rmarkdown-pdflatex-mlmodern.Rmd

---
title: "Using MLModern in R Markdown"
author: "Author Name"
fontsize: "11pt"
output:
  pdf_document:
    extra_dependencies: ["mlmodern"]
    highlight: "arrow"
---

# Sample text

For example, suppose we treat each gene as a _term_ and say each sample is a
_document_. In that case, we can use word embedding methods in NLP like
`word2vec` or `GloVe` to learn low-dimensional vector representations for genes,
given the gene expression count data for a collection of samples.

$$
\mathrm{E}\left[\left(T\left(F_{n}\right)-T(F)\right)^{r}\right]=
\mathrm{E}\left[\left(\sum_{j=1}^{k} T_{j, n} / j !\right)^{r}\right]+o\left(n^{-(r+k-1) / 2}\right)
$$

Traditional methods might assume two genes are similar if they have highly
correlated expression profiles or overlaps in labels. We assume that two
genes are similar if their **expression context** is similar,
i.e., the groups of genes that frequently express together with them are similar.

# Sample code block

```{r, eval=FALSE}
#' Classify any file into text or binary
is_text_file <- function(path, n = file.info(path)$size) {
  bytecode <- readBin(path, what = "raw", n = n)

  if (length(bytecode) == 0L) return(FALSE)

  allow <- as.raw(c(9, 10, 13, 32:255))
  block <- as.raw(c(0:6, 14:31))
  cond1 <- any(bytecode %in% allow)
  cond2 <- !any(bytecode %in% block)
  cond1 && cond2
}
```