. Here we review and report recent progress on closure approximations applicable to lattice models with nearest-neighbour interactions, including cluster approximations and elaborations on the pair (or pairwise) approximation. This study is made in the context of an SIS model for plant-disease epidemics introduced in Filipe and Gibson (1998, Studying and approximating spatio-temporal models for epidemic spread and control, Phil. Trans. R. Soc. Lond. B 353, 2153-2162) of which the contact process [Harris, T. E. (1974), Contact interactions on a lattice, Ann. Prob. 2, 969] is a special case. The various methods of approximation are derived and explained and their predictions are compared and tested against simulation. The merits and limitations of the various approximations are discussed. A hybrid pairwise approximation is shown to provide the best predictions of transient and long-term, stationary behaviour over the whole parameter range of the model.},
   creationdate     = {2008-03-12T00:00:00},
@@ -726,7 +727,6 @@ @Article{Filipe2001
   modificationdate = {2022-07-20T09:45:23},
   owner            = {kingaa},
   pii              = {S0092-8240(01)90234-4},
-  pmid             = {11497160},
   school           = {Statistics Scotland, Edinburgh, UK. jf263@cam.ac.uk},
 }
 
@@ -1776,16 +1776,16 @@ @Book{Pinheiro2000
 @Manual{R,
   title            = {R: A Language and Environment for Statistical Computing},
   address          = {Vienna, Austria},
-  author           = {R Core Team},
+  author           = {{R Core Team}},
   organization     = {R Foundation for Statistical Computing},
   year             = {2014},
-  modificationdate = {2022-07-20T09:45:24},
+  modificationdate = {2023-09-01T09:23:58},
   url              = {http://www.R-project.org/},
 }
 
 @Manual{Rintro,
   title            = {An Introduction to R},
-  author           = {R Core Team},
+  author           = {{R Core Team}},
   year             = {2014},
   comment          = {An Introduction to R
 Notes on R: A Programming Environment for Data Analysis and Graphics
@@ -1794,7 +1794,7 @@ @Manual{Rintro
 
 W. N. Venables, D. M. Smith
 and the R Core Team},
-  modificationdate = {2022-07-20T09:45:24},
+  modificationdate = {2023-09-01T09:24:05},
   url              = {http://cran.r-project.org/doc/manuals/r-release/R-intro.html},
 }
 
@@ -2124,6 +2124,7 @@ @Article{Stevens1946
   month        = jun,
   number       = {2684},
   pages        = {677--680},
+  pmid         = {17750512},
   volume       = {103},
   creationdate = {2014-08-11T00:00:00},
   doi          = {10.1126/science.103.2684.677},
@@ -2133,7 +2134,6 @@ @Article{Stevens1946
   medline-pst  = {ppublish},
   owner        = {kingaa},
   pii          = {103/2684/677},
-  pmid         = {17750512},
 }
 
 @Book{Stock2011,

diff --git a/tutorial.html b/tutorial.html