Added Hex Sticker and Website

.Rbuildignore

@@ -5,3 +5,8 @@ LICENSE.md
 codecov.yml
 ^doc$
 ^Meta$
+^_pkgdown\.yml$
+^docs$
+^pkgdown$
+^\.github$
+^README\.Rmd$

.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/github-pages-deploy-action@v4.5.0
+        with:
+          clean: false
+          branch: gh-pages
+          folder: docs

.gitignore

@@ -75,3 +75,4 @@ data-raw/
 
 # Output from vignettes
 figure/
+docs

DESCRIPTION

@@ -43,3 +43,5 @@ Suggests:
 VignetteBuilder: knitr
 Config/testthat/edition: 3
 LazyData: true
+URL: https://traitecoevo.github.io/hmde/
+BugReports: /~https://github.com/traitecoevo/austraits/issues

README.Rmd

@@ -0,0 +1,87 @@
+---
+output: github_document
+---
+
+# hmde <img src="man/figures/hmde_hex.png" align="right" alt="" width="220" />
+
+The goal of `hmde` is to implement hierarchical Bayesian longitudinal models to solve the Bayesian inverse problem of estimating differential equation parameters based on repeat measurement surveys. Estimation is done using Markov Chain Monte Carlo, implemented through
+[Stan](https://mc-stan.org/) via [RStan](https://mc-stan.org/users/interfaces/rstan), built under [R](https://cran.r-project.org/) 4.3.3. The inbuilt models are based on case studies in ecology. The hierarchical Bayesian longitudinal method was first introduced in O'Brien et al., [2024](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14463).
+
+As `hmde` is first intended for biologists, the initial set of vignettes (`hmde`, `constant-growth`, `von-bertalanffy`, and `canham`) are written aimed at an audience more interested in applications than the underlying theory. A vignette for the more mathematically interested is under development.
+
+## The Maths
+
+The general use case is to estimate a vector of parameters <span>$\boldsymbol{\theta}$</span> for a chosen differential equation
+$$f\left( Y \left( t \right), \boldsymbol{\theta} \right) = \frac{dY}{dt}$$ 
+based on the longitudinal structure
+$$Y \left( t_{j+1} \right) = Y\left( t_j \right) + \int_{t_j}^{t_{j+1}}f\left( Y \left( t \right), \boldsymbol{\theta} \right)\,dt. $$
+
+The input data are observations of the form $y_{ij}$ for individual $i$ at time $t_j$, with repeated observations coming from the same individual. We parameterise $f$ at the individual level by estimating $\boldsymbol{\theta}_i$ as the vector of parameters. We have hyper-parameters that determine the distribution of $\boldsymbol{\theta}_i$ with typical prior distribution
+$$\boldsymbol{\theta}_i \sim \log \mathcal{N}\left(\boldsymbol{\mu}_{\log\left(\boldsymbol{\theta}\right)}, \boldsymbol{\sigma}_{\log \left( \boldsymbol{\theta} \right)}\right), $$ 
+where $\boldsymbol{\mu}_{\log\left(\boldsymbol{\theta}\right)}$ and $\boldsymbol{\sigma}_{\log\left(\boldsymbol{\theta}\right)}$ are vectors of means and standard deviations. In the case of a single individual, these are chosen prior values. In the case of a multi-individual model $\boldsymbol{\mu}_{\log\left(\boldsymbol{\theta}\right)}$ and $\boldsymbol{\sigma}_{\log\left(\boldsymbol{\theta}\right)}$ have their own prior distributions and are fit to data.
+
+## Implemented Models
+
+`hmde` comes with four DEs built and ready to go, each of which has a version for a single individual and multiple individuals.
+
+### Constant Model
+
+The constant model is given by
+$$f \left( Y \left( t \right), \beta \right) = \frac{dY}{dt} = \beta,$$ 
+and is best understood as describing the average rate of change over time.
+
+### von Bertalanffy
+
+The von Bertalanffy mode is given by
+$$f \left( Y \left( t \right), \beta, Y_{max} \right) = \frac{dY}{dt} = \beta \left( Y_{max} - Y \left( t \right) \right),$$ 
+where $\beta$ is the growth rate parameter and $Y_{max}$ is the maximum value that $Y$ takes.
+
+### Canham
+
+The Canham ([Canham et
+al. 2004](https://doi.org/10.1890/1051-0761(2006)016%5B0540:NAOCTC%5D2.0.CO;2))
+model is a hump-shaped function given by
+$$f \left( Y \left( t \right), f_{max}, Y_{max}, k \right) = \frac{dY}{dt} = f_{max} \exp \Bigg( -\frac{1}{2} \bigg( \frac{ \ln \left( Y \left( t \right) / Y_{max} \right) }{k} \bigg)^2 \Bigg), $$ 
+where $f_{max}$ is the maximum growth rate, $Y_{max}$ is the $Y$-value at which that maximum occurs, and $k$ controls how narrow or wide the peak is.
+
+## 
+
+## Installation
+
+‘hmde’ is under active development. You can install the current
+developmental version of ‘hmde’ from [GitHub](/~https://github.com/) with:
+
+``` r
+# install.packages("remotes")
+remotes::install_github("traitecoevo/hmde")
+```
+
+## Quick demo
+
+Create constant growth data with measurement error:
+
+``` r
+library(hmde)
+y_obs <- seq(from=2, to=15, length.out=10) + rnorm(10, 0, 0.1)
+```
+
+Measurement error is necessary as otherwise the normal likelihood
+$$s_{ij} \sim \mathcal{N}\left( 0, \sigma_e \right)$$ 
+blows up as $\sigma_e$ approaches 0.
+
+Fit the model:
+
+``` r
+constant_fit <- hmde_model("constant_single_ind") |>
+        hmde_assign_data(n_obs = 10,                  #Integer
+                         y_obs = y_obs,               #vector length n_obs
+                         obs_index = 1:10,            #vector length n_obs
+                         time = 0:9,                  #Vector length n_obs
+                         y_0_obs = y_obs[1]           #Real
+        ) |>
+        hmde_run(chains = 1, iter = 1000, verbose = FALSE, show_messages = FALSE)
+```
+
+## Found a bug?
+
+Please submit a [GitHub issue](/~https://github.com/traitecoevo/hmde/issues) with details of the bug. A [reprex](https://reprex.tidyverse.org/) would be particularly helpful with the bug-proofing process!

README.md

@@ -1,60 +1,74 @@
 
-<!-- README.md is generated from README.Rmd. Please edit that file -->
-
-# hmde
-
-<!-- badges: start -->
-
-[![R-CMD-check](/~https://github.com/traitecoevo/hmde/actions/workflows/R-CMD-check.yaml/badge.svg)](/~https://github.com/traitecoevo/hmde/actions/workflows/R-CMD-check.yaml)
-[![Codecov test
-coverage](https://codecov.io/gh/traitecoevo/hmde/branch/master/graph/badge.svg)](https://app.codecov.io/gh/traitecoevo/hmde?branch=master)
-[![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
-<!-- badges: end -->
-
-The goal of hmde is to implement hierarchical Bayesian longitudinal models to solve the Bayesian inverse problem of estimating differential equation parameters based on repeat measurement surveys. Estimation is done using Markov Chain Monte Carlo, implemented through
-[Stan](https://mc-stan.org/) via [RStan](https://mc-stan.org/users/interfaces/rstan), built under [R](https://cran.r-project.org/) 4.3.3. The inbuilt models are based on case studies in ecology.
+# hmde <img src="man/figures/hmde_hex.png" align="right" alt="" width="220" />
+
+The goal of `hmde` is to implement hierarchical Bayesian longitudinal
+models to solve the Bayesian inverse problem of estimating differential
+equation parameters based on repeat measurement surveys. Estimation is
+done using Markov Chain Monte Carlo, implemented through
+[Stan](https://mc-stan.org/) via
+[RStan](https://mc-stan.org/users/interfaces/rstan), built under
+[R](https://cran.r-project.org/) 4.3.3. The inbuilt models are based on
+case studies in ecology. The hierarchical Bayesian longitudinal method
+was first introduced in O’Brien et al.,
+[2024](https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14463).
+
+As `hmde` is first intended for biologists, the initial set of vignettes
+(`hmde`, `constant-growth`, `von-bertalanffy`, and `canham`) are written
+aimed at an audience more interested in applications than the underlying
+theory. A vignette for the more mathematically interested is under
+development.
 
 ## The Maths
 
-The general use case is to estimate a vector of parameters $\boldsymbol{\theta}$ for a chosen differential equation
-$$f\left( Y \left( t \right), \boldsymbol{\theta} \right) = \frac{dY}{dt}$$ 
+The general use case is to estimate a vector of parameters
+$\boldsymbol{\theta}$ for a chosen differential equation
+$$f\left( Y \left( t \right), \boldsymbol{\theta} \right) = \frac{dY}{dt}$$
 based on the longitudinal structure
 $$Y \left( t_{j+1} \right) = Y\left( t_j \right) + \int_{t_j}^{t_{j+1}}f\left( Y \left( t \right), \boldsymbol{\theta} \right)\,dt. $$
 
-The input data are observations of the form $y_{ij}$ for individual $i$ at time $t_j$, with repeated observations coming from the same individual. We parameterise $f$ at the individual level by estimating $\boldsymbol{\theta}\_i$ as the vector of parameters. We have hyper-parameters that determine the distribution of $\boldsymbol{\theta}\_i$ with typical prior distribution
-$$\boldsymbol{\theta}\_i \sim \log \mathcal{N}\left(\boldsymbol{\mu}\_{\log\left(\boldsymbol{\theta}\right)}, \boldsymbol{\sigma}\_{\log \left( \boldsymbol{\theta} \right)}\right), $$ 
-where $\boldsymbol{\mu}\_{\log\left(\boldsymbol{\theta}\right)}$ and $\boldsymbol{\sigma}\_{\log\left(\boldsymbol{\theta}\right)}$ are vectors of means and standard deviations. In the case of a single individual, these are chosen prior values. In the case of a multi-individual model $\boldsymbol{\mu}\_{\log\left(\boldsymbol{\theta}\right)}$ and $\boldsymbol{\sigma}\_{\log\left(\boldsymbol{\theta}\right)}$ have their own prior distributions and are fit to data.
+The input data are observations of the form $y_{ij}$ for individual $i$
+at time $t_j$, with repeated observations coming from the same
+individual. We parameterise $f$ at the individual level by estimating
+$\boldsymbol{\theta}_i$ as the vector of parameters. We have
+hyper-parameters that determine the distribution of
+$\boldsymbol{\theta}_i$ with typical prior distribution
+$$\boldsymbol{\theta}_i \sim \log \mathcal{N}\left(\boldsymbol{\mu}_{\log\left(\boldsymbol{\theta}\right)}, \boldsymbol{\sigma}_{\log \left( \boldsymbol{\theta} \right)}\right), $$
+where $\boldsymbol{\mu}_{\log\left(\boldsymbol{\theta}\right)}$ and
+$\boldsymbol{\sigma}_{\log\left(\boldsymbol{\theta}\right)}$ are vectors
+of means and standard deviations. In the case of a single individual,
+these are chosen prior values. In the case of a multi-individual model
+$\boldsymbol{\mu}_{\log\left(\boldsymbol{\theta}\right)}$ and
+$\boldsymbol{\sigma}_{\log\left(\boldsymbol{\theta}\right)}$ have their
+own prior distributions and are fit to data.
 
 ## Implemented Models
 
-Rmot comes with four DEs built and ready to go, each of which has a version for a single individual and multiple individuals.
+`hmde` comes with four DEs built and ready to go, each of which has a
+version for a single individual and multiple individuals.
 
 ### Constant Model
 
 The constant model is given by
-$$f \left( Y \left( t \right), \beta \right) = \frac{dY}{dt} = \beta,$$ 
-and is best understood as describing the average rate of change over time.
-
-### Power law
-
-The power law model is given by
-$$f \left( Y \left( t \right), \beta_0, \beta_1, \bar{Y} \right) = \frac{dY}{dt} = \beta_0 \bigg( \frac{Y \left( t \right)}{\bar{Y}} \bigg)^{\beta_1},$$ 
-where $\beta_0>0$ is the coefficient, $\beta_1$ is the power, and $\bar{Y}$ is a user-provided parameter that centres the model in order to avoid correlation between the $\beta$ parameters.
+$$f \left( Y \left( t \right), \beta \right) = \frac{dY}{dt} = \beta,$$
+and is best understood as describing the average rate of change over
+time.
 
 ### von Bertalanffy
 
 The von Bertalanffy mode is given by
-$$f \left( Y \left( t \right), \beta, Y_{max} \right) = \frac{dY}{dt} = \beta \left( Y_{max} - Y \left( t \right) \right),$$ 
-where $\beta$ is the growth rate parameter and $Y_{max}$ is the maximum value that $Y$ takes.
+$$f \left( Y \left( t \right), \beta, Y_{max} \right) = \frac{dY}{dt} = \beta \left( Y_{max} - Y \left( t \right) \right),$$
+where $\beta$ is the growth rate parameter and $Y_{max}$ is the maximum
+value that $Y$ takes.
 
 ### Canham
 
 The Canham ([Canham et
-al. 2004](https://doi.org/10.1890/1051-0761(2006)016%5B0540:NAOCTC%5D2.0.CO;2))
+al. 2004](https://doi.org/10.1890/1051-0761(2006)016%5B0540:NAOCTC%5D2.0.CO;2))
 model is a hump-shaped function given by
-$$f \left( Y \left( t \right), f_{max}, Y_{max}, k \right) = \frac{dY}{dt} = f_{max} \exp \Bigg( -\frac{1}{2} \bigg( \frac{ \ln \left( Y \left( t \right) / Y_{max} \right) }{k} \bigg)^2 \Bigg), $$ 
-where $f_{max}$ is the maximum growth rate, $Y_{max}$ is the $Y$-value at which that maximum occurs, and $k$ controls how narrow or wide the peak is.
+$$f \left( Y \left( t \right), f_{max}, Y_{max}, k \right) = \frac{dY}{dt} = f_{max} \exp \Bigg( -\frac{1}{2} \bigg( \frac{ \ln \left( Y \left( t \right) / Y_{max} \right) }{k} \bigg)^2 \Bigg), $$
+where $f_{max}$ is the maximum growth rate, $Y_{max}$ is the $Y$-value
+at which that maximum occurs, and $k$ controls how narrow or wide the
+peak is.
 
 ## 
 
@@ -73,12 +87,13 @@ remotes::install_github("traitecoevo/hmde")
 Create constant growth data with measurement error:
 
 ``` r
+library(hmde)
 y_obs <- seq(from=2, to=15, length.out=10) + rnorm(10, 0, 0.1)
 ```
 
 Measurement error is necessary as otherwise the normal likelihood
-$$s_{ij} \sim \mathcal{N}\left( 0, \sigma_e \right)$$ 
-blows up as $\sigma_e$ approaches 0.
+$$s_{ij} \sim \mathcal{N}\left( 0, \sigma_e \right)$$ blows up as
+$\sigma_e$ approaches 0.
 
 Fit the model:
 
@@ -95,4 +110,7 @@ constant_fit <- hmde_model("constant_single_ind") |>
 
 ## Found a bug?
 
-Please submit a [GitHub issue](/~https://github.com/traitecoevo/hmde/issues) with details of the bug. A [reprex](https://reprex.tidyverse.org/) would be particularly helpful with the bug-proofing process!
+Please submit a [GitHub
+issue](/~https://github.com/traitecoevo/hmde/issues) with details of the
+bug. A [reprex](https://reprex.tidyverse.org/) would be particularly
+helpful with the bug-proofing process!

_pkgdown.yml

@@ -0,0 +1,26 @@
+url: https://traitecoevo.github.io/hmde/
+template:
+  bootstrap: 5
+  bootswatch: flatly
+
+navbar:
+  structure:
+    left:  [intro, reference, articles]
+    right: [search, github]
+  components:
+   articles:
+    text: Articles
+    menu:
+    - text: "Case Studies"
+    - text: Constant growth model
+      href: 'articles/constant-growth.html'
+    - text: von Bertalanffy model
+      href: 'articles/von-bertalanffy.html'
+    - text: Canham model
+      href: 'articles/canham.html'
+    github:
+      icon: fab fa-github fa-lg
+      href: /~https://github.com/traitecoevo/hmde/
+
+
+

inst/figures/hex.R

@@ -0,0 +1,39 @@
+library(hexSticker)
+
+measurement_data_transformed <- Tree_Size_Ests$measurement_data %>%
+  group_by(ind_id) %>%
+  mutate(
+    delta_y_obs = y_obs - lag(y_obs),
+    obs_interval = time - lag(time),
+    obs_growth_rate = delta_y_obs/obs_interval,
+    delta_y_est = y_hat - lag(y_hat),
+    est_growth_rate = delta_y_est/obs_interval
+  ) %>%
+  ungroup()
+
+#Plots of size over time for a sample of 5 individuals
+sample_ids <- sample(1:nrow(Tree_Size_Ests$individual_data), size=5)
+plot_data <- measurement_data_transformed %>%
+  filter(ind_id %in% sample_ids)
+
+plot <- ggplot(data=plot_data, aes(group = ind_id)) +
+  geom_point(aes(x = time, y=y_obs, colour = as.factor(ind_id)),
+             shape = 1) +
+  geom_line(aes(x = time, y=y_obs, colour = as.factor(ind_id)),
+            linetype = "dashed") +
+  geom_point(aes(x = time, y=y_hat, colour = as.factor(ind_id)),
+             shape = 2) +
+  geom_line(aes(x = time, y=y_hat, colour = as.factor(ind_id)),
+            linetype = "solid") +
+  labs(x="Time (years)", y="DBH (cm)", colour="Ind. ID") +
+  theme_void() +
+  theme(legend.position = "none")
+
+hexSticker::sticker(plot, package="hmde",
+                    p_color = "white",
+                    p_size=40,
+                    p_y = 1,
+                    s_x=1, s_y=1,
+                    s_width=1.35, s_height = 1.35,
+                    h_fill = "navy", h_color = "white",
+                    filename="inst/figures/hmde_hex.png")

man/figures/hex.R

@@ -0,0 +1,39 @@
+library(hexSticker)
+
+measurement_data_transformed <- Tree_Size_Ests$measurement_data %>%
+  group_by(ind_id) %>%
+  mutate(
+    delta_y_obs = y_obs - lag(y_obs),
+    obs_interval = time - lag(time),
+    obs_growth_rate = delta_y_obs/obs_interval,
+    delta_y_est = y_hat - lag(y_hat),
+    est_growth_rate = delta_y_est/obs_interval
+  ) %>%
+  ungroup()
+
+#Plots of size over time for a sample of 5 individuals
+sample_ids <- sample(1:nrow(Tree_Size_Ests$individual_data), size=5)
+plot_data <- measurement_data_transformed %>%
+  filter(ind_id %in% sample_ids)
+
+plot <- ggplot(data=plot_data, aes(group = ind_id)) +
+  geom_point(aes(x = time, y=y_obs, colour = as.factor(ind_id)),
+             shape = 1) +
+  geom_line(aes(x = time, y=y_obs, colour = as.factor(ind_id)),
+            linetype = "dashed") +
+  geom_point(aes(x = time, y=y_hat, colour = as.factor(ind_id)),
+             shape = 2) +
+  geom_line(aes(x = time, y=y_hat, colour = as.factor(ind_id)),
+            linetype = "solid") +
+  labs(x="Time (years)", y="DBH (cm)", colour="Ind. ID") +
+  theme_void() +
+  theme(legend.position = "none")
+
+hexSticker::sticker(plot, package="hmde",
+                    p_color = "white",
+                    p_size=40,
+                    p_y = 1,
+                    s_x=1, s_y=1,
+                    s_width=1.35, s_height = 1.35,
+                    h_fill = "navy", h_color = "white",
+                    filename="inst/figures/hmde_hex.png")