Merge pull request #6 from ropensci/dev

Minor changes to docs

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: GLMMcosinor
 Title: Fit a Cosinor Model Using a Generalised Mixed Modelling Framework
-Version: 0.2.0
+Version: 0.2.0.9000
 Authors@R: c(
     person("Rex", "Parsons", , "[email protected]", 
            role = c("aut", "cre"), comment = c(ORCID = "0000-0002-6053-8174")),
@@ -12,6 +12,7 @@ Authors@R: c(
            comment = c(ORCID = "0000-0002-7147-4778")),
     person("Prasad", "Chunduri", role = c("ctb", "fnd"),
            comment = c(ORCID = "0000-0001-7297-7580")),
+    person("Margaret", "Doyle", role = "ctb"),
     person("Michael", "Sachs", role = "rev",
            comment = c(ORCID = "0000-0002-1279-8676")),
     person("Joaquin", "Cavieres", role = "rev",
@@ -24,7 +25,7 @@ Description: Allows users to fit a cosinor model using the 'glmmTMB' framework.
     Cornelissen (2014) <doi:10.1186/1742-4682-11-16>.
 License: GPL (>= 3)
 URL: https://github.com/ropensci/GLMMcosinor,
-    https://ropensci.github.io/GLMMcosinor/
+    https://docs.ropensci.org/GLMMcosinor/
 BugReports: https://github.com/ropensci/GLMMcosinor/issues
 Imports:
     assertthat,

NEWS.md

@@ -1,3 +1,5 @@
+# GLMMcosinor (development version)
+
 # GLMMcosinor 0.2.0
 
 * Successful peer review from rOpenSci!

R/GLMMcosinor-package.R

@@ -2,7 +2,7 @@
 #' models with cosinor terms
 #' @description
 #' To learn more about how to use GLMMcosinor, see the vignettes at the
-#' \href{https://ropensci.github.io/GLMMcosinor/}{website} or using
+#' \href{https://docs.ropensci.org/GLMMcosinor/}{website} or using
 #' the following code:
 #'
 #' `browseVignettes(package = "GLMMcosinor")`

README.Rmd

@@ -23,7 +23,7 @@ knitr::opts_chunk$set(
 ```
 
 
-# GLMMcosinor <a href='https://ropensci.github.io/GLMMcosinor/'><img src='man/figures/logo.png' align="right" height="139" /></a>
+# GLMMcosinor <a href='https://docs.ropensci.org/GLMMcosinor/'><img src='man/figures/logo.png' align="right" height="139" /></a>
 
 <!-- badges: start -->
 [![R-CMD-check](https://github.com/ropensci/GLMMcosinor/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ropensci/GLMMcosinor/actions/workflows/R-CMD-check.yaml)
@@ -34,14 +34,22 @@ knitr::opts_chunk$set(
 <!-- badges: end -->
 
 
-The goal of `{GLMMcosinor}` is to fit a cosinor model to rhythmic data except with all the flexibility and functionality of a generalised linear (mixed-) model (GLM) framework using `{glmmTMB}`. 
+The goal of `{GLMMcosinor}` is to fit a cosinor model to rhythmic data with all
+the flexibility and functionality of a generalised linear (mixed-) model (GLM) 
+framework using `{glmmTMB}`. 
 
-For an introduction to the cosinor model, see the [getting started vignette](https://ropensci.github.io/GLMMcosinor/articles/GLMMcosinor.html).
+For an introduction to the cosinor model, see the [getting started vignette](https://docs.ropensci.org/GLMMcosinor/articles/GLMMcosinor.html).
 
-Existing statistical software for circadian data analyses (including `cosinor` [@sachs2023cosinor] or `circacompare` [@parsons2020circacompare]) allow the user to fit data using a regression model, but many are limited due to their inability to specify a link function, multiple components, or a hierarchical structure. `GLMMcosinor` aims to be comprehensive and flexible and is an improvement on other implementations of the cosinor model in R or Python. See table below for features available within currently available methods.
+Existing statistical software for circadian data analyses (including `cosinor` 
+[@sachs2023cosinor] or `circacompare` [@parsons2020circacompare]) allows users 
+to fit regression models to rhythmic data, but many are limited due to their 
+inability to specify a link function, multiple components, or a hierarchical 
+structure. `GLMMcosinor` aims to be comprehensive and flexible and is an 
+improvement on other implementations of cosinor model fitting in R or Python.
+See table below for features available within currently available methods.
 
-`GLMMcosinor` makes use of the `glmmTMB` package framework for estimation of 
-linear cosinor coefficients. If the model has no random effects, `glmmTMB` uses 
+`GLMMcosinor` makes use of the `glmmTMB` package framework for estimation of the
+cosinor model. If the model has no random effects, `glmmTMB` uses 
 maximum likelihood estimation to estimate the linear coefficients of the model. 
 For models with random effects, a Laplace approximation is used to integrate 
 over the random effects. This approximation is handled by the
@@ -63,7 +71,8 @@ flextable::save_as_image(
 
 ## Installation
 
-You can install the development version of GLMMcosinor from [GitHub](https://github.com/) with:
+You can install the development version of GLMMcosinor from 
+[GitHub](https://github.com/) with:
 
 ``` r
 # install.packages("remotes")

README.md

@@ -1,7 +1,7 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# GLMMcosinor <a href='https://ropensci.github.io/GLMMcosinor/'><img src='man/figures/logo.png' align="right" height="139" /></a>
+# GLMMcosinor <a href='https://docs.ropensci.org/GLMMcosinor/'><img src='man/figures/logo.png' align="right" height="139" /></a>
 
 <!-- badges: start -->
 
@@ -18,26 +18,26 @@ Review](https://badges.ropensci.org/603_status.svg)](https://github.com/ropensci
 <!-- badges: end -->
 
 The goal of `{GLMMcosinor}` is to fit a cosinor model to rhythmic data
-except with all the flexibility and functionality of a generalised
-linear (mixed-) model (GLM) framework using `{glmmTMB}`.
+with all the flexibility and functionality of a generalised linear
+(mixed-) model (GLM) framework using `{glmmTMB}`.
 
 For an introduction to the cosinor model, see the [getting started
-vignette](https://ropensci.github.io/GLMMcosinor/articles/GLMMcosinor.html).
+vignette](https://docs.ropensci.org/GLMMcosinor/articles/GLMMcosinor.html).
 
 Existing statistical software for circadian data analyses (including
-`cosinor` (Sachs 2023) or `circacompare` (Parsons et al. 2020)) allow
-the user to fit data using a regression model, but many are limited due
-to their inability to specify a link function, multiple components, or a
-hierarchical structure. `GLMMcosinor` aims to be comprehensive and
-flexible and is an improvement on other implementations of the cosinor
-model in R or Python. See table below for features available within
+`cosinor` (Sachs 2023) or `circacompare` (Parsons et al. 2020)) allows
+users to fit regression models to rhythmic data, but many are limited
+due to their inability to specify a link function, multiple components,
+or a hierarchical structure. `GLMMcosinor` aims to be comprehensive and
+flexible and is an improvement on other implementations of cosinor model
+fitting in R or Python. See table below for features available within
 currently available methods.
 
 `GLMMcosinor` makes use of the `glmmTMB` package framework for
-estimation of linear cosinor coefficients. If the model has no random
-effects, `glmmTMB` uses maximum likelihood estimation to estimate the
-linear coefficients of the model. For models with random effects, a
-Laplace approximation is used to integrate over the random effects. This
+estimation of the cosinor model. If the model has no random effects,
+`glmmTMB` uses maximum likelihood estimation to estimate the linear
+coefficients of the model. For models with random effects, a Laplace
+approximation is used to integrate over the random effects. This
 approximation is handled by the
 [`TMB`](https://cran.r-project.org/package=TMB) package which uses
 automatic differentiation of the joint likelihood function to
@@ -114,7 +114,7 @@ citation("GLMMcosinor")
 #> 
 #>   Parsons R, Jayasinghe O, White N, Rawashdeh O (2024). _GLMMcosinor:
 #>   Fit a Cosinor Model Using a Generalised Mixed Modelling Framework_. R
-#>   package version 0.2.0, https://ropensci.github.io/GLMMcosinor/,
+#>   package version 0.2.0.9000, https://docs.ropensci.org/GLMMcosinor/,
 #>   <https://github.com/ropensci/GLMMcosinor>.
 #> 
 #> A BibTeX entry for LaTeX users is
@@ -124,8 +124,8 @@ citation("GLMMcosinor")
 #> Framework},
 #>     author = {Rex Parsons and Oliver Jayasinghe and Nicole White and Oliver Rawashdeh},
 #>     year = {2024},
-#>     note = {R package version 0.2.0, 
-#> https://ropensci.github.io/GLMMcosinor/},
+#>     note = {R package version 0.2.0.9000, 
+#> https://docs.ropensci.org/GLMMcosinor/},
 #>     url = {https://github.com/ropensci/GLMMcosinor},
 #>   }
 ```

_pkgdown.yml

@@ -1,4 +1,4 @@
-url: https://ropensci.github.io/GLMMcosinor/
+url: https://docs.ropensci.org/GLMMcosinor/
 template:
   bootstrap: 5
   params:

codemeta.json

@@ -4,11 +4,11 @@
   "identifier": "GLMMcosinor",
   "description": "Allows users to fit a cosinor model using the 'glmmTMB' framework. This extends on existing cosinor modelling packages, including 'cosinor' and 'circacompare', by including a wide range of available link functions and the capability to fit mixed models. The cosinor model is described by Cornelissen (2014) <doi:10.1186/1742-4682-11-16>.",
   "name": "GLMMcosinor: Fit a Cosinor Model Using a Generalised Mixed Modelling Framework",
-  "relatedLink": "https://ropensci.github.io/GLMMcosinor/",
+  "relatedLink": "https://docs.ropensci.org/GLMMcosinor/",
   "codeRepository": "https://github.com/ropensci/GLMMcosinor",
   "issueTracker": "https://github.com/ropensci/GLMMcosinor/issues",
   "license": "https://spdx.org/licenses/GPL-3.0",
-  "version": "0.2.0",
+  "version": "0.2.0.9000",
   "programmingLanguage": {
     "@type": "ComputerLanguage",
     "name": "R",
@@ -47,6 +47,11 @@
       "givenName": "Prasad",
       "familyName": "Chunduri",
       "@id": "https://orcid.org/0000-0001-7297-7580"
+    },
+    {
+      "@type": "Person",
+      "givenName": "Margaret",
+      "familyName": "Doyle"
     }
   ],
   "funder": [
@@ -329,7 +334,7 @@
     },
     "SystemRequirements": null
   },
-  "fileSize": "3088.62KB",
+  "fileSize": "3088.739KB",
   "releaseNotes": "https://github.com/ropensci/GLMMcosinor/blob/master/NEWS.md",
   "readme": "https://github.com/ropensci/GLMMcosinor/blob/main/README.md",
   "contIntegration": ["https://github.com/ropensci/GLMMcosinor/actions/workflows/R-CMD-check.yaml", "https://app.codecov.io/gh/ropensci/GLMMcosinor?branch=main"],

vignettes/GLMMcosinor.Rmd

@@ -126,10 +126,10 @@ also be included in the formula as well as random effects. Additionally,
 zero-inflation (`ziformula`) and dispersion (`dispformula`) formulae can
 be incorporated if required. For detailed examples of how to specify
 these types of models, see the
-[mixed-models](https://ropensci.github.io/GLMMcosinor/articles/mixed-models.html),
-[model-specification](https://ropensci.github.io/GLMMcosinor/articles/model-specification.html)
+[mixed-models](https://docs.ropensci.org/GLMMcosinor/articles/mixed-models.html),
+[model-specification](https://docs.ropensci.org/GLMMcosinor/articles/model-specification.html)
 and
-[multiple-components](https://ropensci.github.io/GLMMcosinor/articles/multiple-components.html)
+[multiple-components](https://docs.ropensci.org/GLMMcosinor/articles/multiple-components.html)
 vignettes.
 
 For example, consider the following model and its output:
@@ -268,17 +268,17 @@ The `cglmm()` function allows you to specify different types of cosinor
 models with or without grouping variables. The function can also
 generate dispersion models and zero-inflation models. For more detailed
 explanations and examples, see the
-[model-specification](https://ropensci.github.io/GLMMcosinor/articles/model-specification.html)
+[model-specification](https://docs.ropensci.org/GLMMcosinor/articles/model-specification.html)
 article.
 
 Additionally, the `cglmm()` function provides more advanced
 functionality for multi-component models, and detailed explanations can
 be found in the
-[multiple-components](https://ropensci.github.io/GLMMcosinor/articles/multiple-components.html)
+[multiple-components](https://docs.ropensci.org/GLMMcosinor/articles/multiple-components.html)
 article.
 
 The `cglmm()` function also allows mixed model specification. See the
-[mixed-models](https://ropensci.github.io/GLMMcosinor/articles/mixed-models.html)
+[mixed-models](https://docs.ropensci.org/GLMMcosinor/articles/mixed-models.html)
 article for more details.
 
 ## Using `summary()` and testing for differences between estimates
@@ -468,7 +468,7 @@ generated plot will have x-limits corresponding to the minimum and
 maximum values of the time-vector in the original dataframe, although
 the x-limits can be manually defined by the user using the `xlims`
 argument. The details of using the `autoplot` function are found in the
-[model-visualisations](https://ropensci.github.io/GLMMcosinor/articles/model-visualisations.html)
+[model-visualisations](https://docs.ropensci.org/GLMMcosinor/articles/model-visualisations.html)
 vignette.
 
 ## References

vignettes/data-simulations.Rmd

@@ -1,5 +1,5 @@
 ---
-title: "data-simulations"
+title: "Simulating data"
 author: "Oliver Jayasinghe and Rex Parsons"
 output: rmarkdown::html_vignette
 vignette: >
@@ -19,7 +19,7 @@ knitr::opts_chunk$set(
 library(GLMMcosinor)
 ```
 
-## Using: 'simulate_cosinor.R'
+## Simulating rhythmic data
 
 `simulate_cosinor` allows users to simulate circadian data from Gaussian, Gamma, Binomial, or Poisson distributions. It also supports generation of multi-component data, as well as simulation of grouped data with two levels.
 

vignettes/mixed-models.Rmd

@@ -1,5 +1,5 @@
 ---
-title: "mixed-models"
+title: "Mixed models"
 author: "Oliver Jayasinghe and Rex Parsons"
 output: rmarkdown::html_vignette
 vignette: >
@@ -15,7 +15,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-```{r setup}
+```{r setup, message=FALSE}
 library(GLMMcosinor)
 library(dplyr)
 library(ggplot2)

vignettes/model-specification.Rmd

@@ -1,5 +1,5 @@
 ---
-title: "model-specification"
+title: "Model specification"
 author: "Oliver Jayasinghe and Rex Parsons"
 output: rmarkdown::html_vignette
 vignette: >
@@ -24,7 +24,7 @@ knitr::opts_chunk$set(
 #' @srrstats {RE1.1}
 ```
 
-```{r setup}
+```{r setup, message=FALSE}
 library(GLMMcosinor)
 library(dplyr)
 ```
@@ -95,10 +95,10 @@ variables in the `data.frame`. Any other combination of covariates can
 also be included in the formula as well as random effects and
 zero-inflation (`ziformula`) and dispersion (`dispformula`) formulae.
 For detailed examples of how to specify models, see the
-[mixed-models](https://ropensci.github.io/GLMMcosinor/articles/mixed-models.html),
-[model-specification](https://ropensci.github.io/GLMMcosinor/articles/model-specification.html)
+[mixed-models](https://docs.ropensci.org/GLMMcosinor/articles/mixed-models.html),
+[model-specification](https://docs.ropensci.org/GLMMcosinor/articles/model-specification.html)
 and
-[multiple-components](https://ropensci.github.io/GLMMcosinor/articles/multiple-components.html)
+[multiple-components](https://docs.ropensci.org/GLMMcosinor/articles/multiple-components.html)
 vignettes.
 
 ## Using cglmm()
@@ -144,7 +144,7 @@ The output shows the estimates for the raw coefficients in addition to
 the transformed estimates for amplitude (amp) and acrophase (acr) and
 MESOR (`(Intercept)`). The previous section of this vignette: [An
 overview of the statistical methods used for parameter
-estimation](https://ropensci.github.io/GLMMcosinor/articles/GLMMcosinor.html#an-overview-of-the-statistical-methods-used-for-parameter-estimation)
+estimation](https://docs.ropensci.org/GLMMcosinor/articles/GLMMcosinor.html#an-overview-of-the-statistical-methods-used-for-parameter-estimation)
 outlines the difference between the raw coefficients and the transformed
 coefficients.
 
@@ -358,7 +358,7 @@ component, we would specify the `group` argument as
 `group = c("group", NA))`.
 
 For a detailed explanation of how to specify multi-component models, see
-[multiple-components](https://ropensci.github.io/GLMMcosinor/articles/multiple-components.html)
+[multiple-components](https://docs.ropensci.org/GLMMcosinor/articles/multiple-components.html)
 
 ### Dispersion and zero-inflation model specification
 
@@ -410,7 +410,7 @@ models will not be generated in the output.
 number of components in the dispersion and zero-inflation formulas were
 chosen arbitrarily and purely for demonstration.*
 
-## Using: 'summary(cglmm)'
+## Using `summary(cglmm)`
 
 The `summary()` method for `cglmm` objects provides a more detailed
 summary of the model and its parameter estimates and uncertainty. It