Near-far matching is a study design technique for preprocessing observational data to mimic a pair-randomized trial. Individuals are matched to be near on measured confounders and far on levels of an instrumental variable. Methods outlined in further detail in Rigdon, Baiocchi, and Basu (2018) <doi:10.18637/jss.v086.c05>.

Includes functions and examples to compute NEAT, the Network
    Enrichment Analysis Test described in Signorelli et al.  (2016,
    <DOI:10.1186/s12859-016-1203-6>).

Simplify the exploratory data analysis process for multiple network
             data sets with the help of hierarchical clustering, consensus 
             clustering and heatmaps. Multiple network data consists of multiple
             disjoint networks that have common variables (e.g. ego networks). 
             This package contains the necessary tools for exploring such data,
             from the data pre-processing stage to the creation of dynamic
             visualizations.

Utilities for unambiguous, neat and legible
    representation of data (date, time stamp, numbers, percentages and strings) 
    for presentation of analysis , aiming for elegance and consistency.
    The purpose of this package is to format data, that is better 
    for presentation and any automation jobs that reports numbers.

Collapse, partition, combine, fill gaps in and expand date/time ranges.

User-friendly, clear and simple statistics, primarily for
  publication in psychological science. The main functions are wrappers for
  other packages, but there are various additions as well. Every relevant step
  from data aggregation to reportable printed statistics is covered for basic
  experimental designs.

A fast negative binomial mixed model for conducting association analysis of multi-subject single-cell data. It can be used for identifying marker genes, differential expression and co-expression analyses. The model includes subject-level random effects to account for the hierarchical structure in multi-subject single-cell data. See He et al. (2021) <doi:10.1038/s42003-021-02146-6>.