Provides 'ggplot2' graphics for analysing time
    series data. It aims to fit into the 'tidyverse' and grammar of
    graphics framework for handling temporal data.

An adaptation of classical region/gene-based association analysis
    techniques to the use of summary statistics (P values and effect sizes)
    and correlations between genetic variants as input. It is a tool to perform
    the most popular and efficient gene-based tests using the results of genome-wide
    association (meta-)analyses without having the original genotypes and
    phenotypes at hand.
    See for details:
    Svishcheva et al (2019) Gene-based association tests using GWAS summary
    statistics. Bioinformatics.
    Belonogova et al (2022) SumSTAAR: A flexible framework for gene-based
    association studies using GWAS summary statistics. PLOS Comp Biol.

Functions to speed up the exploratory analysis of simple
    datasets using 'dplyr'. Functions are provided to do the 
    common tasks of calculating confidence intervals.

Shrinkage estimator for polygenic risk prediction models based on summary statistics of genome-wide association studies.

Data frame summaries, cross-tabulations,
  weight-enabled frequency tables and common descriptive
  (univariate) statistics in concise tables available in a
  variety of formats (plain ASCII, Markdown and HTML). A good 
  point-of-entry for exploring data, both for experienced
  and new R users.

Module to compute cluster specific information for regression models
        with clustered errors, including leverage and influence statistics. 
        Models of type 'lm' and 'fixest'(from the 'stats' and 'fixest' packages)
        are supported. 'summclust' implements similar features as the 
        user-written 'summclust.ado' Stata module (MacKinnon, Nielsen & Webb, 2022; 
        <arXiv:2205.03288v1>).

Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>, Li et al. (2019) <doi:10.1371/journal.pone.0210645>, Wu et al. (DHS Spatial Analysis Reports No. 21, 2021), and Li et al. (2023) <arXiv:2007.05117>.