rename to SME

.gitignore

@@ -63,3 +63,5 @@ src/runner.cpp
 src/compile-runner.sh
 Rprof.out
 inst/doc
+
+pkgdown/

DESCRIPTION

@@ -1,5 +1,5 @@
-Package: sme
-Title: The Sparse Marginal Epistasis Test
+Package: smer
+Title: Sparse Marginal Epistasis Test
 Version: 0.0.1
 URL: https://github.com/lcrawlab/sme, https://lcrawlab.github.io/sme/
 BugReports: https://github.com/lcrawlab/sme/issues
@@ -8,9 +8,9 @@ Authors@R: c(
     role = c("cre", "aut"), comment = c(ORCID = "0000-0003-3014-6249")),
     person("Lorin", "Crawford", email = "lcrawford@microsoft.com",
     role = "aut", comment = c(ORCID = "0000-0003-0178-8242")))
-Description: A computationally efficient genetics method which detects
-    statistical epistasis in complex traits.
-    Stamp et al. (2025) <https://doi.org/10.1101/2025.01.11.632557>
+Description: The 'Sparse Marginal Epistasis Test' is a computationally efficient 
+    genetics method which detects statistical epistasis in complex traits.
+    Stamp et al. (2025) <doi:10.1101/2025.01.11.632557>
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
@@ -27,7 +27,6 @@ Imports:
     logging,
     mvMAPIT,
     Rcpp,
-    RcppEigen,
     tidyr
 Suggests:
     GenomicRanges,

LICENSE.md

@@ -1,6 +1,6 @@
 # MIT License
 
-Copyright (c) 2024 sme authors
+Copyright (c) 2024 smer authors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

NAMESPACE

@@ -7,12 +7,11 @@ export(simulate_traits)
 export(sme)
 export(write_hdf5_dataset)
 import(Rcpp)
-import(RcppEigen)
 import(dplyr)
 import(genio)
 import(mvMAPIT)
 importFrom(stats,pnorm)
 importFrom(tidyr,pivot_longer)
 importFrom(utils,read.delim)
 importFrom(utils,write.table)
-useDynLib(sme)
+useDynLib(smer)

NEWS.md

@@ -1,4 +1,4 @@
-# sme 0.0.1
+# smer 0.0.1
 
 * Version that was used in the publication of SME.
 

R/RcppExports.R

@@ -2,38 +2,38 @@
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
 check_openmp <- function() {
-    .Call('_sme_check_openmp', PACKAGE = 'sme')
+    .Call('_smer_check_openmp', PACKAGE = 'smer')
 }
 
 count_samples <- function(filename) {
-    .Call('_sme_count_samples', PACKAGE = 'sme', filename)
+    .Call('_smer_count_samples', PACKAGE = 'smer', filename)
 }
 
 count_fam <- function(filename) {
-    .Call('_sme_count_fam', PACKAGE = 'sme', filename)
+    .Call('_smer_count_fam', PACKAGE = 'smer', filename)
 }
 
 count_snps_bim <- function(filename) {
-    .Call('_sme_count_snps_bim', PACKAGE = 'sme', filename)
+    .Call('_smer_count_snps_bim', PACKAGE = 'smer', filename)
 }
 
 createH5File <- function(filename) {
-    invisible(.Call('_sme_createH5File', PACKAGE = 'sme', filename))
+    invisible(.Call('_smer_createH5File', PACKAGE = 'smer', filename))
 }
 
 readH5File <- function(filename, datasetName) {
-    .Call('_sme_readH5File', PACKAGE = 'sme', filename, datasetName)
+    .Call('_smer_readH5File', PACKAGE = 'smer', filename, datasetName)
 }
 
 replaceH5Dataset <- function(filename, datasetName, newData) {
-    invisible(.Call('_sme_replaceH5Dataset', PACKAGE = 'sme', filename, datasetName, newData))
+    invisible(.Call('_smer_replaceH5Dataset', PACKAGE = 'smer', filename, datasetName, newData))
 }
 
 simulate_traits_cpp <- function(plink_file, additive_heritability, gxg_heritability, additive_snps, gxg_group_1, gxg_group_2) {
-    .Call('_sme_simulate_traits_cpp', PACKAGE = 'sme', plink_file, additive_heritability, gxg_heritability, additive_snps, gxg_group_1, gxg_group_2)
+    .Call('_smer_simulate_traits_cpp', PACKAGE = 'smer', plink_file, additive_heritability, gxg_heritability, additive_snps, gxg_group_1, gxg_group_2)
 }
 
 sme_cpp <- function(plink_file, pheno_file, genotype_mask_file, n_randvecs, n_blocks, rand_seed, gxg_indices, n_threads, gxg_h5_dataset, ld_h5_dataset) {
-    .Call('_sme_sme_cpp', PACKAGE = 'sme', plink_file, pheno_file, genotype_mask_file, n_randvecs, n_blocks, rand_seed, gxg_indices, n_threads, gxg_h5_dataset, ld_h5_dataset)
+    .Call('_smer_sme_cpp', PACKAGE = 'smer', plink_file, pheno_file, genotype_mask_file, n_randvecs, n_blocks, rand_seed, gxg_indices, n_threads, gxg_h5_dataset, ld_h5_dataset)
 }
 

R/catch-routine-registration.R

@@ -2,5 +2,5 @@
 # 'tools::package_native_routine_registration_skeleton()' generates the required
 # registration info for the 'run_testthat_tests' symbol.
 (function() {
-  .Call("run_testthat_tests", FALSE, PACKAGE = "sme")
+  .Call("run_testthat_tests", FALSE, PACKAGE = "smer")
 })

R/simulate_traits.R

@@ -39,7 +39,7 @@
 #' Family ID (`FID`), Individual ID (`IID`), and the simulated trait (`TRAIT`).
 #'
 #' @examples
-#' plink_file <- gsub("\\.bed", "", system.file("testdata", "test.bed", package = "sme"))
+#' plink_file <- gsub("\\.bed", "", system.file("testdata", "test.bed", package = "smer"))
 #' out_file <- tempfile()
 #' additive_heritability <- 0.3
 #' gxg_heritability <- 0.1
@@ -59,7 +59,7 @@
 #' from_file <- read.table(out_file, header = TRUE)
 #' head(from_file)
 #'
-#' @useDynLib sme
+#' @useDynLib smer
 #' @import genio
 #' @import dplyr
 #' @importFrom utils write.table

R/sme.R

@@ -104,8 +104,8 @@
 #' **Important**: All indices in the mask file data are **zero-based**, matching the zero-based indices of the PLINK `.bim` file.
 #'
 #' @examples
-#' plink_file <- gsub("\\.bed", "", system.file("testdata", "test.bed", package="sme"))
-#' pheno_file <- system.file("testdata", "test_h2_0.5.pheno", package="sme")
+#' plink_file <- gsub("\\.bed", "", system.file("testdata", "test.bed", package="smer"))
+#' pheno_file <- system.file("testdata", "test_h2_0.5.pheno", package="smer")
 #' mask_file <- ""
 #'
 #' # Parameter inputs
@@ -130,9 +130,8 @@
 #' )
 #' head(sme_result$summary)
 #'
-#' @useDynLib sme
+#' @useDynLib smer
 #' @import Rcpp
-#' @import RcppEigen
 #' @import dplyr
 #' @importFrom stats pnorm
 #' @importFrom tidyr pivot_longer

README.Rmd

@@ -20,7 +20,7 @@ knitr::opts_chunk$set(
 <!-- badges: start -->
 <!-- badges: end -->
 
-The `sme` package implements a computationally and statistically efficient method
+The `smer` package implements a computationally and statistically efficient method
 for detecting marginal epistasis in genome-wide association studies (GWAS).
 Find the full package documentation including examples and articles here:
 [Sparse Marginal Epistasis test Documentation](https://lcrawlab.github.io/sme/).
@@ -39,7 +39,7 @@ Find the full package documentation including examples and articles here:
 
 ## Installation
 
-You can install the development version of sme from [GitHub](https://github.com/)
+You can install the development version of `smer` from [GitHub](https://github.com/)
 with:
 
 ``` r
@@ -99,7 +99,11 @@ configures the compiler flags and considers the `LDFLAGS` and `CPPFLAGS` from th
 `~/.R/Makevars` file.
 
 ## References
-
+- Stamp J, Crawford L (2025). SME: The Sparse Marginal Epistasis Test. R package 
+  version 0.0.1, https://lcrawlab.github.io/sme/, https://github.com/lcrawlab/sme.
+- Stamp J, Smith Pattillo S, Weinreich D, Crawford L (2025). Sparse modeling of 
+  interactions enables fast detection of genome-wide epistasis in biobank-scale 
+  studies. biorxiv, https://doi.org/10.1101/2025.01.11.632557
 - Stamp J, Crawford L (2024). mvMAPIT: Multivariate Genome Wide Marginal Epistasis
   Test. R package version 2.0.3, https://lcrawlab.github.io/mvMAPIT/,
   https://github.com/lcrawlab/mvMAPIT.

README.md

@@ -9,7 +9,7 @@ up-to-date.
 <!-- badges: start -->
 <!-- badges: end -->
 
-The `sme` package implements a computationally and statistically
+The `smer` package implements a computationally and statistically
 efficient method for detecting marginal epistasis in genome-wide
 association studies (GWAS). Find the full package documentation
 including examples and articles here: [Sparse Marginal Epistasis test
@@ -31,7 +31,7 @@ Documentation](https://lcrawlab.github.io/sme/).
 
 ## Installation
 
-You can install the development version of sme from
+You can install the development version of `smer` from
 [GitHub](https://github.com/) with:
 
 ``` r
@@ -65,9 +65,9 @@ file](https://github.com/lcrawlab/sme/blob/main/DESCRIPTION).
 For OS X and Linux, the OpenMP library can be installed via one of the
 (shell) commands specified below:
 
-| System                                      | Command                           |
-|:--------------------------------------------|:----------------------------------|
-| **OS X (using Homebrew)**                   | `brew install libomp`             |
+| System | Command |
+|:---|:---|
+| **OS X (using Homebrew)** | `brew install libomp` |
 | **Debian-based systems (including Ubuntu)** | `sudo apt-get install libomp-dev` |
 
 To enable openMP, it may be necessary to configure the compiler flags
@@ -92,6 +92,13 @@ file configures the compiler flags and considers the `LDFLAGS` and
 
 ## References
 
+- Stamp J, Crawford L (2025). SME: The Sparse Marginal Epistasis Test. R
+  package version 0.0.1, <https://lcrawlab.github.io/sme/>,
+  <https://github.com/lcrawlab/sme>.
+- Stamp J, Smith Pattillo S, Weinreich D, Crawford L (2025). Sparse
+  modeling of interactions enables fast detection of genome-wide
+  epistasis in biobank-scale studies. biorxiv,
+  <https://doi.org/10.1101/2025.01.11.632557>
 - Stamp J, Crawford L (2024). mvMAPIT: Multivariate Genome Wide Marginal
   Epistasis Test. R package version 2.0.3,
   <https://lcrawlab.github.io/mvMAPIT/>,

data-raw/getting_started.R

@@ -1,7 +1,7 @@
 set.seed(123)
 library(dplyr)
 library(genio)
-library(sme)
+library(smer)
 
 plink_file <- tempfile()
 n_samples <- 5000

inst/logo/logo.R

@@ -9,7 +9,7 @@ r <- hexSticker::sticker(
   s_y = 1.25,
   s_width = 1.25,
   s_height = 1.25,
-  package = "sme",
+  package = "SME",
   p_x = 1,
   p_y = 0.6,
   p_size = 25,

inst/profiling/profiling.R

@@ -1,4 +1,4 @@
-library(sme)
+library(smer)
 library(tidyr)
 
 plink_file <- "/Users/jds/data/ukbb/c12_100k-samples_020k-snps"