Adds https to cran badge

README.Rmd

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 [![R-CMD-check](https://github.com/eliocamp/metR/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/eliocamp/metR/actions/workflows/R-CMD-check.yaml)
 [![Coverage status](https://codecov.io/gh/eliocamp/metR/branch/master/graph/badge.svg?token=jVznWTMCpz)](https://app.codecov.io/gh/eliocamp/metR)
-[![CRAN status](http://www.r-pkg.org/badges/version/metR)](https://cran.r-project.org/package=metR)
+[![CRAN status](https://www.r-pkg.org/badges/version/metR)](https://CRAN.R-project.org/package=metR)
 [![DOI](https://zenodo.org/badge/96357263.svg)](https://zenodo.org/badge/latestdoi/96357263)
 
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 metR packages several functions and utilities that make R better for handling  meteorological data in the tidy data paradigm. It started mostly sa a packaging of assorted wrappers and tricks that I wrote for my day to day work as a researcher in atmospheric sciences. Since then, it has grown organically and for my own needs and feedback from users. 
 
 Conceptually it's divided into *visualization tools* and *data tools*. The former are geoms, stats and scales that help with plotting using [ggplot2](https://ggplot2.tidyverse.org/index.html), such as `stat_contour_fill()` or `scale_y_level()`, while the later are functions for common data processing tools in the atmospheric sciences, such as `Derivate()` or `EOF()`; these are implemented to work in the [data.table](https://github.com/Rdatatable/data.table/wiki) paradigm, but also work with regular data frames. 

README.md

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 [![Coverage
 status](https://codecov.io/gh/eliocamp/metR/branch/master/graph/badge.svg?token=jVznWTMCpz)](https://app.codecov.io/gh/eliocamp/metR)
 [![CRAN
-status](http://www.r-pkg.org/badges/version/metR)](https://cran.r-project.org/package=metR)
+status](https://www.r-pkg.org/badges/version/metR)](https://CRAN.R-project.org/package=metR)
 [![DOI](https://zenodo.org/badge/96357263.svg)](https://zenodo.org/badge/latestdoi/96357263)
 
+<!-- badges: end -->
+
 metR packages several functions and utilities that make R better for
 handling meteorological data in the tidy data paradigm. It started
 mostly sa a packaging of assorted wrappers and tricks that I wrote for