Don’t evaluate mr_dgp vignette so it builds without OpenGWAS credentials

DESCRIPTION

@@ -2,16 +2,20 @@ Package: simulateGP
 Title: Functions for Simulating Genotype-Phenotype Relationships
 Version: 0.1.3
 Authors@R: c(
-    person("Gibran", "Hemani", , "explodecomputer@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0920-1055")),
+    person("Gibran", "Hemani", , "explodecomputer@gmail.com", role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0003-0920-1055")),
     person("John", "Ferguson", , "john.ferguson@nuigalway.ie", role = "aut"),
-    person("Rita", "Rasteiro", , "rita.rasteiro@bristol.ac.uk", role = c("aut"), comment = c(ORCID = "0000-0002-4217-3060"))
+    person("Rita", "Rasteiro", , "rita.rasteiro@bristol.ac.uk", role = "aut",
+           comment = c(ORCID = "0000-0002-4217-3060"))
   )
 Description: Functions for simulating SNP effects on phenotypes. Functions
     for simulating disease risk on probability and liability scales.
     Simulations to quickly obtain estimates of effect sizes for simulated
     genotype-phenotypes.
 License: MIT + file LICENSE
-URL: https://explodecomputer.github.io/simulateGP/
+URL: https://explodecomputer.github.io/simulateGP/,
+    https://github.com/explodecomputer/simulateGP,
+    https://mrcieu.r-universe.dev/simulateGP
 BugReports: https://github.com/explodecomputer/simulateGP/issues
 Depends:
     R (>= 4.0.0)
@@ -48,7 +52,7 @@ Remotes:
     mrcieu/gwasglue2,
     mrcieu/ieugwasr,
     mrcieu/TwoSampleMR
+Config/roxygen2/markdown: TRUE
+Config/roxygen2/version: 8.1.0
 Encoding: UTF-8
 LazyData: true
-Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1

NAMESPACE

@@ -50,22 +50,24 @@ export(y_to_binary)
 importFrom(graphics,hist)
 importFrom(magrittr,"%>%")
 importFrom(rlang,.data)
-importFrom(stats,anova)
-importFrom(stats,cor)
-importFrom(stats,cov)
-importFrom(stats,glm)
-importFrom(stats,lm)
-importFrom(stats,na.exclude)
-importFrom(stats,pf)
-importFrom(stats,plogis)
-importFrom(stats,pnorm)
-importFrom(stats,pt)
-importFrom(stats,qnorm)
-importFrom(stats,quantile)
-importFrom(stats,rbinom)
-importFrom(stats,rnorm)
-importFrom(stats,runif)
-importFrom(stats,start)
-importFrom(stats,uniroot)
-importFrom(stats,var)
+importFrom(stats,
+  anova,
+  cor,
+  cov,
+  glm,
+  lm,
+  na.exclude,
+  pf,
+  plogis,
+  pnorm,
+  pt,
+  qnorm,
+  quantile,
+  rbinom,
+  rnorm,
+  runif,
+  start,
+  uniroot,
+  var
+)
 importFrom(utils,write.table)

R/simulateGP-package.r

@@ -5,7 +5,5 @@
 #'
 #' **Full documentation available here:** [https://explodecomputer.github.io/simulateGP](https://explodecomputer.github.io/simulateGP)
 #'
-#' @name simulateGP-package
 #' @aliases simulateGP simulategp
-#' @docType package
-NULL
+"_PACKAGE"

vignettes/mr_dgp.rmd

@@ -9,6 +9,14 @@ vignette: >
   %\VignetteEncoding{UTF-8}  
 ---
 
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  eval = FALSE
+)
+```
+
 Background: [Deep IV](http://proceedings.mlr.press/v70/hartford17a.html) can infer causal effects in which the sample is stratified by one or many covariates, and there is a different causal effect within each stratum.
 
 The covariates don't necessarily need to be directly measured, could have a large number of proxy variables for example.
@@ -26,14 +34,14 @@ bmi_hits <- ieugwasr::tophits("ukb-b-19953")
 str(bmi_hits)
 ```
 
-There are `r nrow(bmi_hits)` independent instruments for BMI. All the instruments have small effects:
+This gives the independent instruments for BMI. All the instruments have small effects:
 
 ```{r}
 bmi_hits$rsq <- 2 * bmi_hits$beta^2 * bmi_hits$eaf * (1 - bmi_hits$eaf)
 hist(bmi_hits$rsq, breaks=100)
 ```
 
-In total the `r nrow(bmi_hits)` BMI hits explain `r sum(bmi_hits$rsq) * 100`% of the variance in BMI.
+In total the BMI hits explain `sum(bmi_hits$rsq) * 100`% of the variance in BMI.
 
 What is the effect on coronary heart disease?
 
@@ -78,7 +86,7 @@ Problem with this model is that if we assume that all the instruments explain th
 1. Choose a number of causal variants for the trait (large, e.g. >10000)
 2. Sample the allele frequency distribution of all causal variants
 3. Sample the effects for all causal variants
-4. Retain only those that cumulatively explain some amount of variance. e.g. For BMI, the variance exlained is `r round(sum(bmi_hits$rsq, 2))`, so we just want the set of variants that are required to explain that much variance.
+4. Retain only those that cumulatively explain some amount of variance. e.g. For BMI, the variance explained is `sum(bmi_hits$rsq)`, so we just want the set of variants that are required to explain that much variance.
 
 Ultimately, we can draw allele frequencies from some distribution e.g.