Multilevel Model for Multivariate Responses with Missing Values (mlmm)
To conduct Bayesian inference regression for responses with multilevel explanatory variables and missing values(Zeng ISL (2017) <doi:10.1101/153049>). Functions utilizing ‘Stan’, a software to implement posterior sampling using Hamiltonian MC and its variation Non-U-Turn algorithms are generated and provided to implement the posterior sampling of regression coefficients from the multilevel regression models. The package has two main functions to handle not-missing-at-random missing responses and left-censored with not-missing-at random responses. The purpose is to provide a similar format as the other R regression functions but using ‘Stan’ models.

Network Filtering Methods and Measures (NetworkToolbox)
Implements numerous network filtering methods (TMFG; Massara, Di Matteo, & Aste (2016) <doi:10.1093/comnet/cnw015>, MaST; Chu & Liu (1965) and Edmonds (1967) <doi:10.6028/jres.071B.032>, ECO; Fallani, Latora, & Chavez (2017) <doi:10.1371/journal.pcbi.1005305>, and ECO+MaST; Fallani, Latora, & Chavez (2017) <doi:10.1371/journal.pcbi.1005305>), and several network measures (centrality, characteristic path length, clustering coefficient, and edge replication; Rubinov and Sporns (2010) <doi:10.1016/j.neuroimage.2009.10.003>).

Create a Tidy Statistics Output File (tidystats)
Produce a data file containing the output of statistical models and assist with a workflow aimed at writing scientific papers using ‘R Markdown’. Supported statistical functions are: t.test(), cor.test(), lm(), aov(), anova(). The package is based on tidy principles (i.e., the ‘tidyverse’; Wickham, 2017).