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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.
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")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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.