Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.

Provides estimators of the mode of univariate
    data or univariate distributions.

Given independent and identically distributed observations X(1), ..., X(n) from a density f,
 provides five methods to perform a multiscale analysis about f as well as the necessary critical
 values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements
 for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
 any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
 I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
 correction term Gamma in either method offers another two approaches which are more powerful on small scales and
 less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
 compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
 equivalent to the first and was introduced in Rufibach and Walther (2010).

Model-based trees for subgroup analyses in clinical trials and
  model-based forests for the estimation and prediction of personalised
  treatment effects (personalised models). Currently partitioning of linear
  models, lm(), generalised linear models, glm(), and Weibull models,
  survreg(), is supported.  Advanced plotting functionality is supported for
  the trees and a test for parameter heterogeneity is provided for the
  personalised models. For details on model-based trees for subgroup analyses
  see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for
  details on model-based forests for estimation of individual treatment effects
  see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.