conform to CRAN with using https instead of http

DESCRIPTION

@@ -12,10 +12,10 @@ Description: Accelerate Bayesian analytics workflows in 'R' through interactive
     acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, 
     and programmers. This package relies on the sleek and elegant 'greta' package for 
     Bayesian inference. 'greta', in turn, is an interface into 'TensorFlow' from 'R'. 
-    Install 'greta' using instructions available here: <http://www.causact.com/install-tensorflow-greta-and-causact.html>.
-    See <http://github.com/flyaflya/causact> or <http://causact.com> for more documentation.
+    Install 'greta' using instructions available here: <https://www.causact.com/install-tensorflow-greta-and-causact.html>.
+    See <https://github.com/flyaflya/causact> or <https://www.causact.com/> for more documentation.
 License: MIT + file LICENSE
-URL: https://github.com/flyaflya/causact, https://causact.com
+URL: https://github.com/flyaflya/causact, https://www.causact.com/
 BugReports: https://github.com/flyaflya/causact/issues
 SystemRequirements: Python and TensorFlow are needed for Bayesian inference computations; 
     Python (>= 2.7.0) with header files and shared library;

R/data.R

@@ -79,7 +79,7 @@
 #'   \item{CPI2017}{The Corruption Perceptions Index score for 2017: A country/territory’s score indicates the perceived level of public sector corruption on a scale of 0-100, where 0 means that a country is perceived as highly corrupt and a 100 means that a country is perceived as very clean. }
 #'   \item{HDI2017}{The human development index score for 2017: the Human Development Index (HDI) is a measure of achievement in the basic dimensions of human development across countries.  It is an index made from a simple unweighted average of a nation’s longevity, education and income and is widely accepted in development discourse.}
 #' }
-#' @source \url{http://www.transparency.org/cpi} CPI data available from www.transparency.org/cpi.  Accessed Oct 1, 2018. Consumer Perception Index 2017 by Transparency International is licensed under CC-BY- ND 4.0.
+#' @source \url{https://www.transparency.org/cpi} CPI data available from www.transparency.org/cpi.  Accessed Oct 1, 2018. Consumer Perception Index 2017 by Transparency International is licensed under CC-BY- ND 4.0.
 #' @source \url{http://hdr.undp.org/en/content/human-development-index-hdi} HDA data accessed on Oct 1, 2018.
 #' @source \url{https://data.worldbank.org/} Population data accessed on Oct 1, 2018.
 "corruptDF"
@@ -149,7 +149,7 @@
 #' }
 "carModelDF"
 
-#' A representative sample from a random variable that represents the annual number of beach goers to Ocean City, MD beaches on hot days.  Think of this representative sample as coming from either a prior or posterior distribution.  An example using this sample is can be found in The Business Analyst's Guide To Business Analytics at http://causact.com.
+#' A representative sample from a random variable that represents the annual number of beach goers to Ocean City, MD beaches on hot days.  Think of this representative sample as coming from either a prior or posterior distribution.  An example using this sample is can be found in The Business Analyst's Guide To Business Analytics at https://www.causact.com/.
 #' @format A 4,000 element vector.
 #' \describe{
 #'   \item{totalBeachgoersRepSample}{a draw from a representative sample of total beachgoers to Ocean City, MD.}

README.Rmd

@@ -25,7 +25,7 @@ knitr::include_graphics("man/figures/causactDemo.gif")
 
 This package relies on the sleek and elegant `greta` package for Bayesian inference. `greta`, in turn, is an interface into `TensorFlow` from R.  Future iterations of the `causact` package will aim to be a front-end into several universal probablistic programming languages (e.g. Stan, Turing, Gen, etc.). 
 
-Using the `causact` package for Bayesian inference is featured in `A Business Analyst's Introduction to Business Analytics` available at http://causact.com/.
+Using the `causact` package for Bayesian inference is featured in `A Business Analyst's Introduction to Business Analytics` available at https://www.causact.com/.
 
 > NOTE: Package is under active development. Breaking changes are to be expected.  Feedback and encouragement is appreciated via github issues or Twitter (https://twitter.com/preposterior).
 
@@ -44,7 +44,7 @@ install.packages("remotes")
 remotes::install_github("flyaflya/causact")
 ```
 
-`causact` requires the `greta` package for Bayesian updating, which in turn, requires a specific version of `TensorFlow`.  Install both `greta` and `TensorFlow` using the instructions available here: https://www.causact.com/install-tensorflow-greta-and-causact.html 
+`causact` requires the `greta` package for Bayesian updating, which in turn, requires a specific version of `TensorFlow`.  Install both `greta` and `TensorFlow` using the instructions available here: https://www.causact.com/install-tensorflow-greta-and-causact.html. 
 
 ## Usage 
 

README.md

@@ -18,7 +18,7 @@ front-end into several universal probablistic programming languages
 
 Using the `causact` package for Bayesian inference is featured in `A
 Business Analyst's Introduction to Business Analytics` available at
-<http://causact.com/>.
+<https://www.causact.com/>.
 
 > NOTE: Package is under active development. Breaking changes are to be
 > expected. Feedback and encouragement is appreciated via github issues
@@ -38,7 +38,7 @@ or the development version from GitHub:
 `causact` requires the `greta` package for Bayesian updating, which in
 turn, requires a specific version of `TensorFlow`. Install both `greta`
 and `TensorFlow` using the instructions available here:
-<https://www.causact.com/install-tensorflow-greta-and-causact.html>
+<https://www.causact.com/install-tensorflow-greta-and-causact.html>.
 
 ## Usage
 
@@ -118,16 +118,16 @@ drawsDF  ### see top of data frame
 #> # A tibble: 4,000 x 4
 #>    theta_JpWrnglr theta_KiaForte theta_SbrOtbck theta_ToytCrll
 #>             <dbl>          <dbl>          <dbl>          <dbl>
-#>  1          0.856          0.250          0.575          0.217
-#>  2          0.842          0.245          0.569          0.180
-#>  3          0.847          0.227          0.681          0.222
-#>  4          0.846          0.232          0.667          0.229
-#>  5          0.839          0.247          0.666          0.226
-#>  6          0.849          0.234          0.563          0.204
-#>  7          0.848          0.254          0.675          0.203
-#>  8          0.862          0.231          0.554          0.217
-#>  9          0.865          0.249          0.556          0.192
-#> 10          0.880          0.235          0.567          0.240
+#>  1          0.810          0.265          0.603          0.213
+#>  2          0.806          0.275          0.634          0.235
+#>  3          0.812          0.200          0.688          0.228
+#>  4          0.898          0.277          0.562          0.193
+#>  5          0.853          0.168          0.673          0.227
+#>  6          0.831          0.309          0.720          0.221
+#>  7          0.833          0.192          0.573          0.213
+#>  8          0.881          0.202          0.635          0.189
+#>  9          0.864          0.232          0.552          0.216
+#> 10          0.859          0.188          0.628          0.183
 #> # ... with 3,990 more rows
 ```