Update URLs (#848)

vignettes/example.bib

@@ -4,14 +4,14 @@ @article{Neuenschwander2008
 number = {13},
 pages = {2420--2439},
 title = {{Critical aspects of the Bayesian approach to phase I cancer trials.}},
-url = {http://onlinelibrary.wiley.com/doi/10.1002/sim.3230},
+url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.3230},
 volume = {27},
 year = {2008}
 }
 
 @article{WhiteheadWilliamson1998,
 author = {Whitehead, J. and Williamson, D.},
-title = {{Bayesian decision procedures based on logistic regression models for dose-finding studies}}, 
+title = {{Bayesian decision procedures based on logistic regression models for dose-finding studies}},
 journal = {Journal of Biopharmaceutical Statistics},
 volume = {8},
 pages = {445--467},
@@ -31,7 +31,7 @@ @article{wy15
 @Misc{FACTS,
 author =   {FACTS Development Team},
 title =    {Fixed and Adaptive Clinical Trial Simulator},
-howpublished = {\url{http://www.berryconsultants.com/software/}},
+howpublished = {\url{https://www.berryconsultants.com/software/}},
 year = {2015}
 }
 

vignettes/vignettes.bib

@@ -19,7 +19,7 @@ @Manual{dfpk
 @article{Thall2010,
 abstract = {An early phase clinical trial is the first step in evaluating the effects in humans of a potential new anti-disease agent or combination of agents. Usually called "phase I" or "phase I/II" trials, these experiments typically have the nominal scientific goal of determining an acceptable dose, most often based on adverse event probabilities. This arose from a tradition of phase I trials to evaluate cytotoxic agents for treating cancer, although some methods may be applied in other medical settings, such as treatment of stroke or immunological diseases. Most modern statistical designs for early phase trials include model-based, outcome-adaptive decision rules that choose doses for successive patient cohorts based on data from previous patients in the trial. Such designs have seen limited use in clinical practice, however, due to their complexity, the requirement of intensive, computer-based data monitoring, and the medical community's resistance to change. Still, many actual applications of model-based outcome-adaptive designs have been remarkably successful in terms of both patient benefit and scientific outcome. In this paper, I will review several Bayesian early phase trial designs that were tailored to accommodate specific complexities of the treatment regime and patient outcomes in particular clinical settings.},
 author = {Thall, Peter F.},
-file = {:C$\backslash$:/Users/sabanesd/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Thall - 2010 - Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials.pdf:pdf},
+url = {https://pubmed.ncbi.nlm.nih.gov/21318084/},
 journal = {Statistical Science},
 keywords = {Adaptive design, Bayesian design, clinical trial,,adaptive design,and phrases,bayesian design,clinical trial,dose-finding,ii trial,phase i,phase i trial},
 number = {2},
@@ -122,7 +122,7 @@ @article{Liu2013
 @Manual{FACTS,
 author =   {{FACTS Development Team}},
 title =    {Fixed and Adaptive Clinical Trial Simulator},
-url = {http://www.berryconsultants.com/software},
+url = {https://www.berryconsultants.com/software},
 year = {2015}
 }
 
@@ -260,7 +260,7 @@ @article{Paoletti2015
 @article{Storer1989,
 abstract = {The Phase I clinical trial is a study intended to estimate the so-called maximum tolerable dose (MTD) of a new drug. Although there exists more or less a standard type of design for such trials, its development has been largely ad hoc. As usually implemented, the trial design has no intrinsic property that provides a generally satisfactory basis for estimation of the MTD. In this paper, the standard design and several simple alternatives are compared with regard to the conservativeness of the design and with regard to point and interval estimation of an MTD (33rd percentile) with small sample sizes. Using a Markov chain representation, we found several designs to be nearly as conservative as the standard design in terms of the proportion of patients entered at higher dose levels. In Monte Carlo simulations, two two-stage designs are found to provide reduced bias in maximum likelihood estimation of the MTD in less than ideal dose-response settings. Of the three methods considered for determining confidence intervals--the delta method, a method based on Fieller's theorem, and a likelihood ratio method--none was able to provide both usefully narrow intervals and coverage probabilities close to nominal.},
 author = {Storer, Barry E.},
-file = {:/rbamvgf10/U{\_}sabanesd{\$}/My Documents/Mendeley/1989/Storer/Storer - 1989 - Design and analysis of phase I clinical trials.pdf:pdf},
+url = {https://www.ncbi.nlm.nih.gov/2790129},
 journal = {Biometrics},
 mendeley-groups = {Dose-finding},
 month = {sep},
@@ -286,23 +286,23 @@ @Manual{JAGS
     author = {Martyn Plummer},
     title = {\proglang{JAGS}: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling},
     year = {2003},
-    url={http://mcmc-jags.sourceforge.net}
+    url={https://mcmc-jags.sourceforge.net}
 }
 
  @Manual{crmPack,
     title = {\pkg{crmPack}: Object-Oriented Implementation of CRM Designs},
     author = {{Sabanes Bove}, Daniel and Yeung, Wai Yin and Palermo, Giuseppe and Jaki, Thomas},
     year = {2018},
     note = {\proglang{R} package version 0.2.7},
-    url = {http://CRAN.R-project.org/package=crmPack}
+    url = {https://CRAN.R-project.org/package=crmPack}
   }
 
   @Manual{knitr2018,
     title = {\pkg{knitr}: A General-Purpose Package for Dynamic Report Generation in R},
     author = {Yihui Xie},
     year = {2018},
     note = {R package version 1.20},
-    url = {https://yihui.name/knitr/},
+    url = {https://yihui.org/knitr/},
   }
 
    @Manual{R2016,
@@ -320,23 +320,23 @@ @Manual{Stata2015
     organization = {StataCorp LP},
     address = {College Station, TX},
     year = {2015},
-    url = {http://www.stata.com/},
+    url = {https://www.stata.com/},
   }
 
  @Manual{Addplan2016,
     title = {\proglang{ADDPLAN}: Adaptive Design Trials Software},
     author = {{ICON Plc}},
     address = {Dublin, Ireland},
     year = {2016},
-    url = {http://www.iconplc.com/innovation/addplan/},
+    url = {https://www.iconplc.com/innovation/addplan/},
   }
 
  @Manual{East2016,
     title = {\proglang{East} 6: Statistical Software for the Design, Simulation and Monitoring of Clinical Trials},
     author = {{Cytel Inc.}},
     address = {Cambridge, MA},
     year = {2016},
-    url = {http://www.cytel.com/software/east/},
+    url = {https://www.cytel.com/software/east/},
   }
 
 
@@ -345,7 +345,7 @@ @Manual{SAS2003
 title = {\proglang{SAS/STAT} Software, Version~9.1},
 year = {2003},
 address = {Cary, NC},
-url = {http://www.sas.com/}
+url = {https://www.sas.com/}
 }
 
  @Manual{dfcrm2013,
@@ -361,7 +361,7 @@ @Book{ggplot2
     title = {\pkg{ggplot2}: Elegant Graphics for Data Analysis},
     publisher = {Springer-Verlag},
     year = {2009},
-    url = {http://ggplot2.org},
+    url = {https://ggplot2.tidyverse.org/},
   }
 
  @Book{advancedR,