Merge pull request #20 from greta-dev/spring-clean-i18

Spring clean i18

.Rbuildignore

@@ -4,3 +4,6 @@ figure/
 ^README\.Rmd$
 ^\.github$
 ^codecov\.yml$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$

.github/workflows/pkgdown.yaml

@@ -0,0 +1,49 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown.yaml
+
+permissions: read-all
+
+jobs:
+  pkgdown:
+    runs-on: ubuntu-latest
+    # Only restrict concurrency for non-PR jobs
+    concurrency:
+      group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-pandoc@v2
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::pkgdown, local::.
+          needs: website
+
+      - name: Build site
+        run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
+        shell: Rscript {0}
+
+      - name: Deploy to GitHub pages 🚀
+        if: github.event_name != 'pull_request'
+        uses: JamesIves/[email protected]
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

.gitignore

@@ -2,3 +2,4 @@
 .Rhistory
 .RData
 .Ruserdata
+docs

DESCRIPTION

@@ -3,13 +3,27 @@ Type: Package
 Title: Generalised Additive Models in greta using mgcv
 Version: 0.1.0
 Authors@R: c(
-    person("Nick", "Golding", , "[email protected]", role = "aut",
-           comment = c(ORCID = "0000-0001-8916-5570")),
-    person("Nicholas", "Tierney", , "[email protected]", role = "aut",
-           comment = c(ORCID = "https://orcid.org/0000-0003-1460-8722")),
-    person("David L", "Miller", role = c("aut", "cre"),
-           email = "[email protected]")
-           )
+    person(
+      given = "Nick", 
+      family = "Golding", 
+      email = "[email protected]", 
+      role = "aut",
+      comment = c(ORCID = "0000-0001-8916-5570")
+      ),
+    person(
+      given = "Nicholas", 
+      family = "Tierney", 
+      email = "[email protected]", 
+      role = c("aut"),
+      comment = c(ORCID = "0000-0003-1460-8722")
+      ),
+    person(
+      given = "David L", 
+      family = "Miller", 
+      role = c("aut", "cre"),
+      email = "[email protected]"
+      )
+      )
 Description: A module for greta that lets you use mgcv's smoother functions 
     and formula syntax to define smooth terms for use in a greta model. You 
     can then define your own likelihood to complete the model, and fit it by 
@@ -20,9 +34,15 @@ Imports:
     mgcv,
     rlang,
     stats
-Depends: greta
+Depends: 
+  greta (>= 0.5.0),
+  R (>= 4.1.0)
 Suggests: 
     testthat (>= 3.0.0)
 Encoding: UTF-8
-RoxygenNote: 7.3.1
+LazyData: true
+RoxygenNote: 7.3.2
 Config/testthat/edition: 3
+URL: https://github.com/greta-dev/greta.gam, https://greta-dev.github.io/greta.gam/
+BugReports: https://github.com/greta-dev/greta.gam/issues
+Roxygen: list(markdown = TRUE)

R/jagam2greta.R

@@ -1,12 +1,21 @@
 #' Turn a jagam model definition into a greta model definition
 #'
-#' Takes a GAM defined by \code{formula} and returns the corresponding \code{greta} model via the power of \code{jagam}. Response variable is generated from dummy data and not used.
+#' Takes a GAM defined by `formula` and returns the corresponding `greta` model
+#'   via the power of `jagam`. Response variable is generated from dummy data
+#'   and not used.
 #'
 #' @inheritParams smooths
 #' @param newdata new dataset
 #'
-#' @return a \code{list} with the following elements: \code{betas} a greta array for the coefficients to be estimated (with appropriate priors applied), \code{X} design matrix for this model, \code{X_pred} prediction matrix.
-jagam2greta <- function(formula, data, newdata, sp = NULL, knots = NULL, tol = 0) {
+#' @return a `list` with the following elements: `betas` a greta array for
+#'   the coefficients to be estimated (with appropriate priors applied), `X`
+#'   design matrix for this model, `X_pred` prediction matrix.
+jagam2greta <- function(formula,
+                        data,
+                        newdata,
+                        sp = NULL,
+                        knots = NULL,
+                        tol = 0) {
   # make a dummy response to get jagam to work
   formula <- stats::update(formula, dummy ~ .)
 
@@ -31,9 +40,8 @@ jagam2greta <- function(formula, data, newdata, sp = NULL, knots = NULL, tol = 0
   X <- jags_stuff$jags.data$X
 
   # do something smart with smoothing parameters
-  if (is.null(sp)) {
-    sp <- gamma(0.05, 1 / 0.005, dim = 2 * length(jags_stuff$pregam$smooth))
-  }
+  n_smooth_params <- length(jags_stuff$pregam$smooth)
+  sp <- sp %||% gamma(0.05, 1 / 0.005, dim = 2 * n_smooth_params)
 
   # get all the penalties, form them (because of proper priorness), then
   # turn them into vcov matrices via solve()
@@ -74,5 +82,9 @@ jagam2greta <- function(formula, data, newdata, sp = NULL, knots = NULL, tol = 0
   # put all the betas together
   betas <- c(int, betas)
 
-  return(list(betas = betas, X = X, smooth_list = jags_stuff$pregam$smooth))
+  return(list(
+    betas = betas,
+    X = X,
+    smooth_list = jags_stuff$pregam$smooth
+  ))
 }

R/package.R

@@ -1,10 +1,10 @@
 #' greta.gam: generalised additive models in greta using mgcv
 #' @name greta.gam
 #'
-#' @description greta.gam is a module for greta that lets you use mgcv's smoother
-#'   functions and formula syntax to define smooth terms for use in a greta
-#'   model. You can then define your own likelihood to complete the model, and
-#'   fit it by MCMC.
+#' @description greta.gam is a module for greta that lets you use mgcv's
+#'   smoother functions and formula syntax to define smooth terms for use in a
+#'   greta model. You can then define your own likelihood to complete the
+#'   model, and fit it by MCMC.
 #'
 #' @import greta
 #' @import mgcv

R/smooths.R

@@ -2,49 +2,49 @@
 #'
 #' @name smooths
 #'
-#' @description \code{smooths} translates the right hand side of a mgcv GAM
+#' @description `smooths` translates the right hand side of a mgcv GAM
 #'   formula into a corresponding Bayesian representation of smooth terms. This
 #'   formula may include multiple combined smooths of different types, as well
 #'   as fixed effect terms and intercepts. The resulting greta array
 #'   representing the combined smooth can then be used in a greta model.
 #'
 #' @param formula a GAM formula representing the smooth terms, as in
-#'   \code{\link[mgcv:gam]{mgcv::gam}}. Only the right hand side fo the formula
+#'   [mgcv::gam()]. Only the right hand side fo the formula
 #'   will be used.
 #'
 #' @param data a data frame or list containing the covariates required by the
 #'   formula. These covariates cannot be greta arrays.
 #'
 #' @param knots an optional list containing user specified knot values to be
-#'   used for basis construction, as in \code{\link[mgcv:gam]{mgcv::gam}} These
+#'   used for basis construction, as in [mgcv::gam()] These
 #'   knots cannot be greta arrays.
 #'
 #' @param sp an optional vector of smoothing parameters, two per smooth term in
-#'   the model, in the same order as the formula. If \code{sp = NULL}, all
+#'   the model, in the same order as the formula. If `sp = NULL`, all
 #'   smoothing parameters will be learned, otherwise all smoothing parameters
 #'   must be specified by the user. The smoothing parameters may either be a
 #'   numeric vector or a greta array (which could be a variable).
 #'
 #' @param tol a non-negative scalar numerical tolerance parameter. You can try
 #'   increasing this if the model has numerical stability issues
 #'
-#' @details Only the right hand side of \code{formula} will be used to define
+#' @details Only the right hand side of `formula` will be used to define
 #'   the smooth terms. The user must complete the gam model by specifying the
 #'   link and likelihood term in greta. A warning will be issued if the formula
 #'   has a left hand side.
 #'
 #'   Note that by default, GAM formulas add an intercept term. If you have
 #'   already specified an intercept for your greta model, you can remove the
-#'   intercept from the smooth term by adding \code{-1} as a term in your
+#'   intercept from the smooth term by adding `-1` as a term in your
 #'   formula.
 #'
-#'   Like \code{\link[mgcv:jagam]{mgcv::jagam}}, \code{smooths} translates a
+#'   Like [mgcv::jagam()], `smooths` translates a
 #'   mgcv GAM formula into a Bayesian representation of the smooth terms, using
 #'   the GAM smoothing penalty matrix as a multivariate normal prior to penalise
-#'   model fitting. Unlike \code{gam}, \code{smooths} does not perform the
+#'   model fitting. Unlike `gam`, `smooths` does not perform the
 #'   integration required to penalise model fitting. The model must be fitted by
 #'   MCMC to carry out this integration - it does not make sense to do maximum
-#'   likelihood optimisation on a greta model that uses \code{smooths}.
+#'   likelihood optimisation on a greta model that uses `smooths`.
 #'
 #'
 #' @examples
@@ -72,11 +72,16 @@
 #' points(x, y, pch = 19, cex = 0.4, col = "red")
 #' }
 #' @export
-smooths <- function(formula, data = list(), knots = NULL, sp = NULL, tol = 0) {
+smooths <- function(formula,
+                    data = list(),
+                    knots = NULL,
+                    sp = NULL,
+                    tol = 0) {
   warn_if_formula_has_lhs(formula)
 
   # get all the MGCV objects for Bayesian version, converted to greta arrays
-  jg <- jagam2greta(formula,
+  jg <- jagam2greta(
+    formula,
     data = data,
     tol = tol
   )

_pkgdown.yml

@@ -0,0 +1,4 @@
+url: https://greta-dev.github.io/greta.gam/
+template:
+  bootstrap: 5
+