thisutils

Introduction

thisutils provides reliable building blocks for research workflows: sparse-matrix conversion and top-k selection, correlations, neighborhoods and LISI scores, repeated execution with structured messages, and optional dependency checks — with explicit semantics and bounded resource use.

Installation

Install CRAN version:

install.packages("thisutils")
# or
if (!require("pak", quietly = TRUE)) {
  install.packages("pak")
}
pak::pak("thisutils")

Install development version from GitHub use pak:

if (!require("pak", quietly = TRUE)) {
  install.packages("pak")
}
pak::pak("mengxu98/thisutils")

Quick start

library(Matrix)
library(thisutils)

x <- Matrix(
  c(-3, 0, 2, -1, 4, 0),
  nrow = 3,
  sparse = TRUE,
  dimnames = list(paste0("r", 1:3), paste0("c", 1:2))
)

# Implicit zeros for ordinary matrices; stored entries only for graphs
run_sparse_topk(x, k = 2, by = "col")
run_sparse_topk_stored(x, k = 2, by = "col")

# Blockwise correlation with a bounded dense working block
sparse_cor(simulate_sparse_matrix(200, 50), threshold = 0.2, block_size = 64)

# Repeat tasks with aligned serial/parallel results and per-input seeds
parallelize_fun(
  list(first = x, second = x),
  function(mat) thisutils::sparse_cor(mat, threshold = 0.2, block_size = 64),
  cores = 2,
  backend = "psock",
  seed = 2026
)

See the function reference for the complete API, and run vignette("research-package-workflows", package = "thisutils") for a connected example installed with the package.