By adding dependencies to the "Suggests" field of a package's
    DESCRIPTION file, and then declaring that they are needed within any
    dependent functionality, it is often possible to significantly reduce the
    number of "hard" dependencies required by a package. This package provides
    a minimal way to declare when a suggested package is needed.

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

Visualizes sulcal morphometry data derived from 'BrainVisa' <https://brainvisa.info/> including width, depth, surface area, and length. The package enables mapping of statistical group results or subject-level values onto cortical surface maps, with options to focus on all sulci or only selected regions of interest. Users can display all four measures simultaneously or restrict plots to chosen measures, creating composite, publication-quality brain visualizations in R to support the analysis and interpretation of sulcal morphology.

Estimation of mean squared prediction error of a small area predictor is provided. In particular, the recent method of Simple, Unified, Monte-Carlo Assisted approach for the mean squared prediction error estimation of small area predictor is provided. We also provide other existing methods of mean squared prediction error estimation such as jackknife method for the mixed logistic model.

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.