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Fixes #369

README.Rmd

@@ -21,8 +21,8 @@ knitr::opts_chunk$set(
 ```
 
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.03393/status.svg)](https://doi.org/10.21105/joss.03393)
-[![downloads](http://cranlogs.r-pkg.org/badges/see)](https://cran.r-project.org/package=see)
-[![total](https://cranlogs.r-pkg.org/badges/grand-total/see)](https://cranlogs.r-pkg.org/) 
+[![downloads](https://cranlogs.r-pkg.org/badges/see)](https://cran.r-project.org/package=see)
+[![total](https://cranlogs.r-pkg.org/badges/grand-total/see)](https://cranlogs.r-pkg.org/)
 
 ***"Damned are those who believe without seeing"***
 
@@ -51,15 +51,15 @@ ggplot(results) +
   geom_line(aes(x = wt, y = .fitted, color = `factor(cyl)`))
 ```
 
-A number of packages have been developed to extend *ggplot2* and assist with model visualization (for a sampling of these packages, visit [ggplot2-gallery](https://exts.ggplot2.tidyverse.org/gallery/)). Some of these packages provide functions for additional geoms, annotations, or common visualization types without linking them to a specific statistical analysis or fundamentally changing the *ggplot2* workflow (e.g., *ggrepel*, *ggalluvial*, *ggridges*, *ggdist*, *ggpubr*, etc.). Other *ggplot2* extensions provide functions to generate publication-ready visualizations for specific types of models (e.g., *metaviz*, *tidymv*, *sjPlot*, *survminer*). For example, the *ggstatsplot* package [@Patil2021] offers visualizations for statistical analysis of one-way factorial designs, and the *plotmm* package [@Waggoner2020] supports specific types of mixture model objects. 
+A number of packages have been developed to extend *ggplot2* and assist with model visualization (for a sampling of these packages, visit [ggplot2-gallery](https://exts.ggplot2.tidyverse.org/gallery/)). Some of these packages provide functions for additional geoms, annotations, or common visualization types without linking them to a specific statistical analysis or fundamentally changing the *ggplot2* workflow (e.g., *ggrepel*, *ggalluvial*, *ggridges*, *ggdist*, *ggpubr*, etc.). Other *ggplot2* extensions provide functions to generate publication-ready visualizations for specific types of models (e.g., *metaviz*, *tidymv*, *sjPlot*, *survminer*). For example, the *ggstatsplot* package [@Patil2021] offers visualizations for statistical analysis of one-way factorial designs, and the *plotmm* package [@Waggoner2020] supports specific types of mixture model objects.
 
 The aim of the *see* package is to produce visualizations for a wide variety of models and statistical analyses in a way that is tightly linked with the model fitting process and requires minimal interruption of users' workflow. *see* accomplishes this aim by providing a single `plot()` method for objects created by the other *easystats* packages, such as *parameters* tables, *modelbased* predictions, *performance* diagnostic tests, *correlation* matrices, and so on. The *easystats* packages compute numeric results for a wide range of statistical models, and the *see* package acts as a visual support to the entire *easystats* ecosystem. As such, visualizations corresponding to all stages of statistical analysis, from model fitting to diagnostics to reporting, can be easily created using *see*. *see* plots are compatible with other *ggplot2* functions for further customization (e.g., `labs()` for a plot title). In addition, *see* provides several aesthetic utilities to embellish both *easystats* plots and other *ggplot2* plots. The result is a package that minimizes the barrier to producing high-quality statistical visualizations in R.
 
 The central goal of *easystats* is to make the task of doing statistics in R as easy as possible. This goal is realized through intuitive and consistent syntax, consistent and transparent argument names, comprehensive documentation, informative warnings and error messages, and smart functions with sensible default parameter values. The *see* package follows this philosophy by using a single access point---the generic `plot()` method---for visualization of all manner of statistical results supported by *easystats*.
 
 # Installation
 
-[![CRAN](http://www.r-pkg.org/badges/version/see)](https://cran.r-project.org/package=see) [![see status badge](https://easystats.r-universe.dev/badges/see)](https://easystats.r-universe.dev) [![R-CMD-check](https://github.com/easystats/see/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/easystats/see/actions) [![codecov](https://codecov.io/gh/easystats/see/branch/main/graph/badge.svg)](https://app.codecov.io/gh/easystats/see)
+[![CRAN](https://www.r-pkg.org/badges/version/see)](https://cran.r-project.org/package=see) [![see status badge](https://easystats.r-universe.dev/badges/see)](https://easystats.r-universe.dev) [![R-CMD-check](https://github.com/easystats/see/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/easystats/see/actions) [![codecov](https://codecov.io/gh/easystats/see/branch/main/graph/badge.svg)](https://app.codecov.io/gh/easystats/see)
 
 The *see* package is available on CRAN, while its latest development version is available on R-universe (from _rOpenSci_).
 
@@ -178,7 +178,7 @@ predicted <- estimate_expectation(model, data = "grid")
 plot(predicted)
 ```
 
-One can also visualize *marginal means* (i.e., the mean at each factor level averaged over other predictors) using `estimate_means()`, that is then passed to `plot()`. 
+One can also visualize *marginal means* (i.e., the mean at each factor level averaged over other predictors) using `estimate_means()`, that is then passed to `plot()`.
 
 ```{r modelbased2, error=TRUE}
 means <- estimate_means(model)
@@ -340,7 +340,7 @@ In case you want to file an issue or contribute in another way to the package, p
 
 # Code of Conduct
 
-Please note that this project is released with a 
+Please note that this project is released with a
 [Contributor Code of Conduct](https://easystats.github.io/see/CODE_OF_CONDUCT.html). By participating in this project you agree to abide by its terms.
 
 # References

README.md

@@ -2,7 +2,7 @@
 # see: Model Visualisation Toolbox for ‘easystats’ and ‘ggplot2’ <img src='man/figures/logo.png' align="right" height="139" />
 
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.03393/status.svg)](https://doi.org/10.21105/joss.03393)
-[![downloads](http://cranlogs.r-pkg.org/badges/see)](https://cran.r-project.org/package=see)
+[![downloads](https://cranlogs.r-pkg.org/badges/see)](https://cran.r-project.org/package=see)
 [![total](https://cranlogs.r-pkg.org/badges/grand-total/see)](https://cranlogs.r-pkg.org/)
 
 ***“Damned are those who believe without seeing”***
@@ -94,7 +94,7 @@ results supported by *easystats*.
 
 # Installation
 
-[![CRAN](http://www.r-pkg.org/badges/version/see)](https://cran.r-project.org/package=see)
+[![CRAN](https://www.r-pkg.org/badges/version/see)](https://cran.r-project.org/package=see)
 [![see status
 badge](https://easystats.r-universe.dev/badges/see)](https://easystats.r-universe.dev)
 [![R-CMD-check](https://github.com/easystats/see/workflows/R-CMD-check/badge.svg?branch=main)](https://github.com/easystats/see/actions)