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.

Provides functions for tabulating and summarizing continuous, ordinal, 
    and categorical variables in data frames. The package was designed to streamline 
    exploratory data analysis and simplify the creation of summary tables for reports 
    and other purposes.

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) <doi:10.48550/arXiv.2007.05117>.

Provides tools to simulate multi-omics datasets with predefined signal structures. The generated data can be used for testing, validating, and benchmarking integrative analysis methods such as factor models and clustering approaches. This version includes enhanced signal customization, visualization tools (scatter, histogram, 3D), MOFA-based analysis pipelines, PowerPoint export, and statistical profiling of datasets. Designed for both method development and teaching, SUMO supports real and synthetic data pipelines with interpretable outputs. Tini, Giulia, et al (2019) <doi:10.1093/bib/bbx167>.