Sequential Kalman filter for scalable online changepoint detection by temporally correlated data. It enables fast single and multiple change points with missing values. See the reference: Hanmo Li, Yuedong Wang, Mengyang Gu (2023), <arXiv:2310.18611>.

SKIFTI files contain brain imaging data in coordinates across Tract Based Spatial Statistics (TBSS) skeleton, which 
    represent the brain white matter intensity values. 
    'skiftiTools' provides a unified environment for reading, writing, 
    visualizing and manipulating SKIFTI-format data. It supports the "subsetting", 
    "concatenating", and using data as data.frame for R statistical functions. The SKIFTI data is structured for convenient access to the data and metadata,
    and includes support for visualizations. For more information see Merisaari et al. (2024) <doi:10.57736/87d2-0608>.

A generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.

Functions that simplify calls to the 'Skilljar' API. See 
    <https://api.skilljar.com/docs/> for documentation on the 'Skilljar' API.
    This package is not supported by 'Skilljar'.

A simple to use summary function that can be used with pipes
    and displays nicely in the console. The default summary statistics may
    be modified by the user as can the default formatting.  Support for
    data frames and vectors is included, and users can implement their own
    skim methods for specific object types as described in a vignette.
    Default summaries include support for inline spark graphs.
    Instructions for managing these on specific operating systems are
    given in the "Using skimr" vignette and the README.

Implements a Bayesian hierarchical model designed to identify skips in mobile menstrual cycle self-tracking on mobile apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) <doi:10.1093/jamia/ocab182>.