Faster null fit

.Rbuildignore

@@ -1,3 +1,6 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^LICENSE\.md$
+
+^_pkgdown\.yml$
+^pkgdown$

.github/workflows/R-CMD-check.yaml

@@ -23,6 +23,7 @@ jobs:
 
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
+      _R_CHECK_FORCE_SUGGESTS_: false
       RSPM: ${{ matrix.config.rspm }}
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 

.github/workflows/pkgdown.yaml

@@ -0,0 +1,48 @@
+# 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:
+    branches: [main, master]
+  release:
+    types: [published]
+  workflow_dispatch:
+
+name: pkgdown
+
+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

DESCRIPTION

@@ -1,31 +1,36 @@
 Package: radEmu
 Title: Using Relative Abundance Data to Estimate of Multiplicative Differences in Mean Absolute Abundance 
-Version: 1.1.0.0
+Version: 1.2.0.0
 Authors@R: as.person(c(
     "David Clausen <dsc24@uw.edu> [aut, cre]",
     "Amy Willis [aut]", 
     "Sarah Teichman [aut]"
   ))
 Description: A differential abundance method for the analysis of microbiome data. radEmu estimates fold-differences in the abundance of taxa across samples relative to "typical" fold-differences. Notably, it does not require pseudocounts, nor choosing a denominator taxon. 
+URL: https://github.com/statdivlab/radEmu, https://statdivlab.github.io/radEmu/
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Depends: 
     MASS,
     Matrix,
     methods,
+    magrittr,
+    rlang,
     R (>= 2.10)
 Suggests: 
     testthat (>= 3.0.0),
     numDeriv,
     phyloseq,
+    TreeSummarizedExperiment,
+    SummarizedExperiment,
     knitr,
-    magrittr,
-    dplyr, 
-    ggplot2, 
+    dplyr,
+    ggplot2,
     stringr, 
+    parallel,
     rmarkdown
 Config/testthat/edition: 3
 VignetteBuilder: knitr

NAMESPACE

@@ -1,20 +1,25 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(plot,emuFit)
 S3method(print,emuFit)
 export(emuFit)
 export(emuFit_micro)
 export(score_test)
 import(MASS)
 import(Matrix)
+importFrom(magrittr,"%>%")
 importFrom(methods,as)
+importFrom(rlang,.data)
 importFrom(stats,cov)
 importFrom(stats,median)
 importFrom(stats,model.matrix)
 importFrom(stats,optim)
 importFrom(stats,pchisq)
 importFrom(stats,qnorm)
 importFrom(stats,rbinom)
+importFrom(stats,reorder)
 importFrom(stats,rnbinom)
 importFrom(stats,rnorm)
 importFrom(stats,rpois)
+importFrom(stats,setNames)
 importFrom(stats,weighted.mean)

R/d_l_aug_dB.R

@@ -0,0 +1,41 @@
+
+d_l_aug_dB <- function(Bj,
+                       Bj_constr_no_k_constr,
+                       z,
+                       Y,
+                       X,
+                       Bk_constr_no_j_j_constr,
+                       k_constr,
+                       j,
+                       j_constr,
+                       constraint_fn,
+                       constraint_grad_fn){
+  #update parameter determined by null constraint
+  Bk_constr_j_constr <- 
+    constraint_fn(c(Bk_constr_no_j_j_constr,
+                    Bj[k_constr]))
+  #create object to store updated two columns of B (j and j_constr) in
+  Bj_j_constr <- cbind(Bj,0)
+
+  #update object with elements of B_j_constr not subject to null constraint 
+  Bj_j_constr[-k_constr,2] <- Bj_constr_no_k_constr
+
+  #update object with element of B_j_constr  subject to null constraint 
+  Bj_j_constr[k_constr,2] <- Bk_constr_j_constr 
+
+  #construct log means for Yj and Yj_constr
+  log_means <- X%*%Bj_j_constr + matrix(z,ncol = 1)%*%matrix(1,nrow = 1, ncol = 2)
+
+  #compute ll
+  deriv_j <- t(X)%*%(Y[,j,drop = FALSE] - exp(log_means[,1]))
+  deriv_j_constr <- t(X)%*%(Y[,j_constr,drop = FALSE] - exp(log_means[,2]))
+  constr_grad <- constraint_grad_fn(c(Bk_constr_no_j_j_constr,
+                                      Bj[k_constr]))
+  constr_grad <- constr_grad[length(constr_grad)]
+  deriv_j[k_constr] <- deriv_j[k_constr] + deriv_j_constr[k_constr]*constr_grad
+
+
+  #return ll
+  return(c(deriv_j,deriv_j_constr[-k_constr]))
+
+}