Title: R Integration for Edlib, the C/C++ Library for Exact Pairwise
Sequence Alignment using Edit (Levenshtein) Distance
Authors@R: c(person("Martin", "Šošić", email = "sosic.martin@gmail.com", role = c("aut")), person("Evan", "Biederstedt", email="evan.biederstedt@gmail.com", role=c("aut", "cre")))
Description: Bindings to edlib, a lightweight performant C/C++ library for exact pairwise sequence alignment using edit distance (Levenshtein distance). The algorithm computes the optimal alignment path, but also can be used to find only the start and/or end of the alignment path for convenience. Edlib was designed to be ultrafast and require little memory, with the capability to handle very large sequences. Three alignment methods are supported: global (Needleman-Wunsch), infix (Hybrid Wunsch), and prefix (Semi-Hybrid Wunsch). The original C/C++ library is described in "Edlib: a C/C++ library for fast, exact sequence alignment using edit distance", M. Šošić, M. Šikić, <doi:10.1093/bioinformatics/btw753>.
Suggests: testthat (>= 3.1.0), rmarkdown, knitr
Packaged: 2022-01-11 15:17:54 UTC; evanbiederstedt

Packaged: 2022-11-21 12:41:32 UTC; evanbiederstedt

Description: Implements the Euclidean distance matrix completion algorithms of Alfakih, Khandani, and Wolkowicz (1999) <doi:10.1023/A:1008655427845>, Trosset (2000) <doi:10.1023/A:1008722907820>, Fang and O'Leary (2012) <doi:10.1080/10556788.2011.643888>, and Rahman and Oldford (2017) <arXiv:1610.06599> the Sensor Network Localization Algorithm of Krislock and Wolkowicz (2010) <doi:10.1137/090759392>, and the molecular reconstruction algorithm of Alipanahi (2011).
Author: Adam Rahman and Wayne Oldford (R), Timothy Davis and Stefan Larimore (colamd & Suitesparse)
Imports: Matrix, igraph, lbfgs, truncnorm, MASS, nloptr, vegan, sdpt3r,

Title: Euclidean Distance Matrix Completion Tools
Description: Implements various general algorithms to estimate missing elements
of a Euclidean (squared) distance matrix.
Includes optimization methods based on semi-definite programming found in
a non-convex position formulation by Fang and O'Leary (2012)<doi:10.1080/10556788.2011.643888>, and
a dissimilarity parameterization formulation by Trosset (2000)<doi:10.1023/A:1008722907820>.
distances are those on the minimal spanning tree, the guided random search
algorithm will complete the matrix while preserving the minimal spanning tree following
Point configurations in specified dimensions can be determined from the completions.
Special problems such as the sensor localization problem,
as for example in Krislock and Wolkowicz (2010)<doi:10.1137/090759392>,
the geometry of a molecular structure, as for example in
Hendrickson (1995)<doi:10.1137/0805040>, can also be solved.
These and other methods are described in the thesis of Adam Rahman(2018)<https://hdl.handle.net/10012/13365>.
Authors@R: c(person(given = "Adam", family = "Rahman",
person(given = "R. Wayne", family = "Oldford",
Packaged: 2021-09-08 17:59:30 UTC; rwoldford