Contains a suite of shiny applications
    meant to explore linear model inference feature through simulation
    and games.

Computes the implied weights of linear regression models for estimating
     average causal effects and provides diagnostics based on these weights. These
     diagnostics rely on the analyses in Chattopadhyay and Zubizarreta (2023)
     <doi:10.1093/biomet/asac058> where
     several regression estimators are represented as weighting estimators, in connection
     to inverse probability weighting. 'lmw' provides tools to diagnose
     representativeness, balance, extrapolation, and influence for these models,
     clarifying the target population of inference. Tools are also available to
     simplify estimating treatment effects for specific target populations of interest.

We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) <doi:10.1186/s12864-019-5926-4>.

Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han SY., Liang YC., Li Y., et al. (2018) <doi:10.1093/bib/bby065>.

Identifies pathways synergisticly regulated by the interested lncRNA(long non-coding RNA) sets based on a lncRNA-mRNA(messenger RNA) interaction network. 1) The lncRNA-mRNA interaction network was built from the protein-protein interactions and the lncRNA-mRNA co-expression relationships in 28 RNA-Seq data sets. 2) The interested lncRNAs can be mapped into networks as seed nodes and a random walk strategy will be performed to evaluate the rate of each coding genes influenced by the seed lncRNAs. 3) Pathways regulated by the lncRNA set will be evaluated by a weighted Kolmogorov-Smirnov statistic as an ES Score. 4) The p value and false discovery rate value will also be calculated through a permutation analysis. 5) The running score of each pathway can be plotted and the heat map of each pathway can also be plotted if an expression profile is provided. 6) The rank and scores of the gene list of each pathway can be printed.