Joint analysis and imputation of incomplete data in the Bayesian
    framework, using (generalized) linear (mixed) models and extensions there of,
    survival models, or joint models for longitudinal and survival data, as
    described in Erler, Rizopoulos and Lesaffre (2021) <doi:10.18637/jss.v100.i20>.
    Incomplete covariates, if present, are automatically imputed.
    The package performs some preprocessing of the data and creates a 'JAGS'
    model, which will then automatically be passed to 'JAGS' 
    <https://mcmc-jags.sourceforge.io/> with the help of 
    the package 'rjags'.

A small package containing functions to perform a joint calibration of totals and quantiles. The calibration for totals is based on Deville and Särndal (1992) <doi:10.1080/01621459.1992.10475217>, the calibration for quantiles is based on Harms and Duchesne (2006) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20060019255>. The package uses standard calibration via the 'survey', 'sampling' or 'laeken' packages. In addition, entropy balancing via the 'ebal' package and empirical likelihood based on codes from Wu (2005) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2005002/article/9051-eng.pdf> can be used. See the paper by Beręsewicz and Szymkowiak (2023) for details <arXiv:2308.13281>.

Tools for competing risks trials that allow simultaneous inference on
    recovery and mortality endpoints. Provides data preparation helpers, standard
    cumulative incidence estimators (restricted mean time gained/lost), and severity
    weighted extensions that integrate longitudinal ordinal outcomes to summarise
    treatment benefit. Methods follow Wen, Hu, and Wang (2023) Biometrics 79(3):1635-1645
    <doi:10.1111/biom.13752>.

Different algorithms to perform approximate joint diagonalization
	of a finite set of square matrices. Depending on the algorithm,
	orthogonal or non-orthogonal diagonalizer is found. These algorithms
	are particularly useful in the context of blind source separation. 
	Original publications of the algorithms can be found in 
	Ziehe et al. (2004), Pham and Cardoso (2001) <doi:10.1109/78.942614>, 
	Souloumiac (2009) <doi:10.1109/TSP.2009.2016997>, 
	Vollgraff and Obermayer <doi:10.1109/TSP.2006.877673>. An example of 
	application in the context of Brain-Computer Interfaces EEG denoising
	can be found in Gouy-Pailler et al (2010) <doi:10.1109/TBME.2009.2032162>.