An implementation of the blocking algorithm KLSH in Steorts, Ventura, Sadinle, Fienberg (2014) <DOI:10.1007/978-3-319-11257-2_20>, which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette.

Used to create dynamic, interactive 'D3.js' 
  based parallel coordinates and principal component plots in 'R'.
  The plots make visualizing k-means or other clusters simple and informative.

Computes various confidence intervals for the Kaplan-Meier
        estimator, namely: Peto's CI, Rothman CI, CI's based on
        Greenwood's variance, Thomas and Grunkemeier CI and the
        simultaneous confidence bands by Nair and Hall and Wellner.

Implements k-means like blockmodeling of one-mode and linked networks as presented in Žiberna (2020) <doi:10.1016/j.socnet.2019.10.006>. The development of this package is financially supported by the Slovenian Research Agency (<https://www.arrs.si/>) within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

Given constraints for right censored data, we use a recursive computational algorithm to calculate the the "constrained" Kaplan-Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data and accelerated failure time model with given coefficients. EM algorithm from emplik package is used to get the initial value. The properties and performance of the EM algorithm is discussed in Mai Zhou and Yifan Yang (2015)<doi: 10.1007/s00180-015-0567-9> and Mai Zhou and Yifan Yang (2017) <doi: 10.1002/wics.1400>. More applications could be found in Mai Zhou (2015) <doi: 10.1201/b18598>.

Implementations of the kernel measure of multi-sample dissimilarity (KMD) between
    several samples using K-nearest neighbor graphs and minimum spanning trees. The KMD 
    measures the dissimilarity between multiple samples, based on the observations from them.
    It converges to the population quantity (depending on the kernel) which is between 0 and 1.
    A small value indicates the multiple samples are from the same distribution, and a large value
    indicates the corresponding distributions are different. The population quantity is 0 if and only
    if all distributions are the same, and 1 if and only if all distributions are mutually singular.
    The package also implements the tests based on KMD for H0: the M distributions are equal
    against H1: not all the distributions are equal. Both permutation test and asymptotic test are
    available. These tests are consistent against all alternatives where at least two samples have
    different distributions. For more details on KMD and the associated tests, see Huang, Z. and
    B. Sen (2024) <doi:10.1080/01621459.2023.2298036>.