try and fix debian error lhdf5 linker (#5)

* try and fix debian error lhdf5 linker * another one * Update README.md * another one * another one
lcrawlab · Jan 17, 2025 · b4bd1c5 · b4bd1c5
1 parent 355d0e7
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -15,4 +15,8 @@
 .vscode
 ^data-raw$
 ^dev$
-.github
+.github
+^src/test-genotype_masking.cpp$
+^src/test-mqs.cpp$
+^src/test-read_genotype_mask.cpp$
+^tests/testthat/test-cpp.R$
diff --git a/.gitignore b/.gitignore
@@ -48,6 +48,7 @@ po/*~
 # RStudio Connect folder
 rsconnect/
 .Rproj.user
+smer.Rproj
 sme.Rproj
 
 

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -7,10 +7,12 @@ Authors@R: c(
     person("Julian", "Stamp", email = "julian.d.stamp@gmail.com",
     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: 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>
+    role = "aut", comment = c(ORCID = "0000-0003-0178-8242")),
+    person("sriramlab", role = "cph", comment = "Author of included mailman algorithm"),
+    person("Blue Brain Project/EPFL", role = "cph", comment = "Author of included HighFive library"))
+Description: The Sparse Marginal Epistasis Test is a computationally efficient 
+    genetics method which detects statistical epistasis in complex traits;
+    see Stamp et al. (2025, <doi:10.1101/2025.01.11.632557>) for details.
 License: MIT + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)

diff --git a/R/getting_started.R b/R/getting_started.R
@@ -59,9 +59,9 @@
 #' head(getting_started$summary)
 #'
 #' @seealso
-#' \link[sme]{sme}
+#' \link[smer]{sme}
 #'
 #' @keywords datasets
 #' @source data-raw/getting_started.R
 #' @import mvMAPIT
-"getting_started"
+"getting_started"
diff --git a/README.Rmd b/README.Rmd
@@ -18,6 +18,9 @@ knitr::opts_chunk$set(
 # The Sparse Marginal Epistasis test <img src="man/figures/logo.png" align="right" height="200" alt="" />
 
 <!-- badges: start -->
+[![R-CMD-check.yaml](https://github.com/lcrawlab/sme/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/lcrawlab/sme/actions/workflows/r-cmd-check.yml)
+[![CRAN downloads](https://cranlogs.r-pkg.org/badges/grand-total/smer)](https://cranlogs.r-pkg.org/badges/grand-total/smer)
+[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/smer)](https://cran.r-project.org/package=smer)
 <!-- badges: end -->
 
 The `smer` package implements a computationally and statistically efficient method
@@ -34,7 +37,7 @@ Find the full package documentation including examples and articles here:
   detecting gene-by-gene interactions.
 -	Optimize for Memory Constraints: Highly configurable block wise processing of the
   data allows to make the most of available resources. See also
-  [How To Optimize the Memory Requirements of SME](articles/tutorial-memory-optimization.html).
+  [How To Optimize the Memory Requirements of SME](https://lcrawlab.github.io/sme/articles/tutorial-memory-optimization.html).
 -	Parallelization: Utilizes OpenMP for multi-threaded processing.
 
 ## Installation

diff --git a/README.md b/README.md
@@ -7,6 +7,11 @@ up-to-date.
 # The Sparse Marginal Epistasis test <img src="man/figures/logo.png" align="right" height="200" alt="" />
 
 <!-- badges: start -->
+
+[![R-CMD-check.yaml](https://github.com/lcrawlab/sme/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/lcrawlab/sme/actions/workflows/r-cmd-check.yml)
+[![CRAN
+downloads](https://cranlogs.r-pkg.org/badges/grand-total/smer)](https://cranlogs.r-pkg.org/badges/grand-total/smer)
+[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/smer)](https://cran.r-project.org/package=smer)
 <!-- badges: end -->
 
 The `smer` package implements a computationally and statistically
@@ -26,7 +31,7 @@ Documentation](https://lcrawlab.github.io/sme/).
 - Optimize for Memory Constraints: Highly configurable block wise
   processing of the data allows to make the most of available resources.
   See also [How To Optimize the Memory Requirements of
-  SME](articles/tutorial-memory-optimization.html).
+  SME](https://lcrawlab.github.io/sme/articles/tutorial-memory-optimization.html).
 - Parallelization: Utilizes OpenMP for multi-threaded processing.
 
 ## Installation

diff --git a/man/getting_started.Rd b/man/getting_started.Rd
diff --git a/src/Makevars b/src/Makevars
@@ -1,5 +1,5 @@
 ## Use the R_HOME indirection to support installations of multiple R version
-RHDF5_LIBS=$(shell "${R_HOME}/bin${R_ARCH_BIN}/Rscript" -e 'Rhdf5lib::pkgconfig("PKG_CXX_LIBS")') 
-PKG_LIBS = -L/usr/local/lib -L/opt/homebrew/lib $(LDFLAGS) $(SHLIB_OPENMP_CXXFLAGS) -lhdf5 $(RHDF5_LIBS)
-PKG_CPPFLAGS = -I../inst/include/ -I/usr/local/include -I/opt/homebrew/include $(CPPFLAGS) 
+RHDF5_LIBS=$(shell "${R_HOME}/bin${R_ARCH_BIN}/Rscript" -e 'Rhdf5lib::pkgconfig("PKG_CXX_LIBS")')
+PKG_LIBS = -L/usr/local/lib -L/opt/homebrew/lib $(LDFLAGS) $(SHLIB_OPENMP_CXXFLAGS) $(RHDF5_LIBS)
+PKG_CPPFLAGS = -I../inst/include/ -I/usr/local/include -I/opt/homebrew/include $(CPPFLAGS)
 PKG_CXXFLAGS = $(SHLIB_OPENMP_CXXFLAGS)
diff --git a/vignettes/smer.Rmd b/vignettes/smer.Rmd
@@ -68,7 +68,7 @@ PLINK's phenotype format.
 The `sme()` function includes parameters that let you control memory usage and
 computational resources. For detailed guidance on optimizing these settings for
 your system, please see our tutorial
-[How To Optimize the Memory Requirements of SME](articles/tutorial-memory-optimization.html).
+[How To Optimize the Memory Requirements of SME](https://lcrawlab.github.io/sme/articles/tutorial-memory-optimization.html).
 
 ### Specifying SNPs for Analysis
 
@@ -116,7 +116,7 @@ sme_result <- getting_started
 This analysis uses simulated data for demonstration purposes.
 We simulated synthetic phenotypes from 5000 synthetic genotypes with 6000 SNPs.
 If you would like to learn how to simulate data, please refer to our tutorial
-[How To Simulate Traits](articles/tutorial-simulate-traits.html).
+[How To Simulate Traits](https://lcrawlab.github.io/sme/articles/tutorial-simulate-traits.html).
 
 ### Understanding the Results
 

diff --git a/vignettes/study-erythroid-differentiation-data.Rmd b/vignettes/study-erythroid-differentiation-data.Rmd
@@ -48,7 +48,7 @@ HCT, is not informed by functional data of erythropoiesis.
 The external data sources used in this study represented genomic intervals
 of DHS regions and LD blocks. In the following we mock this data to illustrate
 how to create a mask file for `sme()`.
-See [How To Create a Mask File](articles/tutorial-create-mask-file.html) for
+See [How To Create a Mask File](https://lcrawlab.github.io/sme/articles/tutorial-create-mask-file.html) for
 more details.
 
 ```{r bim_data}

diff --git a/vignettes/tutorial-memory-optimization.Rmd b/vignettes/tutorial-memory-optimization.Rmd
@@ -138,7 +138,7 @@ kable(estimated_memory)
 Despite the computational efficiency of SME, genome-wide testing requires
 considerable resources. We recommend to analyze data in batches, and to launch
 multiple processes simultaneously on a high-performance cluster (HPC).
-In [this study](articles/study-erythroid-differentiation-data.html),
+In [this study](https://lcrawlab.github.io/sme/articles/study-erythroid-differentiation-data.html),
 we analyzed 544k SNPs genotype in 350k individuals. We launched 544 slurm jobs
 requesting 43GB memory and 6 CPUs each to analyze batches of 1000 SNPs with
 chunk sizes of 250 SNPs.