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bit_vector

Fast and highly tuned bit vector implementation including space efficient rank and select support having only 3.51% space overhead.

bitvector

BitVector

bitvector2

Simple succinct bitvector and rank implementation in go

bwt

Burrows-Wheeler Transform and FM-index in golang

bwt1

Burrows-Wheeler Transform

bwtw

Burrows-Wheeler Transform and FM-index in golang

cda-tries

Compressed double-array tries for static string dictionaries

fast_succinct_trie

String map implementation through Fast Succinct Trie

fenwick

List data structure supporting prefix sums

fibvec

Go package that use fibonacci coding to store unsigned integers

fixvec

A Go library for a vector representation of values using fixed bits

fm-index

FM-index

go-bloomindex

Bloom-filter based search index

go-loudstrie

LOUDS(Level-Order Unary Degree Sequence) Trie implementation for Go

go-popcount

PopCount implementation for Go. Using hardware POPCNT instruction if available it.

go-sbvector

Succinct Bit Vector implementation for Go.

go-sbvector1

Succinct Bit Vector implementation for Go.

go-succinct-data-structure-trie

Succinct Data Structure of Trie, written in Go

go-tsz

Time series compression algorithm from Facebook's Gorilla paper

go-waveletmatrix

The implementation of Wavelet-Matrix in Go.

learned-compressed-rank-select-talg22

🧪 Code to reproduce the experiments in the TALG22 paper: A Learned Approach to Design Compressed Rank/Select Data Structures

partialsum

A Go library for succinct partial sums data structure

rankselect

Space-Efficient, High-Performance Rank & Select Structures on Uncompressed Bit Sequences

rsdic

A Go library for space-efficient rank/select operations for both sparse and dense bit arrays.

sdarray

sparse dense array

surf

First Practical and General-purpose Range Filter

vecstring

A Go library for a vector representation of strings.

wavelettree

A Go package for myriad array operations using wavelet trees

wavelettree1

Wavelet Tree

wltree

Go library of Wavelet Tree that supports Rank and Select.

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