Use four spaces when indenting paragraphs within the Description.

Title: Optimal, Fast, and Reproducible Univariate Clustering
Description: Fast, optimal, and reproducible weighted univariate
clustering by dynamic programming. Four types of problem including
univariate k-means, k-median, k-segments, and multi-channel
weighted k-means are solved with guaranteed optimality and
reproducibility. The core algorithm minimizes the sum of (weighted)
within-cluster distances using respective metrics. Its advantage
over heuristic clustering in efficiency and accuracy is pronounced
at a large number of clusters k. Weighted k-means can also process
time series to perform peak calling. Multi-channel weighted k-means
groups multiple univariate signals into k clusters. An auxiliary
function generates histograms that are adaptive to patterns in data.
This package provides a powerful set of tools for univariate data
analysis with guaranteed optimality, efficiency, and reproducibility.

clustering by dynamic programming. Four problem are solved, including
univariate k-means (Wang & Song 2011) <doi:10.32614/RJ-2011-015>
(Song & Zhong 2020) <doi:10.1093/bioinformatics/btaa613>, k-median,
k-segments, and multi-channel weighted k-means. Dynamic programming
is used to minimize the sum of (weighted) within-cluster distances
using respective metrics. Its advantage over heuristic clustering in
efficiency and accuracy is pronounced at a large number of clusters.
Weighted k-means can also process time series to perform peak
calling. Multi-channel weighted k-means groups multiple univariate
signals into k clusters. An auxiliary function generates histograms
adaptive to patterns in data. This package provides a powerful set
of tools for univariate data analysis with guaranteed optimality,

c(person("Joe", "Song", role = c("aut", "cre"),
clustering by dynamic programming. Four problems are solved, including
Multi-channel weighted k-means groups multiple univariate
efficiency, and reproducibility, useful for peak calling on temporal,