Estimation of latent class models with individual covariates for capture-recapture data. See Forcina and Bartolucci (2021)<arxiv:2106.03811>.

person("Florian", "Werner", email = "florian.werner@wzw.tum.de", role = c("aut", "cre"))
Baseline correction, normalization and linear combination fitting (LCF)
of X-ray absorption near edge structure (XANES) spectra.
The package includes data loading of .xmu files exported from 'ATHENA' (Ravel and Newville, 2005) <doi:10.1107/S0909049505012719>.
Loaded spectra can be background corrected and all standards can be fitted at once.
Two linear combination fitting functions can be used:
(1) fit_athena(): Simply fitting combinations of standards as in ATHENA,
(2) fit_float(): Fitting all standards with changing baseline correction and edge-step normalization parameters.

Data sets for package ``LMERConvenienceFunctions''
Antoine Tremblay, Department of Psychology, Dalhousie University
This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model from Tremblay & Newman (In Preparation).

This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.

Bayesian population size estimation using non parametric latent-class models.

ArrayUtils.R MCMCenv_refClass.R CR_Support.R Lcm_CR_fn.R
Lcm_CR_Strat_fn.R

A Tool for Selecting Genes Related to Survival Outcomes using
Longitudinal Gene Expression Data
Jiehuan Sun [aut, cre], Jose D. Herazo-Maya [aut], Jane-Ling Wang [aut], Naftali Kaminski [aut], and Hongyu Zhao [aut]
Longitudinal genomics data and survival outcome are common in biomedical studies. It is of great interest to select genes related to the survival outcome. LCox is a computationally efficient tool for selecting genes related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome.