A companion package to
  'cmstatr' <https://cran.r-project.org/package=cmstatr>. 'cmstatr' contains
  statistical methods that are published in the Composite Materials Handbook,
  Volume 1 (2012, ISBN: 978-0-7680-7811-4), while 'cmstatrExt' contains
  statistical methods that are not included in that handbook.

Conditional moments test, as proposed by Newey (1985) <doi:10.2307/1911011 > and Tauchen (1985) <doi:10.1016/0304-4076(85)90149-6>, useful to detect specification violations for models estimated by maximum likelihood. Methods for probit and tobit models are provided.

Provides R bindings for selected components of the
    Computational Morphometry Toolkit ('CMTK') for image registration and
    point transformation. A subset of the 'C++' source code required for point
    transforms is bundled with 'cmtkr'. This allows direct calls into the 'CMTK'
    library, avoiding command-line invocations and providing
    order-of-magnitude speed improvements. Additional 'CMTK' functionality may
    be wrapped in future releases. 'CMTK' is described in Rohlfing T and
    Maurer CR (2003) <doi:10.1109/titb.2003.808506>.

Various utilities for the complex multivariate Gaussian distribution and complex Gaussian processes.

Provides comprehensive functionalities for causal modeling with Coincidence Analysis (CNA), which is a configurational comparative method of causal data analysis that was first introduced in Baumgartner (2009) <doi:10.1177/0049124109339369>, and generalized in Baumgartner & Ambuehl (2020) <doi:10.1017/psrm.2018.45>. CNA is designed to recover INUS-causation from data, which is particularly relevant for analyzing processes featuring conjunctural causation (component causation) and equifinality (alternative causation). CNA is currently the only method for INUS-discovery that allows for multiple effects (outcomes/endogenous factors), meaning it can analyze common-cause and causal chain structures. Moreover, as of version 4.0, it is the only method of its kind that provides measures for model evaluation and selection that are custom-made for the problem of INUS-discovery.