A collection of datasets of human-computer interaction (HCI) experiments. 
    Each dataset is from an HCI paper, with all fields described and the original publication 
    linked. All paper authors of included data have consented to the inclusion of their data 
    in this package. The datasets include data from a range of HCI studies, such as pointing 
    tasks, user experience ratings, and steering tasks. 
    Dataset sources: 
    Bergström et al. (2022) <doi:10.1145/3490493>;
    Dalsgaard et al. (2021) <doi:10.1145/3489849.3489853>;
    Larsen et al. (2019) <doi:10.1145/3338286.3340115>;
    Lilija et al. (2019) <doi:10.1145/3290605.3300676>;
    Pohl and Murray-Smith (2013) <doi:10.1145/2470654.2481307>;
    Pohl and Mottelson (2022) <doi:10.3389/frvir.2022.719506>.

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.