Convert Markdown ('.md') or R Markdown ('.Rmd') texts, R scripts, and directory structures, into mind map widgets or files ('.mm'), and vice versa. "FreeMind" mind map ('.mm') files can be opened by or imported to common mindmap software such as 'FreeMind' (<http://freemind.sourceforge.net/wiki/index.php/Main_Page>).

Extract and Analyze Median Molecule Intensity from 'citrus'
Output
Citrus is a computational technique developed for the analysis
of high dimensional cytometry data sets. This package extracts, statistically
analyzes, and visualizes marker expression from 'citrus' data. This code was used to
generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al.
“Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile
Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus,
please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download
the 'citrus' package, please see <https://github.com/nolanlab/citrus>.

This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2018). The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate.

This is a method (MinED) for mining probability distributions using deterministic sampling which is proposed by Joseph, Wang, Gu, Lv, and Tuo (2019) <DOI:10.1080/00401706.2018.1552203>. The MinED samples can be used for approximating the target distribution. They can be generated from a density function that is known only up to a proportionality constant and thus, it might find applications in Bayesian computation. Moreover, the MinED samples are generated with much fewer evaluations of the density function compared to random sampling-based methods such as MCMC and therefore, this method will be especially useful when the unnormalized posterior is expensive or time consuming to evaluate. This research is supported by a U.S. National Science Foundation grant DMS-1712642.