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>.

In various domains, many datasets exhibit both high variable dependency and group structures, which necessitates their simultaneous estimation. This package provides functions for two subgroup identification methods based on penalized functions, both of which utilize factor model structures to adapt to data with cross-sectional dependency. The first method is the Subgroup Identification with Latent Factor Structure Method (SILFSM) we proposed. By employing Center-Augmented Regularization and factor structures, the SILFSM effectively eliminates data dependencies while identifying subgroups within datasets. For this model, we offer optimization functions based on two different methods: Coordinate Descent and our newly developed Difference of Convex-Alternating Direction Method of Multipliers (DC-ADMM) algorithms; the latter can be applied to cases where the distance function in Center-Augmented Regularization takes L1 and L2 forms. The other method is the Factor-Adjusted Pairwise Fusion Penalty (FA-PFP) model, which incorporates factor augmentation into the Pairwise Fusion Penalty (PFP) developed by Ma, S. and Huang, J. (2017) <doi:10.1080/01621459.2016.1148039>. Additionally, we provide a function for the Standard CAR (S-CAR) method, which does not consider the dependency and is for comparative analysis with other approaches. Furthermore, functions based on the Bayesian Information Criterion (BIC) of the SILFSM and the FA-PFP method are also included in 'SILFS' for selecting tuning parameters. For more details of Subgroup Identification with Latent Factor Structure Method, please refer to He et al. (2024) <doi:10.48550/arXiv.2407.00882>.