@@ -1,8 +1,8 @@

 Package: OncoSimulR

 Type: Package

 Title: Forward Genetic Simulation of Cancer Progression with Epistasis 

-Version: 2.5.2

-Date: 2016-12-10

+Version: 2.5.3

+Date: 2016-12-12

 Authors@R: c(person("Ramon", "Diaz-Uriarte", role = c("aut", "cre"),

 		     email = "rdiaz02@gmail.com"),

 	      person("Mark", "Taylor", role = "ctb", email = "ningkiling@gmail.com"))

@@ -30,7 +30,7 @@ URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cance

 BugReports: https://github.com/rdiaz02/OncoSimul/issues

 Depends: R (>= 3.3.0)

 Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel

-Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown

+Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander

 LinkingTo: Rcpp

 VignetteBuilder: knitr

@@ -1,3 +1,7 @@

+Changes in version 2.5.2 (2016-12-12):

+	- Vignette uses pander in tables.

+	- Typos fixed and other enhancements in vignette.

+	

 Changes in version 2.5.2 (2016-12-10):

         - Lots and lots of addition to vignette including benchmarks.

         - Diversity of sampled genotypes.

@@ -10,7 +10,9 @@ author: "

 		 <rdiaz02@gmail.com>, <http://ligarto.org/rdiaz>

 		 "

-date: "`r paste0(Sys.Date(),'. OncoSimulR version ', packageVersion('OncoSimulR'), '. Revision: ', system('git rev-parse --short HEAD', intern = TRUE))`"

+date: "`r paste0(Sys.Date(),'. OncoSimulR version ', packageVersion('OncoSimulR'), suppressWarnings(ifelse(length(try(system('git rev-parse --short HEAD', ignore.stderr = TRUE, intern = TRUE))), paste0('. Revision: ', system('git rev-parse --short HEAD', intern = TRUE)), '')))`"

+header-includes:

+    - \input{preamble.tex}

 output: 

   bookdown::html_document2:

     css: custom4.css

@@ -21,6 +23,7 @@ classoption: a4paper

 geometry: margin=3cm

 fontsize: 12pt

 bibliography: OncoSimulR.bib

+biblio-style: "apalike"

 link-citations: true

 vignette: >

   %\VignetteIndexEntry{OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression.}

@@ -129,6 +132,7 @@ MathJax.Hub.Config({

 knitr::opts_chunk$set(echo = TRUE, collapse = TRUE)

 options(width = 70)

 require(BiocStyle)

+require(pander)

 ```

 <!-- bookdown::pdf_document2 seems to produce the tex even if failures -->

@@ -144,8 +148,9 @@ require(BiocStyle)

 <!-- sed -i 's/\\Rfunction{\([^}]\+\)}/*`\1`*/g' p22.md -->

+\clearpage

-# Introduction {#intro}

+# Introduction {#introdd}

 OncoSimulR is an individual-based forward-time genetic simulator for

 biallelic markers (wildtype vs. mutated) in asexually reproducing

@@ -286,6 +291,7 @@ is a summary of some of the key features:

   from the simulations.

+

 The table below, modified from the table at the

 [Genetics Simulation Resources (GSR) page](https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/#detailed)

 provides a summary of the key features of OncoSimulR. (An

@@ -295,7 +301,7 @@ https://popmodels.cancercontrol.cancer.gov/gsr/search/ or from the

 [Genetics Simulation Resources table itself](https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/#detailed),

 by moving the mouse over each term).

-

+\clearpage

 |Attribute Category     | Attribute                                     |

 |-----------------------|-----------------------------------------------|

@@ -322,7 +328,7 @@ by moving the mouse over each term).

 |  Mutation Models|	Two-allele Mutation Model (wildtype, mutant), without back mutation|

 |  Events Allowed|	Varying Genetic Features: change of individual mutation rates (mutator/antimutator genes)|

 |  Spatial Structure| No Spatial Structure (perfectly mixed and no migration)|

-Table: (\#tab:osrfeatures) Key features of OncoSimulR. Modified from

+Table:(\#tab:osrfeatures) Key features of OncoSimulR. Modified from

 the original table from

 https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/#detailed

 .

@@ -485,17 +491,19 @@ g1 <- simOGraph(4, out = "rT")

 ## 2. Simulate two evolutionary trajectories

 s1 <- oncoSimulPop(10, allFitnessEffects(g1, drvNames = 1:4),

                    mc.cores = 2, ## adapt to your hardware

-                   seed = NULL) ## for reproducibility in this vignette

+                   seed = NULL) ## for reproducibility of vignette

 ## 3. Sample those data uniformly, and add noise

 d1 <- samplePop(s1, timeSample = "unif", propError = 0.1)

-## 4. You would now run the appropriate inferential method and compare

-## observed and true

+## 4. You would now run the appropriate inferential method and

+## compare observed and true

+

 require(Oncotree)

 fit1 <- oncotree.fit(d1)

-## Now, compare fitted and original. This is well beyond the scope of this

-## document (and OncoSimulR itself).

+

+## Now, compare fitted and original. This is well beyond the

+## scope of this document (and OncoSimulR itself).

 ```

@@ -532,8 +540,8 @@ RNGkind("L'Ecuyer-CMRG")

 ```{r exochs}

 ## Specify fitness effects. 

-## Numeric values arbitrary, but set the intermediate genotype 

-## en route to ui as mildly deleterious so there is a valley. 

+## Numeric values arbitrary, but set the intermediate genotype en

+## route to ui as mildly deleterious so there is a valley.

 ## As in Ochs and Desai, the ui and uv genotypes

 ## can never appear. 

@@ -545,7 +553,9 @@ od <- allFitnessEffects(

                   "u:v" = uv, "i" = i,

                   "v:-i" = -Inf, "v:i" = vi))

-## For the sake of extending this example, also turn i into a mutator gene

+## For the sake of extending this example, also turn i into a

+## mutator gene

+

 odm <- allMutatorEffects(noIntGenes = c("i" = 50))

 ## How do mutation and fitness look like for each genotype?

@@ -600,19 +610,23 @@ RNGkind("L'Ecuyer-CMRG")

 ## reference genotype or evolutionary model, or stopping criterion, 

 ## sampling procedure, or ...

-## 1. Generate a random fitness landscape, from the Rough

-##    Mount Fuji model, with g genes, and c ("slope" constant) and

-##    reference chosen randomly. Ask for a minimal number of accessible

-##    genotypes

+## 1. Generate a random fitness landscape, from the Rough Mount

+##    Fuji model, with g genes, and c ("slope" constant) and

+##    reference chosen randomly. Ask for a minimal number of

+##    accessible genotypes

+

 g <- 6

 c <- runif(1, 1/5, 5)

 rl <- rfitness(g, c = c, min_accessible_genotypes = g)

-## Plot it if you want; commented here as it takes long for a vignette

+## Plot it if you want; commented here as it takes long for a

+## vignette

+

 ## plot(rl)

 ## Obtain landscape measures from Magellan. Export to Magellan

 to_Magellan(rl, file = "rl1.txt")

+

 ## (Getting the statistics requires you to install Magellan,

 ## and that requires either calling the web app 

 ## (http://wwwabi.snv.jussieu.fr/public/Magellan/) or

@@ -1165,7 +1179,7 @@ version 2. Please note that **the functionality of version 1 will soon be remove

-

+\clearpage

 # Running time and space consumption of OncoSimulR {#timings}

@@ -1230,6 +1244,91 @@ some of the benchmarks is available from the

 repository at https://github.com/rdiaz02/OncoSimul).

+```{r colnames_benchmarks, echo = FALSE, eval = TRUE}

+

+data(benchmark_1)

+data(benchmark_1_0.05)

+data(benchmark_2)

+data(benchmark_3)

+

+colnames(benchmark_1)[

+    match(c(

+	"time_per_simul",

+    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

+	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean",

+	"TotalPopSize.Max.", "keepEvery",  "Attempts.Median",

+	"Attempts.Mean", "Attempts.Max.",

+	"PDBaseline", "n2", "onlyCancer"),

+	 colnames(benchmark_1)

+	)] <- c("Elapsed Time, average per simulation (s)",

+	              "Object Size, average per simulation (MB)",

+				  "Number of Clones, median",

+				  "Number of Iterations, median",

+				  "Final Time, median",

+				  "Total Population Size, median",

+				   "Total Population Size, mean",

+				  "Total Population Size, max.",

+				  "keepEvery",

+				  "Attempts until Cancer, median",

+				  "Attempts until Cancer, mean",

+				  "Attempts until Cancer, max.",

+				  "PDBaseline", "n2", "onlyCancer"

+				  )

+				  

+	

+colnames(benchmark_1_0.05)[

+    match(c("time_per_simul",

+    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

+	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 

+	"TotalPopSize.Max.",

+	"keepEvery",

+	"PDBaseline", "n2", "onlyCancer", "Attempts.Median"),

+	colnames(benchmark_1_0.05))] <- c("Elapsed Time, average per simulation (s)",

+	              "Object Size, average per simulation (MB)",

+				  "Number of Clones, median",

+				  "Number of Iterations, median",

+				  "Final Time, median",

+				  "Total Population Size, median",

+				  "Total Population Size, mean",

+				  "Total Population Size, max.",

+				  "keepEvery",

+				  "PDBaseline", "n2", "onlyCancer",

+				  "Attempts until Cancer, median"

+				  )

+

+

+colnames(benchmark_2)[match(c("Model", "fitness", "time_per_simul",

+    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

+	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 

+	"TotalPopSize.Max."), colnames(benchmark_2))] <-  c("Model",

+				  "Fitness",

+	"Elapsed Time, average per simulation (s)",

+	              "Object Size, average per simulation (MB)",

+				  "Number of Clones, median",

+				  "Number of Iterations, median",

+				  "Final Time, median",

+				  "Total Population Size, median",

+				  "Total Population Size, mean",

+				  "Total Population Size, max."

+				  )	

+				  

+colnames(benchmark_3)[match(c("Model", "fitness", "time_per_simul",

+    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

+	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 

+	"TotalPopSize.Max."), colnames(benchmark_3))] <-  c("Model",

+				  "Fitness",

+	"Elapsed Time, average per simulation (s)",

+	              "Object Size, average per simulation (MB)",

+				  "Number of Clones, median",

+				  "Number of Iterations, median",

+				  "Final Time, median",

+				  "Total Population Size, median",

+				  "Total Population Size, mean",

+				  "Total Population Size, max."

+				  )					  

+```

+

+

 ## Exp and McFL with "detectionProb" and pancreas example {#bench1}

 To get familiar with some of they factors that affect time and size,

@@ -1246,9 +1345,6 @@ period are pruned and only the existing clones at the end of the

 simulation are returned (see details in \@ref(prune)).

-```{r loadbench1, echo = FALSE, eval = TRUE} 

-data(benchmark_1) 

-``` 

 Will run `r unique(benchmark_1$Numindiv)` simulations.  The results

 I show are for a laptop with an 8-core Intel Xeon E3-1505M CPU,

@@ -1340,36 +1436,50 @@ summary(unlist(lapply(exp1, "[[", "TotalPopSize")))

 The above runs yield the following:

+\blandscape

+

+Table: (\#tab:bench1) Benchmarks of Exp and McFL models using the default `detectionProb` with two settings of `keepEvery`. 

 ```{r bench1, eval=TRUE, echo = FALSE}

-data(benchmark_1)

-knitr::kable(benchmark_1[1:4, c("time_per_simul",

-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

-	"FinalTime.Median", "TotalPopSize.Median",

-	"TotalPopSize.Max.", "keepEvery")], 

-    booktabs = TRUE,

-	col.names = c("Elapsed Time, average per simulation (s)",

-	              "Object Size, average per simulation (MB)",

-				  "Number of Clones, median",

-				  "Number of Iterations, median",

-				  "Final Time, median",

-				  "Total Population Size, median",

-				  "Total Population Size, max.",

-				  "keepEvery"

-				  ),

-    caption = "Benchmarks of Exp and McFL models using the default

-	`detectionProb` with two settings of `keepEvery`.", 

-	align = "c")

+

+panderOptions("table.split.table", 99999999)

+panderOptions("table.split.cells", 900)  ## For HTML

+## panderOptions("table.split.cells", 8) ## For PDF

+

+set.alignment('right')

+panderOptions('round', 3)

+				          

+pander(benchmark_1[1:4, c("Elapsed Time, average per simulation (s)", 

+ 	              "Object Size, average per simulation (MB)",

+ 				  "Number of Clones, median",

+ 				  "Number of Iterations, median",

+ 				  "Final Time, median",

+ 				  "Total Population Size, median",

+ 				  "Total Population Size, max.",

+ 				  "keepEvery")],

+				  ## caption = "\\label{tab:bench1}Benchmarks of Exp and McFL  models using the default `detectionProb` with two settings of `keepEvery`."

+				  )

 ```

-The above table shows that a naive comparison (looking simply at

-execution time) might conclude that the McFL model is much, much

-slower than the Exp model. But that is not the complete story: using

-the `detectionProb` stopping mechanism (see \@ref(detectprob)) will

-lead to stopping the simulations very quickly in the exponential

-model because as soon as a clone with fitness $>1$ appears it starts

-growing exponentially. In fact, we can see that the number of

-iterations and the final time are much smaller in the Exp than in

-the McFL model. 

+\elandscape

+

+\clearpage

+

+

+

+The above table shows that a naive comparison (looking simply at execution

+time) might conclude that the McFL model is much, much slower than the Exp

+model. But that is not the complete story: using the `detectionProb`

+stopping mechanism (see \@ref(detectprob)) will lead to stopping the

+simulations very quickly in the exponential model because as soon as a

+clone with fitness $>1$ appears it starts growing exponentially. In fact,

+we can see that the number of iterations and the final time are much

+smaller in the Exp than in the McFL model.  We will elaborate on this

+point below (section \@ref(common1)), when we discuss the setting for

+`checkSizePEvery` (here left at its default value of 20): checking the

+exiting condition more often (smaller `checkSizePEvery`) would probably be

+justified here (notice also the very large final times) and would lead to

+a sharp decrease in number of iterations and, thus, running time.

+

 This table also shows that the `keepEvery = NA` setting, which was in effect

@@ -1451,30 +1561,38 @@ t_exp6 <- system.time(

 ```

+\blandscape

+Table: (\#tab:bench1b) Benchmarks of Exp models modifying the default `detectionProb` with two settings of `keepEvery`.

 ```{r bench1b, eval=TRUE, echo = FALSE}

-data(benchmark_1)

-knitr::kable(benchmark_1[5:8, c("time_per_simul",

-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

-	"FinalTime.Median", "TotalPopSize.Median",

-	"TotalPopSize.Max.", "keepEvery",

-	"PDBaseline", "n2")], 

-    booktabs = TRUE,

-	col.names = c("Elapsed Time, average per simulation (s)",

-	              "Object Size, average per simulation (MB)",

-				  "Number of Clones, median",

-				  "Number of Iterations, median",

-				  "Final Time, median",

-				  "Total Population Size, median",

-				  "Total Population Size, max.",				  

-				  "keepEvery",

-				  "PDBaseline", "n2"

-				  ),	

-    caption = "Benchmarks of Exp models modifying the default

-	`detectionProb` with two settings of `keepEvery`.", 

-	align = "c")

+panderOptions("table.split.table", 99999999)

+panderOptions("table.split.cells", 900)  ## For HTML

+## panderOptions("table.split.cells", 8) ## For PDF

+set.alignment('right')

+panderOptions('round', 2)

+panderOptions('big.mark', ',')

+panderOptions('digits', 2)

+

+pander(benchmark_1[5:8, c("Elapsed Time, average per simulation (s)",

+ 	              "Object Size, average per simulation (MB)",

+ 				  "Number of Clones, median",

+ 				  "Number of Iterations, median",

+ 				  "Final Time, median",

+ 				  "Total Population Size, median",

+ 				  "Total Population Size, max.",

+ 				  "keepEvery",

+				  "PDBaseline",

+				  "n2")], 

+## 				  round = c(rep(2, 3), rep(0, 7)),

+## 				  digits = c(rep(2, 3), rep(1, 7)),

+	  ## caption = "\\label{tab:bench1b}Benchmarks of Exp and McFL models modifying the default `detectionProb` with two settings of `keepEvery`."

+    )

+

 ```

+\elandscape

+

+\clearpage

 As above,  `keepEvery = NA` (in `exp4` and `exp6`) leads to much

 smaller object sizes and slightly smaller numbers of clones and

@@ -1509,62 +1627,95 @@ initial population sizes) because of the dependency of death rate on total

 population size (see section \@ref(mcfl)).

-The number of attempts until cancer was reached in the above models is

-shown in the following table (the values can be obtained from any of the

-above runs doing, for instance, `median(unlist(lapply(exp1, function(x)

-x$other$attemptsUsed)))` ).:

-

-

+The number of attempts until cancer was reached in the above

+models is shown in the Table \@ref(tab:bench1c) (the values can be obtained from

+any of the above runs doing, for instance, `median(unlist(lapply(exp1,

+function(x) x$other$attemptsUsed)))` ):

+Table: (\#tab:bench1c) Number of attempts until cancer.

 ```{r bench1c, eval=TRUE, echo = FALSE}

-data(benchmark_1)

-knitr::kable(benchmark_1[1:8, c("Attempts.Median"), drop = FALSE], 

-    booktabs = TRUE,

-	row.names = TRUE,

-	col.names = "Attempts until cancer",

-    caption = "Median Number of attempts until cancer.", 

-	align = "r")

+panderOptions("table.split.table", 99999999)

+panderOptions("table.split.cells", 900)  ## For HTML

+## panderOptions("table.split.cells", 8) ## For PDF

+set.alignment('right')

+panderOptions('round', 2)

+panderOptions('big.mark', ',')

+panderOptions('digits', 2)

+

+pander(benchmark_1[1:8, c(

+"Attempts until Cancer, median", 

+"Attempts until Cancer, mean", 

+"Attempts until Cancer, max.", 

+				  "PDBaseline",

+				  "n2")], 

+## 				  round = c(rep(2, 3), rep(0, 7)),

+## 				  digits = c(rep(2, 3), rep(1, 7)),

+	  ## caption = "\\label{tab:bench1c}Median number of attempts until cancer."

+    )

+## ## data(benchmark_1)

+## knitr::kable(benchmark_1[1:8, c("Attempts.Median",

+##                                 "PDBaseline", "n2"), drop = FALSE], 

+##     booktabs = TRUE,

+## 	row.names = TRUE,

+## 	col.names = c("Attempts until cancer", "PDBaseline", "n2"),

+##     caption = "Median number of attempts until cancer.", 

+## 	align = "r")

 ```

-

-

+The McFL models finish in a single attempt. The exponential model

+simulations where we can exit with small population sizes (`exp1`, `exp2`)

+need many fewer attempts to reach cancer than those where large population

+sizes are required (`exp3` to `exp6`). There is no relevant different

+among those four, which is what we would expect: a population that has

+already reached a size of 50,000 cells from an initial population size of

+500 is obviously a growing population where there is at least one mutant

+with positive fitness; thus, it unlikely to go extinct and therefore

+having to grow up to at least 500,000 will not significantly increase the

+risk of extinction.

 We will now rerun all of the above models with argument `onlyCancer =

-FALSE`.  The results are shown below (note that the differences between

-this table and table \@ref(tab:bench1) for the McFL models are due only to

-sampling variation).

+FALSE`.  The results are shown in Table \@ref(tab:timing3) (note that the

+differences between this table and table \@ref(tab:bench1) for the McFL

+models are due only to sampling variation).

+

+\bslandscape

-Table: (\#tab:timing3) Benchmarks of models in Table \@ref(tab:bench1) and

-\@ref(tab:bench1b) when run with `onlyCancer = FALSE`

+Table: (\#tab:timing3) Benchmarks of models in Table \@ref(tab:bench1) and \@ref(tab:bench1b) when run with `onlyCancer = FALSE`

 ```{r bench1d, eval=TRUE, echo = FALSE}

-data(benchmark_1)

-knitr::kable(benchmark_1[9:16, c("time_per_simul",

-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

-	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 

-	"TotalPopSize.Max.",

-	"keepEvery",

-	"PDBaseline", "n2")], 

-    booktabs = TRUE,

-	col.names = c("Elapsed Time, average per simulation (s)",

-	              "Object Size, average per simulation (MB)",

-				  "Number of Clones, median",

-				  "Number of Iterations, median",

-				  "Final Time, median",

-				  "Total Population Size, median",

+panderOptions("table.split.table", 99999999)

+panderOptions("table.split.cells", 900)  ## For HTML

+## panderOptions("table.split.cells", 8) ## For PDF

+panderOptions("table.split.cells", 15) ## does not fit otherwise

+set.alignment('right')

+panderOptions('round', 3)

+

+pander(benchmark_1[9:16, 

+    c("Elapsed Time, average per simulation (s)",

+ 	              "Object Size, average per simulation (MB)",

+ 				  "Number of Clones, median",

+ 				  "Number of Iterations, median",

+ 				  "Final Time, median",

+ 				  "Total Population Size, median",

 				  "Total Population Size, mean",

-				  "Total Population Size, max.",

-				  "keepEvery",

-				  "PDBaseline", "n2"

-				  ),

-##    caption = "Benchmarks of models in Table \@ref(tab:bench1) and

-##   \@ref(tab:bench1b) when run with `onlyCancer = FALSE`", 

-	align = "c")

+ 				  "Total Population Size, max.",

+ 				  "keepEvery",

+				  "PDBaseline",

+				  "n2")],

+## caption = "\\label{tab:timing3} Benchmarks of models in Table \\@ref(tab:bench1) and \\@ref(tab:bench1b) when run with `onlyCancer = FALSE`."

+				  )	

+	

 ```

+\eslandscape

+

+\clearpage

+

+

+

 Now most simulations under the exponential model end up in extinction, as

 seen by the median population size of 0 (but not all, as the mean and

@@ -1576,47 +1727,101 @@ on the question being asked (see, for example, section \@ref(exbauer) for

 a question where we will naturally want to use `onlyCancer = FALSE`).

+To make it easier to compare results with those of the next section, Table

+\@ref(tab:allr1bck) shows all the runs so far.

+

+

+\bslandscape

+

+Table: (\#tab:allr1bck) Benchmarks of all models in Tables \@ref(tab:bench1), \@ref(tab:bench1b) and \@ref(tab:timing3).  

+```{r bench1dx0, eval=TRUE, echo = FALSE}

+panderOptions("table.split.table", 99999999)

+## panderOptions("table.split.cells", 900)  ## For HTML

+panderOptions("table.split.cells", 19)

+

+set.alignment('right') 

+panderOptions('round', 3)

+	

+pander(benchmark_1[ , c("Elapsed Time, average per simulation (s)",

+ 	              "Object Size, average per simulation (MB)", 

+				  "Number of Clones, median", 

+				  "Number of Iterations, median", 

+				  "Final Time, median", "Total Population Size, median", 

+				  "Total Population Size, mean", "Total Population Size, max.",

+ 	              "keepEvery", "PDBaseline", "n2", "onlyCancer")], 

+				  ## caption = "\\label{tab:allr1bck}Benchmarks of all models in Tables \\@ref(tab:bench1), \\@ref(tab:bench1b)  and \\@ref(tab:timing3)."  

+				  )  

+```

+

+\eslandscape

+

+\clearpage

+

+

+

 ### Changing fitness: $s=0.1$ and $s=0.05$ {#bench1xf}

 In the above fitness specification the fitness effect of each gene (when

 its restrictions are satisfied) is $s = 0.1$ (see section \@ref(numfit)

 for details). Here we rerun all the above benchmarks using $s= 0.05$ and

-results are shown below in Table \@(tab:timing3xf); the results from these

+results are shown below in Table \@ref(tab:timing3xf); the results from these

 benchmarks are available as `data(benchmark_1_0.05)`:

+\bslandscape

-Table: (\#tab:timing3xf) Benchmarks of all models in Tables \@ref(tab:bench1),

-\@ref(tab:bench1b) and \@ref(tab:timing3) using $s=0.05$ (instead of

-$s=0.1$).

+Table: (\#tab:timing3xf) Benchmarks of all models in Table \@ref(tab:allr1bck) using $s=0.05$ (instead of $s=0.1$).

 ```{r bench1dx, eval=TRUE, echo = FALSE}

-data(benchmark_1_0.05)

-knitr::kable(benchmark_1_0.05[, c("time_per_simul",

-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

-	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 

-	"TotalPopSize.Max.",

-	"keepEvery",

-	"PDBaseline", "n2", "onlyCancer")], 

-    booktabs = TRUE,

-	col.names = c("Elapsed Time, average per simulation (s)",

-	              "Object Size, average per simulation (MB)",

-				  "Number of Clones, median",

-				  "Number of Iterations, median",

-				  "Final Time, median",

-				  "Total Population Size, median",

-				  "Total Population Size, mean",

-				  "Total Population Size, max.",				  

-				  "keepEvery",

-				  "PDBaseline", "n2", "onlyCancer"

-				  ),

-##    caption = "Benchmarks of models in Table \@ref(tab:bench1) and

-##   \@ref(tab:bench1b) when run with `onlyCancer = FALSE`", 

-	align = "c")

+## data(benchmark_1_0.05)

+## knitr::kable(benchmark_1_0.05[, c("time_per_simul",

+##     "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

+## 	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 

+## 	"TotalPopSize.Max.",

+## 	"keepEvery",

+## 	"PDBaseline", "n2", "onlyCancer")], 

+##     booktabs = TRUE,

+## 	col.names = c("Elapsed Time, average per simulation (s)",

+## 	              "Object Size, average per simulation (MB)",

+## 				  "Number of Clones, median",

+## 				  "Number of Iterations, median",

+## 				  "Final Time, median",

+## 				  "Total Population Size, median",

+## 				  "Total Population Size, mean",

+## 				  "Total Population Size, max.",				  

+## 				  "keepEvery",

+## 				  "PDBaseline", "n2", "onlyCancer"

+## 				  ),

+## ##    caption = "Benchmarks of models in Table \@ref(tab:bench1) and

+## ##   \@ref(tab:bench1b) when run with `onlyCancer = FALSE`", 

+## 	align = "c")

+	

+panderOptions("table.split.table", 99999999)

+## panderOptions("table.split.cells", 900)  ## For HTML

+panderOptions("table.split.cells", 19)

+

+set.alignment('right') 

+panderOptions('round', 3)

+	

+pander(benchmark_1_0.05[ , c("Elapsed Time, average per simulation (s)",

+ 	              "Object Size, average per simulation (MB)", 

+				  "Number of Clones, median", 

+				  "Number of Iterations, median", 

+				  "Final Time, median", 

+				  "Total Population Size, median", 

+				  "Total Population Size, mean", "Total Population Size, max.",

+ 	              "keepEvery", "PDBaseline", "n2", "onlyCancer")], 

+ 	              ## caption = "\\label{tab:timing3xf}Benchmarks of all models in Table \\@ref(tab:allr1bck) using $s=0.05$ (instead of $s=0.1$)."  

+)  

+				  

 ```

+\eslandscape

+

+\clearpage

+

 As expected, having a smaller $s$ leads to slower processes in most cases,

 since it takes longer to reach the exiting conditions sooner. Particularly

 noticeable are the runs for the McFL models (notice the increases in

-population size and number of iterations). 

+population size and number of iterations ---see also below). 

 That is not the case, however, for `exp5` and `exp6` (and `exp5_noc` and

@@ -1651,6 +1856,15 @@ additional iterations. They exit sooner in terms of time periods,

 but they do much more work before arriving there.

+The setting of `checkSizePEvery` is also having a huge effect on the McFL

+model simulations (the number of iterations is $>10^6$). Even more than in

+the previous section, checking the exiting condition more often (smaller

+`checkSizePEvery`) would probably be justified here (notice also the very

+large final times) and would lead to a sharp decrease in number of

+iterations and, thus, running time.

+

+

+

 The moral here is that in complex simulations like this, the effects

 of some parameters ($s$ in this case) might look counter-intuitive

 at first. Thus the need to "experiment before launching a large

@@ -1680,34 +1894,41 @@ pancr <- allFitnessEffects(

 	             "MLL3", "TGFBR2", "PXDN"))

-## Random fitness landscape with 6 genes 

+## Random fitness landscape with 6 genes

+## At least 50 accessible genotypes

 rfl6 <- rfitness(6, min_accessible_genotypes = 50)

-attributes(rfl6)$accessible_genotypes ## How many actually accessible

+attributes(rfl6)$accessible_genotypes ## How many accessible

 rf6 <- allFitnessEffects(genotFitness = rfl6)

 ## Random fitness landscape with 12 genes

+## At least 200 accessible genotypes

 rfl12 <- rfitness(12, min_accessible_genotypes = 200)

-attributes(rfl12)$accessible_genotypes ## How many actually accessible

+attributes(rfl12)$accessible_genotypes ## How many accessible

 rf12 <- allFitnessEffects(genotFitness = rfl12)

 ## Independent genes; positive fitness from exponential distribution

-## mean around 0.1, and negative from exponential with mean around 0.02.

-## Half positive, half negative

+## mean around 0.1, and negative from exponential with mean around 

+## 0.02. Half of genes positive fitness effects, half negative.

+

 ng <- 200

-re_200 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), -rexp(ng/2, 50)))

+re_200 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), 

+                                           -rexp(ng/2, 50)))

 ng <- 500

-re_500 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), -rexp(ng/2, 50)))

+re_500 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), 

+                                           -rexp(ng/2, 50)))

 ng <- 2000

-re_2000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), -rexp(ng/2, 50)))

+re_2000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), 

+                                            -rexp(ng/2, 50)))

 ng <- 4000

-re_4000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), -rexp(ng/2, 50)))

+re_4000 <- allFitnessEffects(noIntGenes = c(rexp(ng/2, 10), 

+                                            -rexp(ng/2, 50)))

 ```

@@ -1769,12 +1990,12 @@ oncoSimulPop(Nindiv,

 For the exponential model we will stop simulations when the populations

-gets $>10^6$ cells (simulations start from 500 cells). For the McFarland

+have $>10^6$ cells (simulations start from 500 cells). For the McFarland

 model we will use the `detectionProb` mechanism (see section

 \@ref(detectprob) for details); we could have used as stopping mechanism

 `detectionSize = 2 * initSize` (which would be basically equivalent to

 reaching cancer, as argued in [@McFarland2013]) but we want to provide

-further runs under the `detectionProb` mechanism. We will start from 1000

+further examples under the `detectionProb` mechanism. We will start from 1000

 cells, not 500 (starting from 1000 we almost always reach cancer in a

 single execution).

@@ -1818,42 +2039,69 @@ met.

-```{r loadbench2usual, echo = FALSE, eval = TRUE} 

-data(benchmark_2) 

-``` 

+<!-- ```{r loadbench2usual, echo = FALSE, eval = TRUE}  -->

+<!-- data(benchmark_2)  -->

+<!-- ```  -->

 The results of the benchmarks, using `r unique(benchmark_2$Numindiv)`

 individual simulations, are shown in Table \@ref(tab:timingusual).

+\blandscape

-Table: (\#tab:timingusual) Benchmarks under some common use cases,

-set 1.

+Table: (\#tab:timingusual) Benchmarks under some common use cases, set 1.

 ```{r benchustable, eval=TRUE, echo = FALSE}

-data(benchmark_2)

-

-knitr::kable(benchmark_2[, c("Model", "fitness", "time_per_simul",

-    "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

-	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 

-	"TotalPopSize.Max.")], 

-    booktabs = TRUE,

-	col.names = c("Model",

-				  "Fitness",

-	"Elapsed Time, average per simulation (s)",

-	              "Object Size, average per simulation (MB)",

-				  "Number of Clones, median",

-				  "Number of Iterations, median",

-				  "Final Time, median",

-				  "Total Population Size, median",

-				  "Total Population Size, mean",

-				  "Total Population Size, max."

-				  ),

-	align = "c")

+## data(benchmark_2)

+

+## knitr::kable(benchmark_2[, c("Model", "fitness", "time_per_simul",

+##     "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

+## 	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 

+## 	"TotalPopSize.Max.")], 

+##     booktabs = TRUE,

+## 	col.names = c("Model",

+## 				  "Fitness",

+## 	"Elapsed Time, average per simulation (s)",

+## 	              "Object Size, average per simulation (MB)",

+## 				  "Number of Clones, median",

+## 				  "Number of Iterations, median",

+## 				  "Final Time, median",

+## 				  "Total Population Size, median",

+## 				  "Total Population Size, mean",

+## 				  "Total Population Size, max."

+## 				  ),

+## 	align = "c")

+

+panderOptions("table.split.table", 99999999)

+panderOptions("table.split.cells", 900)  ## For HTML

+## panderOptions("table.split.cells", 8) ## For PDF

+

+## set.alignment('right', row.names = 'center')

+panderOptions('table.alignment.default', 'right')

+

+panderOptions('round', 3)

+

+pander(benchmark_2[ , c(

+    "Model", "Fitness",

+    "Elapsed Time, average per simulation (s)",

+ 	              "Object Size, average per simulation (MB)",

+ 				  "Number of Clones, median",

+ 				  "Number of Iterations, median",

+ 				  "Final Time, median",

+ 				  "Total Population Size, median",

+ 				  "Total Population Size, mean",				  

+ 				  "Total Population Size, max.")], 

+				  justify = c('left', 'left', rep('right', 8)),

+				  ## caption = "\\label{tab:timingusual}Benchmarks under some common use cases, set 1." 

+				  )	

+	

 ```

+\elandscape

+

+\clearpage

 In most cases, simulations run reasonably fast (under 0.1 seconds per

-individual simulation) and the return objects are small. In will only

+individual simulation) and the return objects are small. I will only

 focus on a few cases.

 The McFL model with random fitness landscape `rf12` and with `pancr` does

@@ -1927,29 +2175,56 @@ output in the 'miscell-files/vignette_bench_Rout' directory of the

 main OncoSimul repository at https://github.com/rdiaz02/OncoSimul.

 The data are available as `data(benchmark_3)`.

-Table: (\#tab:timingusual2) Benchmarks under some common use cases,

-set 2.	

+\blandscape

+

+Table: (\#tab:timingusual2) Benchmarks under some common use cases, set 2.	

 ```{r benchustable2, eval=TRUE, echo = FALSE}

-data(benchmark_3)

+## data(benchmark_3)

+

+## knitr::kable(benchmark_3[, c("Model", "fitness", "time_per_simul",

+##     "size_mb_per_simul", "NumClones.Median", "NumIter.Median",

+## 	"FinalTime.Median", "TotalPopSize.Median", "TotalPopSize.Mean", 

+## 	"TotalPopSize.Max.")], 

+##     booktabs = TRUE,

+## 	col.names = c("Model",

+## 				  "Fitness", "Elapsed Time, average per simulation (s)",

+## 	              "Object Size, average per simulation (MB)",

+## 				  "Number of Clones, median",

+## 				  "Number of Iterations, median",

+## 				  "Final Time, median",

+## 				  "Total Population Size, median",

+## 				  "Total Population Size, mean",

+## 				  "Total Population Size, max."

+## 				  ),

+## 	align = "c")

+

+panderOptions("table.split.table", 99999999)