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`salza` is a practical implementation of two universal algorithmic information measures on sequences based on LZ77[^1]<sup>,</sup>[^2] and relative LZ[^3]<sup>,</sup>[^4] coding.
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Its rationale are described in this [preprint](https://cloud.uvolante.org/index.php/s/bJrf25qXQKEFxmZ/download). We thank the anonymous reviewers of ISIT'19 for spotting an inconsistency in the DPI ([ISIT'19 slides](https://cloud.uvolante.org/index.php/s/7oSeZKy9GKAMKKg)). This version is an update to the one submitted to IT, reflecting the current implementation (faster and slightly more accurate) and illustrating universal causal inference.
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Its rationale are described in this [preprint](https://cloud.uvolante.org/index.php/s/bJrf25qXQKEFxmZ/download). We thank the anonymous reviewers of ISIT'19 for spotting an inconsistency in the DPI ([ISIT'19 slides](https://cloud.uvolante.org/index.php/s/7oSeZKy9GKAMKKg/download)). This version is an update to the one submitted to IT, reflecting the current implementation (faster and slightly more accurate) and illustrating universal causal inference.
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`salza` comes with built-in computation of a universal, normalized semi-distance (much in the spirit of Cilibrasi _et al._ work[^5]) and an implementation of causality inference using the (stable) PC algorithm[^6].
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In case it is needed, [`drpt`](https://forge.uvolante.org/code/drpto/wikis) will convert a (`salza`) semi-distance matrix into a true distance matrix.
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In case it is needed, [`drpt`](https://forge.uvolante.org/code/drpt/wikis) will convert a (`salza`) semi-distance matrix into a true distance matrix.
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`salza` is multithreaded[^7], too.
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