rebuild readme (#2)

* rebuild readme

* another miss

README.md

@@ -1,7 +1,7 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
-<!-- You'll still need to render `README.Rmd` regularly, to keep `README.md` 
-up-to-date. 
+<!-- You'll still need to render `README.Rmd` regularly, to keep `README.md`
+up-to-date.
 `devtools::build_readme()` is handy for this. -->
 
 # The Sparse Marginal Epistasis test <img src="man/figures/logo.png" align="right" height="200" alt="" />
@@ -12,8 +12,8 @@ up-to-date.
 The `sme` 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/).
+including examples and articles here: [Sparse Marginal Epistasis test
+Documentation](https://lcrawlab.github.io/sme/).
 
 ## Key Features
 
@@ -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

vignettes/sme.Rmd

@@ -16,7 +16,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-The multimodal marginal epistasis (SME) test performs a genome-wide search for
+The sparse marginal epistasis (SME) test performs a genome-wide search for
 SNPs involved in genetic interactions while conditioning on information derived
 from functional genomic data.
 
@@ -136,7 +136,7 @@ true causal SNPs (Single Nucleotide Polymorphisms) in green on the
 plot - these are the genetic variants we included in our simulation as having
 real effects.
 
-```{r manhattan_plot, fig.alt="Manhattan plot to illustrate the multimodal marginal epistasis test"}
+```{r manhattan_plot, fig.alt="Manhattan plot to illustrate the sparse marginal epistasis test"}
 sme_result$summary %>%
   ggplot(aes(
   x = index,
@@ -164,7 +164,7 @@ interactions
 The dashed line marks the true 5% PVE level we used in the simulation, allowing
 you to see how accurately the method estimated the actual effect sizes.
 
-```{r pve_plot, fig.alt="PVE plot to illustrate the multimodal marginal epistasis test"}
+```{r pve_plot, fig.alt="PVE plot to illustrate the sparse marginal epistasis test"}
 sme_result$summary %>%
   ggplot(aes(x = true_gxg_snp, y = pve, fill = true_gxg_snp)) +
   geom_boxplot() +
@@ -194,7 +194,7 @@ dashed line in the plot. This reference line helps you evaluate how accurately
 SME estimated the genetic components of trait variation.
 
 
-```{r h2_plot, fig.alt="h2 plot to illustrate the multimodal marginal epistasis test"}
+```{r h2_plot, fig.alt="h2 plot to illustrate the sparse marginal epistasis test"}
 sme_result$vc_estimate %>%
   ggplot(aes(x = component, y = vc_estimate, fill = component)) +
   geom_boxplot() +

vignettes/tutorial-memory-optimization.Rmd

@@ -72,7 +72,7 @@ alt="Sparse Marginal Epistasis test (SME) schematic pseudo-random vectors"/>
 from sharing random vectors.
 **(a)** In the randomized trace estimates we can identify reusable matrix by
 vector products. Computing the exact trace of a product of two covariance
-matrices is prohibitively computationally expensive. Instead, the multimodal
+matrices is prohibitively computationally expensive. Instead, the sparse
 marginal epistasis (SME) test approximates the traces using random vectors $z$.
 For the full MQS computation of the point estimates of the variance components,
 we see that the matrix-by-vector products of the form $Az$ with $A \in \{K, G\}$