0-1-0 Release Candidate (#26)

* expand description

* have gee_fit be internal function

* update license year

* fix urls

* seed readme to avoid changes

* fix doi link

* update parsnip vesions

* Increment version number to 0.1.0

* typo in readme

* CoC link forwarding

Co-authored-by: Max Kuhn <mxkuhn@gmail.com>

Author: EmilHvitfeldt

DESCRIPTION

@@ -1,16 +1,18 @@
 Package: multilevelmod
 Title: Model Wrappers for Multi-Level Models
-Version: 0.0.0.9000
+Version: 0.1.0
 Authors@R: c(
     person("Max", "Kuhn", , "max@rstudio.com", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-2402-136X")),
     person("RStudio", role = "cph")
   )
 Description: Bindings for hierarchical regression models for use with the
-    'parsnip' package.
+    'parsnip' package. Models include longitudinal generalized linear models 
+    (Liang and Zeger, 1986) <doi:10.1093/biomet/73.1.13>, and mixed-effect
+    models (Pinheiro and Bates) <doi:10.1007/978-1-4419-0318-1_1>.
 License: MIT + file LICENSE
 Depends: 
-    parsnip (>= 0.1.7),
+    parsnip (>= 0.2.0),
     R (>= 2.10)
 Imports: 
     dplyr,
@@ -31,14 +33,13 @@ Suggests:
     tidymodels
 VignetteBuilder: 
     knitr
-Remotes:
-    tidymodels/parsnip#631
 Config/Needs/website: 
+    tidymodels/tidymodels
     tidyverse/tidytemplate
 Encoding: UTF-8
 Language: en-US
 LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.1.2
-URL: ttps://github.com/tidymodels/multilevelmod,
+URL: https://github.com/tidymodels/multilevelmod,
     http://multilevelmod.tidymodels.org/

LICENSE

@@ -1,2 +1,2 @@
-YEAR: 2020
+YEAR: 2022
 COPYRIGHT HOLDER: RStudio

R/gee.R

@@ -70,6 +70,8 @@ gee_formula <- function(f) {
 #' Also, because of issues with the `gee()` function, a supplementary call to
 #' `glm()` is needed to get the rank and QR decomposition objects so that
 #' `predict()` can be used.
+#' @return A gee object
+#' @keywords internal
 #' @export
 gee_fit <- function(formula, data, family = gaussian, ...) {
   f <- gee_formula(formula)

README.Rmd

@@ -16,9 +16,8 @@ knitr::opts_chunk$set(
 # multilevelmod
 
 <!-- badges: start -->
-[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
-[![CRAN status](https://www.r-pkg.org/badges/version/multilevelmod)](https://CRAN.R-project.org/package=multilevelmod)
-[![Codecov test coverage](https://codecov.io/gh/topepo/multilevelmod/branch/main/graph/badge.svg)](https://codecov.io/gh/topepo/multilevelmod?branch=main)
+[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
+[![Codecov test coverage](https://codecov.io/gh/topepo/multilevelmod/branch/main/graph/badge.svg)](https://app.codecov.io/gh/topepo/multilevelmod?branch=main)
 [![R-CMD-check](https://github.com/tidymodels/multilevelmod/workflows/R-CMD-check/badge.svg)](https://github.com/tidymodels/multilevelmod/actions)
 <!-- badges: end -->
 
@@ -33,7 +32,6 @@ multilevelmod enables the use of multi-level models (a.k.a mixed-effects models,
 You can install the released version of multilevelmod from [CRAN](https://CRAN.R-project.org) with:
 
 ``` r
-# not yet
 install.packages("multilevelmod")
 ```
 
@@ -43,7 +41,7 @@ For the development version:
 devtools::install_github("tidymodels/multilevelmod")
 ```
 
-## Avaliable Engines
+## Available Engines
 
 The multilevelmod package provides engines for the models in the following table.
 
@@ -77,6 +75,7 @@ The `sleepstudy` data is used as an example:
 
 ```{r sleep-lme}
 library(multilevelmod)
+set.seed(1234)
 data(sleepstudy, package = "lme4")
 
 mixed_model_spec <- linear_reg() %>% set_engine("lmer")
@@ -103,13 +102,13 @@ hier_model_fit
 
 ## Contributing
 
-This project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
+This project is released with a [Contributor Code of Conduct](https://www.contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.
 
-- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://rstd.io/tidymodels-community).
+- For questions and discussions about tidymodels packages, modeling, and machine learning, please [post on RStudio Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
 
 - If you think you have encountered a bug, please [submit an issue](https://github.com/tidymodels/multilevelmod/issues).
 
-- Either way, learn how to create and share a [reprex](https://rstd.io/reprex) (a minimal, reproducible example), to clearly communicate about your code.
+- Either way, learn how to create and share a [reprex](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question) (a minimal, reproducible example), to clearly communicate about your code.
 
 - Check out further details on [contributing guidelines for tidymodels packages](https://www.tidymodels.org/contribute/) and [how to get help](https://www.tidymodels.org/help/).
 

README.md

@@ -6,11 +6,9 @@
 <!-- badges: start -->
 
 [![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
-[![CRAN
-status](https://www.r-pkg.org/badges/version/multilevelmod)](https://CRAN.R-project.org/package=multilevelmod)
+experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html)
 [![Codecov test
-coverage](https://codecov.io/gh/topepo/multilevelmod/branch/main/graph/badge.svg)](https://codecov.io/gh/topepo/multilevelmod?branch=main)
+coverage](https://codecov.io/gh/topepo/multilevelmod/branch/main/graph/badge.svg)](https://app.codecov.io/gh/topepo/multilevelmod?branch=main)
 [![R-CMD-check](https://github.com/tidymodels/multilevelmod/workflows/R-CMD-check/badge.svg)](https://github.com/tidymodels/multilevelmod/actions)
 <!-- badges: end -->
 
@@ -28,7 +26,6 @@ You can install the released version of multilevelmod from
 [CRAN](https://CRAN.R-project.org) with:
 
 ``` r
-# not yet
 install.packages("multilevelmod")
 ```
 
@@ -38,7 +35,7 @@ For the development version:
 devtools::install_github("tidymodels/multilevelmod")
 ```
 
-## Avaliable Engines
+## Available Engines
 
 The multilevelmod package provides engines for the models in the
 following table.
@@ -72,6 +69,7 @@ The `sleepstudy` data is used as an example:
 
 ``` r
 library(multilevelmod)
+set.seed(1234)
 data(sleepstudy, package = "lme4")
 
 mixed_model_spec <- linear_reg() %>% set_engine("lmer")
@@ -116,7 +114,7 @@ hier_model_fit
 #>  observations: 180
 #> ------
 #>             Median MAD_SD
-#> (Intercept) 251.5    6.4 
+#> (Intercept) 251.3    6.5 
 #> Days         10.5    1.7 
 #> 
 #> Auxiliary parameter(s):
@@ -125,9 +123,9 @@ hier_model_fit
 #> 
 #> Error terms:
 #>  Groups   Name        Std.Dev. Corr
-#>  Subject  (Intercept) 24           
-#>           Days         7       0.06
-#>  Residual             26           
+#>  Subject  (Intercept) 24.1         
+#>           Days         6.9     0.09
+#>  Residual             26.0         
 #> Num. levels: Subject 18 
 #> 
 #> ------
@@ -138,19 +136,20 @@ hier_model_fit
 ## Contributing
 
 This project is released with a [Contributor Code of
-Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
+Conduct](https://www.contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html).
 By contributing to this project, you agree to abide by its terms.
 
 -   For questions and discussions about tidymodels packages, modeling,
     and machine learning, please [post on RStudio
-    Community](https://rstd.io/tidymodels-community).
+    Community](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question).
 
 -   If you think you have encountered a bug, please [submit an
     issue](https://github.com/tidymodels/multilevelmod/issues).
 
 -   Either way, learn how to create and share a
-    [reprex](https://rstd.io/reprex) (a minimal, reproducible example),
-    to clearly communicate about your code.
+    [reprex](https://community.rstudio.com/new-topic?category_id=15&tags=tidymodels,question)
+    (a minimal, reproducible example), to clearly communicate about your
+    code.
 
 -   Check out further details on [contributing guidelines for tidymodels
     packages](https://www.tidymodels.org/contribute/) and [how to get

man/gee_fit.Rd

@@ -143,7 +143,7 @@ predict(lme_fit, two_factor_incompl %>% filter(subject == "donor1"))
 
 ## Models using lmer, glmer, and stan_glmer
 
-The `"lmer"`, `"glmer"`, and `"stan_glmer"` engines all use the same formula syntax for fitting multilevel models. See [Section 2.1 of _Linear Mixed Models with lme4_](https://cran.r-project.org/web/packages/lme4/vignettes/lmer.pdf) for details. In this section, we'll demonstrate using the `"lmer"` engine. 
+The `"lmer"`, `"glmer"`, and `"stan_glmer"` engines all use the same formula syntax for fitting multilevel models. See [Section 2.1 of _Linear Mixed Models with lme4_](https://cran.r-project.org/package=lme4/vignettes/lmer.pdf) for details. In this section, we'll demonstrate using the `"lmer"` engine. 
 
 All of the model specification occurs in the formula; no models terms are specified via `set_engine()` (although other arguments can be passed there, as usual). To fit the same random intercept model, the syntax is: