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        "description": "# Epigenetic Pacemaker\n## A fast conditional expectation maximization algorithm for modeling epigenetic state\n\nDNA methylation is widely used to model physiological phenotypes, such as \naging[1](https://doi.org/10.1186/gb-2013-14-10-r115) and type II diabetes[2](https://doi.org/10.1093/hmg/ddy093). \nThe epigenetic pacemaker, **EPM**, is an implementation of a fast conditional expectation maximization algorithm used to \nmodel epigenetic states associated with a phenotype of interest [3](https://doi.org/10.2217/epi-2017-0130) The EPM was first introduced by Snir et al. \n[4](https://doi.org/10.1371/journal.pcbi.1005183) as an extension of the Universal Pacemaker (UPM). The EPM can model non-linear \nepigenetic trait associations directly without transformation of the phenotype of interest[5](https://doi.org/10.1080/15592294.2019.1623634).\n\n## Installation\n\n```shell\npip3 install EpigeneticPacemaker\n``` \n\n## Documentation\n\n[epigeneticpacemaker.readthedocs.io](https://epigeneticpacemaker.readthedocs.io/en/latest/)\n\n## Citations \n\n1. [Horvath, S. DNA methylation age of human tissues and cell types. Genome Biol. 14, R115 (2013).](https://doi.org/10.1186/gb-2013-14-10-r115)\n2. [Orozco, L. D. et al. Epigenome-wide association in adipose tissue from the METSIM cohort. Hum. Mol. Genet. 0, 223495 (2018).](https://doi.org/10.1093/hmg/ddy093)\n3. [Snir, S. & Pellegrini, M. An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm. 10, 695\u2013706 (2018).](https://doi.org/10.1371/journal.pcbi.1005183)\n4. [Snir, S., vonHoldt, B. M. & Pellegrini, M. A Statistical Framework to Identify Deviation from Time Linearity in Epigenetic Aging. PLoS Comput. Biol. 12, 1\u201315 (2016).](https://doi.org/10.2217/epi-2017-0130)\n5. [Snir, S., Farrell, C. & Pellegrini, M. Human epigenetic ageing is logarithmic with time across the entire lifespan. Epigenetics (2019). doi:10.1080/15592294.2019.1623634](https://doi.org/10.1080/15592294.2019.1623634)\n\n\n",
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