ggca 0.4.1

Computes efficiently the correlation (Pearson, Spearman or Kendall) and the p-value (two-sided) between all the pairs from two datasets
Documentation 
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[[bench]]
harness = false
name = "benchmarks"
[dependencies.GSL]
version = "2.0.1"

[dependencies.bincode]
version = "1.3.1"

[dependencies.csv]
version = "1.1"

[dependencies.env_logger]
version = "0.9.0"

[dependencies.extsort]
version = "0.4.2"

[dependencies.fast-float]
version = "0.2"

[dependencies.itertools]
version = "0.9.0"

[dependencies.kendalls]
version = "0.2.1"

[dependencies.lazy_static]
version = "1.4.0"

[dependencies.log]
version = "0.4.14"

[dependencies.pyo3]
version = "0.15.1"

[dependencies.serde]
default-features = false
version = "1.0.14"

[dependencies.serde_derive]
default-features = false
version = "1.0.14"
[dev-dependencies.approx]
version = "0.5.0"

[dev-dependencies.criterion]
version = "0.3.5"

[features]
default = ["extension-module"]
extension-module = ["pyo3/extension-module"]

[lib]
crate-type = ["cdylib", "rlib"]
name = "ggca"

[package]
authors = ["JWare Solutions <jware.organization@gmail.com>"]
description = "Computes efficiently the correlation (Pearson, Spearman or Kendall) and the p-value (two-sided) between all the pairs from two datasets"
documentation = "https://docs.rs/ggca/"
edition = "2018"
exclude = [".*", "*.tar.gz", "*.sh"]
keywords = ["mRNA", "expression", "modulation", "correlation", "p-value"]
license = "GPL-3.0"
name = "ggca"
readme = "README.md"
repository = "https://github.com/jware-solutions/ggca"
version = "0.4.1"