Hierarchical community detection on networks by a recursive spectral partitioning strategy, which is shown to be effective and efficient in Li, Lei, Bhattacharyya, Sarkar, Bickel, and Levina (2018) <arXiv:1810.01509>. The package also includes a data generating function for a binary tree stochastic block model, a special case of stochastic block model that admits hierarchy between communities.

By binding R functions and the 'Highmaps' <https://www.highcharts.com.cn/products/highmaps> chart library, 'hchinamap' package provides a simple way to map China and its provinces. The map of China drawn by this package contains complete Chinese territory, especially the Nine-dotted line, South Tibet, Hong Kong, Macao and Taiwan.

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>.

Computes heteroskedasticity-consistent covariance matrix
    estimators for ordinary least squares regression models. The published HC0
    through HC5m estimators implemented in the package follow White (1980)
    <doi:10.2307/1912934>, Hinkley (1977)
    <doi:10.1080/00401706.1977.10489550>, Horn et al. (1975)
    <doi:10.1080/01621459.1975.10479877>, MacKinnon and White (1985)
    <doi:10.1016/0304-4076(85)90158-7>, Cribari-Neto (2004)
    <doi:10.1016/S0167-9473(02)00366-3>, Cribari-Neto and da Silva
    (2011) <doi:10.1007/s10182-010-0141-2>, Cribari-Neto et al. (2007)
    <doi:10.1080/03610920601126589>, and Li et al. (2016)
    <doi:10.1080/00949655.2016.1198906>. The package also includes HCbeta, a
    new estimator proposed by the package authors. It provides normal Wald tests,
    confidence intervals, diagnostics, and S3 output for applied inference.