Performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: <arXiv:2206.02994> <arXiv:2104.00846><arXiv:2310.12140>.

Provides a unified framework for the simultaneous testing
    of differential expression, variability, and skewness of genes using
    RNA-Seq data. The framework adopts a compositional data analysis
    approach for modelling RNA-Seq count data, applies the centered
    log-ratio transformation to obtain continuous variables, and
    uses the skew-normal distribution for statistical inference. 
    Methods are described in Li and Khang (bioRxiv preprint, 2024, version 3) 
    <doi:10.1101/2024.04.09.588804>.

Functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.

Cluster-independent method based on topology structure of gene co-expression network for identifying feature gene sets, extracting cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Without prior cell clustering, SifiNet circumvents potential inaccuracies in clustering that may influence subsequent analyses. This method is introduced in Qi Gao, Zhicheng Ji, Liuyang Wang, Kouros Owzar, Qi-Jing Li, Cliburn Chan, Jichun Xie "SifiNet: a robust and accurate method to identify feature gene sets and annotate cells" (2024) <doi:10.1093/nar/gkae307>.

Print function signatures and find overly complicated code.

Provides fundamental function support for 'SigBridgeR' and its single-cell phenotypic screening algorithm, including optional functions.