Provides gene signature quality control metrics in publication ready plots. Namely, enables the visualization of properties such as expression, variability, correlation, and comparison of methods of standardisation and scoring metrics.

Succinctly and correctly format statistical summaries of
    various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). 
    This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.

Provides tools to identify and visualize branches in a phylogenetic tree that are significantly responsive to some intervention, taking as primary inputs a phylogenetic tree (of class phylo) and a data frame (or matrix) of corresponding tip (OTU) labels and p-values.

The goal of SIHR is to provide inference procedures in the high-dimensional generalized linear regression setting for:
    (1) linear functionals <doi:10.48550/arXiv.1904.12891> <doi:10.48550/arXiv.2012.07133>,
    (2) conditional average treatment effects,
    (3) quadratic functionals <doi:10.48550/arXiv.1909.01503>,
    (4) inner product,
    (5) distance.

Calculate superior identification index and its extensions.
    Measure the performance of journals based on how well they could 
    identify the top papers by any index (e.g. citation indices) according to Huang & Yang. 
    (2022) <doi:10.1007/s11192-022-04372-z>. These methods could be extended to 
    evaluate other entities such as institutes, countries, etc.

Method to estimate the spatial influence scales of landscape variables on a response variable. The method is based on Chandler and Hepinstall-Cymerman (2016) Estimating the spatial scales of landscape effects on abundance, Landscape ecology, 31: 1383-1394, <doi:10.1007/s10980-016-0380-z>.

Provides a general framework to perform statistical inference of each gene pair 
        and global inference of whole-scale gene pairs in gene networks using the well known 
        Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional 
        settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). 
        Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso 
        (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso 
        (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified 
        graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM 
        estimation with false discovery rate control (FDR) using scaled Lasso or Lasso 
        (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install 'Rtools' before the 
        installation of this package.