update vignettes for CRAN submission

.Rbuildignore

@@ -12,3 +12,5 @@
 ^data-raw$
 ^docs$
 ^_pkgdown\.yml$
+^doc$
+^Meta$

.gitignore

@@ -2,3 +2,5 @@
 .Rhistory
 .RData
 README.html
+doc
+Meta

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: heemod
 Title: Markov Models for Health Economic Evaluations
-Version: 0.14.2
+Version: 0.14.3
 Authors@R: c(
     person("Kevin", "Zarca", email = "[email protected]", role = c("aut", "cre")),
     person("Antoine", "Filipovic-Pierucci", role = "aut"),
     person("Matthew", "Wiener", role = "ctb"),  
-    person("Zdenek", "Kabat", role = "ctb"),
+    person("Zden    ek", "Kabat", role = "ctb"),
     person("Vojtech", "Filipec", role = "ctb"),
 	  person("Jordan", "Amdahl", role=c("ctb")),
 	  person("Yonatan", "Carranza Alarcon", role=c("ctb")),
@@ -18,18 +18,18 @@ Description: An implementation of the modelling and reporting features described
     32, 690-700 (2012).): deterministic and probabilistic sensitivity analysis, 
     heterogeneity analysis, time dependency on state-time and model-time 
     (semi-Markov and non-homogeneous Markov models), etc.
-Depends: R (>= 3.3.0)
+Depends: R (>= 3.6.0)
 Imports:
-    dplyr (>= 0.7.2),
-    ggplot2 (>= 2.2.0),
-    lazyeval (>= 0.2.0),
-    memoise (>= 1.1.0),
-    mvnfast (>= 0.2.2),
-    plyr (>= 1.8.0),
-    pryr (>= 0.1.2),
-    tibble (>= 1.3.0),
-    rlang (>= 0.3), 
-    purrr (>= 0.3)
+    dplyr (>= 1.0.4),
+    ggplot2 (>= 3.3.3),
+    lazyeval (>= 0.2.2),
+    memoise (>= 2.0.0),
+    mvnfast (>= 0.2.5),
+    plyr (>= 1.8.6),
+    pryr (>= 0.1.4),
+    tibble (>= 3.0.6),
+    rlang (>= 0.4.1), 
+    purrr (>= 0.3.4)
 License: GPL (>= 3)
 LazyData: true
 VignetteBuilder: knitr

vignettes/a_introduction.Rmd

@@ -5,7 +5,7 @@ output:
   rmarkdown::html_vignette:
     toc: true
 vignette: >
-  %\VignetteIndexEntry{An Introduction to heemod}
+  %\VignetteIndexEntry{An Introduction to `heemod`}
   %\VignetteEngine{knitr::rmarkdown}
   \usepackage[utf8]{inputenc}
 ---
@@ -14,6 +14,17 @@ vignette: >
 library(heemod)
 ```
 
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  purl = NOT_CRAN,
+  eval = NOT_CRAN,
+  screenshot.force = FALSE
+)
+```
+
 This document is a presentation of the basic steps to define and run a model in `heemod`. Note that decision trees are actually a subset even of *Markov model*, and thus can be specified easily with this package.
 
 When building a Markov model the following steps must be performed:

vignettes/b_time_dependency.Rmd

@@ -14,6 +14,17 @@ vignette: >
 library(heemod)
 ```
 
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  purl = NOT_CRAN,
+  eval = NOT_CRAN,
+  screenshot.force = FALSE
+)
+```
+
 In more complex Markov models state values or transition probabilities can vary with time. These models are called *non-homogeneous* or *time-inhomogeneous* Markov models. A further distinction can be made depending on whether state values or transition probabilities:
 
   1. depend on how long __the entire model__ has been running (model-time dependency);

vignettes/c_homogeneous.Rmd

@@ -15,6 +15,13 @@ library(heemod)
 library(ggplot2)
 ```
 
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  screenshot.force = FALSE
+)
+```
+
 The most simple Markov models in health economic evaluation are models were transition probabilities between states do not change with time. Those are called *homogeneous* or *time-homogeneous* Markov models.
 
 ## Model description

vignettes/d_non_homogeneous.Rmd

@@ -15,6 +15,13 @@ library(heemod)
 library(ggplot2)
 ```
 
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  screenshot.force = FALSE
+)
+```
+
 ## Model description
 
 This example is an implementation of the assessment of a new total hip replacement (THR) technology described in chapter 3.5 of [Decision Modelling for Health Economic Evaluation](https://global.oup.com/academic/product/decision-modelling-for-health-economic-evaluation-9780198526629). A more detailed report is available [at this location](https://www.york.ac.uk/media/che/documents/papers/technicalpapers/CHE%20Technical%20Paper%2028.pdf). This reports goes a bit further in the analysis. For the sake of simplicity we will not reproduce exactly the analysis from the book. See vignette `vignette("i-reproduction", "heemod")` for an exact reproduction.

vignettes/e_probabilistic.Rmd

@@ -13,6 +13,16 @@ vignette: >
 ```{r, echo=FALSE, include=FALSE}
 library(heemod)
 ```
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  purl = NOT_CRAN,
+  eval = NOT_CRAN,
+  screenshot.force = FALSE
+)
+```
 
 This vignette shows how to transform the deterministic Markov model presented in `vignette("c-homogeneous", "heemod")` in a probabilistic model.
 

vignettes/f_sensitivity.Rmd

@@ -10,6 +10,18 @@ vignette: >
   \usepackage[utf8]{inputenc}
 ---
 
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  purl = NOT_CRAN,
+  eval = NOT_CRAN,
+  screenshot.force = FALSE
+)
+```
+
+
 ```{r, echo=FALSE, include=FALSE}
 library(heemod)
 

vignettes/g_heterogeneity.Rmd

@@ -14,6 +14,14 @@ vignette: >
 library(heemod)
 ```
 
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  screenshot.force = FALSE
+)
+```
+
+
 ```{r, include = FALSE}
 
 param <- define_parameters(
@@ -223,7 +231,7 @@ plot(res_h, type = "counts")
 
 Weights can be used in the analysis by including an optional column `.weights` in the new data to specify the respective weights of each strata in the target population.
 
-```{r, eval=FALSE}
+```{r}
 tab_indiv_w
 res_w <- update(res_mod, newdata = tab_indiv_w)
 res_w

vignettes/h_tabular.Rmd

@@ -10,6 +10,17 @@ vignette: >
   \usepackage[utf8]{inputenc}
 ---
 
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  purl = NOT_CRAN,
+  eval = NOT_CRAN,
+  screenshot.force = FALSE
+)
+```
+
 ```{r, echo=FALSE, include=FALSE}
 library(heemod)
 library(dplyr)

vignettes/i_reproduction.Rmd

@@ -10,6 +10,17 @@ vignette: >
   \usepackage[utf8]{inputenc}
 ---
 
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  purl = NOT_CRAN,
+  eval = NOT_CRAN,
+  screenshot.force = FALSE
+)
+```
+
 ```{r, echo=FALSE, include=FALSE}
 library(heemod)
 ```

vignettes/j_survival.Rmd

@@ -14,6 +14,17 @@ vignette: >
 library(heemod)
 ```
 
+```{r, echo = FALSE} 
+NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  purl = NOT_CRAN,
+  eval = NOT_CRAN,
+  screenshot.force = FALSE
+)
+```
+
 ## Distributions
 
 The `heemod` package provides a number of ways to estimate transition probabilities from survival distributions.  Survival distributions can come from at least three different sources:

vignettes/k_calibration.Rmd

@@ -5,15 +5,15 @@ output:
   rmarkdown::html_vignette:
     toc: true
 vignette: >
-  %\VignetteIndexEntry{Calibrating heemod models}
+  %\VignetteIndexEntry{Calibrating `heemod` models}
   %\VignetteEngine{knitr::rmarkdown}
   \usepackage[utf8]{inputenc}
 ---
 
 ```{r, echo=FALSE, include=FALSE}
 library(heemod)
 ```
- 
+
 ```{r, define, include = FALSE}
 param <- define_parameters(
     age_init = 60,