Functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Currently, the package contains data restructuring functions and functions for generating biometrically informed data for kin pairs.

The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) <doi:10.3982/ECTA18260>, that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them.

Analyzes group patterns using discourse analysis data with
    graph theory mathematics. Takes the order of which individuals talk
    and converts it to a network edge and weight list. Returns the
    density, centrality, centralization, and subgroup information for each
    group. Based on the analytical framework laid out in Chai et al.
    (2019) <doi:10.1187/cbe.18-11-0222>.

Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.

Provides several data sets for use with discrete statistical tests
  and discrete multiple testing procedures. Some of them are also available as a
  four-column version, so that each row represents a 2x2 table.

Multiple testing procedures described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" <doi:10.1214/18-EJS1441>. The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input a set of observed p-values and their discrete support under the null. A function to compute such p-values and supports for Fisher's exact tests is also provided, along with a wrapper allowing to apply discrete procedures directly from contingency tables.