minor updates for CRAN checks

Author: nhejazi

CRAN-RELEASE

@@ -1,2 +1,2 @@
-This package was submitted to CRAN on 2020-09-17.
-Once it is accepted, delete this file and tag the release (commit ad1344e).
+This package was submitted to CRAN on 2020-09-23.
+Once it is accepted, delete this file and tag the release (commit d659c8c).

cran-comments.md

@@ -18,5 +18,7 @@
 * In the initial attempt, there were two significant issues:
   * A few more links throughout used `http`; these have been moved to `https`
     or otherwise changed entirely.
-  * The old URL `https://sl3.tlverse.org` has been replaced by
-    `https://tlverse.org/sl3`
+  * The old URL `https://sl3.tlverse.org` has been replaced by a corrected
+    version `https://tlverse.org/sl3/`
+  * Changing of user options via calls to `options()` in the vignettes have
+    been removed entirely.

docs/articles/intro_txshift.html

@@ -4,7 +4,7 @@ pkgdown_sha: ~
 articles:
   intro_txshift: intro_txshift.html
   ipcw_txshift: ipcw_txshift.html
-last_built: 2020-09-17T07:31Z
+last_built: 2020-09-23T20:58Z
 urls:
   reference: https://code.nimahejazi.org/txshift/reference
   article: https://code.nimahejazi.org/txshift/articles

docs/articles/ipcw_txshift.html

@@ -11,10 +11,6 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, echo=FALSE}
-options(scipen = 999)
-```
-
 ## Introduction
 
 Stochastic treatment regimes present a relatively simple manner in which to
@@ -148,8 +144,9 @@ summary(tmle_hal_shift_2)
 
 To easily incorporate ensemble machine learning into the estimation procedure,
 we rely on the facilities provided in the [`sl3` R
-package](https://tlverse.org/sl3) [@coyle2020sl3]. For a complete guide on
-using the `sl3` R package, consider consulting https://tlverse.org/sl3.
+package](https://tlverse.org/sl3/) [@coyle2020sl3]. For a complete guide on
+using the `sl3` R package, consider consulting https://tlverse.org/sl3/ and
+https://tlverse.org/tlverse-handbook/sl3.html.
 
 ```{r, eval = FALSE}
 # SL learners to be used for most fits (e.g., IPCW, outcome regression)
@@ -174,7 +171,7 @@ sl_learner_density <- Lrnr_sl$new(learners = g_lib,
 
 ### Estimating Stochastic Interventions Effects with Stacked Regressions
 
-Using the framework provided by the [`sl3` package](https://tlverse.org/sl3),
+Using the framework provided by the [`sl3` package](https://tlverse.org/sl3/),
 the nuisance parameters of the TML estimator may be fit with ensemble learning,
 using the cross-validation framework of the Super Learner algorithm of
 @vdl2007super.

docs/pkgdown.yml

@@ -11,10 +11,6 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-```{r, echo=FALSE}
-options(scipen = 999)
-```
-
 ## Introduction
 
 For a more general introduction to the targeted maximum likelihood estimator
@@ -180,8 +176,9 @@ summary(tmle_hal_shift_2)
 
 To easily incorporate ensemble machine learning into the estimation procedure,
 we rely on the facilities provided in the [`sl3` R
-package](https://tlverse.org/sl3) [@coyle2020sl3]. For a complete guide on
-using the `sl3` R package, consider consulting https://tlverse.org/sl3.
+package](https://tlverse.org/sl3/) [@coyle2020sl3]. For a complete guide on
+using the `sl3` R package, consider consulting https://tlverse.org/sl3/ and
+https://tlverse.org/tlverse-handbook/sl3.html.
 
 ```{r make_sl, eval = FALSE}
 # SL learners to be used for most fits (e.g., IPCW, outcome regression)
@@ -204,7 +201,7 @@ sl_learner_density <- Lrnr_sl$new(learners = lrnr_dens_lib,
 
 ### Estimating the IPCW-TMLE with Optimal Stacked Regressions
 
-Using the framework provided by the [`sl3` package](https://tlverse.org/sl3),
+Using the framework provided by the [`sl3` package](https://tlverse.org/sl3/),
 the nuisance parameters of the TML estimator may be fit with ensemble learning,
 using the cross-validation framework of the Super Learner algorithm of
 @vdl2007super. In principal, it would be desirable to estimate the parameter