Univariate agglomerative hierarchical clustering with a comprehensive list of choices of a linkage function in O(n*log n) time. The better algorithmic time complexity is paired with an efficient 'C++' implementation.

Facilitates hierarchical clustering analysis with functions
    to read data in 'txt', 'xlsx', and 'xls' formats, apply normalization
    techniques to the dataset, perform hierarchical clustering and
    construct scatter plot from principal component analysis to evaluate
    the groups obtained.

Implements hierarchical clustering methods (single linkage, complete linkage, average linkage, and centroid linkage) 
    with stepwise printing and dendrograms for didactic purposes.

Software for performing the reduction, exploratory and model selection phases of the procedure proposed by Cox, D.R. and Battey, H.S. (2017) <doi:10.1073/pnas.1703764114> for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood.

Implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level
    splitting and within-cluster subsampling to accommodate dependence, and
    inverse-probability weighting to correct distribution shift induced by missingness.
    Conditional densities are estimated by inverting fitted conditional quantiles
    (linear quantile regression or quantile regression forests), and p-values are
    aggregated across resampling and splitting steps using the Cauchy combination test.

This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. 
    We implement an effective solution to recover the above hidden compact representation under the likelihood framework. 
    Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.