Description: Variable selection for latent class analysis for model-based clustering of multivariate categorical data. The package implements a general framework for selecting the subset of variables with relevant clustering information and discard those that are redundant and/or not informative. The variable selection method is based on the approach of Fop et al. (2017) <doi:10.1214/17-AOAS1061> and Dean and Raftery (2010) <doi:10.1007/s10463-009-0258-9>. Different algorithms are available to perform the selection: stepwise, swap-stepwise and evolutionary stochastic search. Concomitant covariates used to predict the class membership probabilities can also be included in the latent class analysis model. The selection procedure can be run in parallel on multiple cores machines.

Title: Latent Class Capture Recapture Models
Author: Francesco Bartolucci [aut, cre],
Authors@R: c(person("Francesco", "Bartolucci", role = c("aut","cre"), email = "francesco.bartolucci@unipg.it"), person("Antonio", "Forcina", role = "aut", email = "forcinarosara@gmail.com"))
Description: Estimation of latent class models with individual covariates for capture recapture data. See Forcina and Bartolucci (2021)<arxiv:2106.03811>.
Packaged: 2021-08-20 17:34:37 UTC; francescobartolucci

Packaged: 2021-09-19 10:22:45 UTC; francescobartolucci

Title: Latent Class Capture-Recapture Models
Description: Estimation of latent class models with individual covariates for capture-recapture data. See Forcina and Bartolucci (2021)<arxiv:2106.03811>.
Packaged: 2021-10-03 15:08:24 UTC; francescobartolucci

Packaged: 2021-12-04 20:17:44 UTC; francescobartolucci

Authors@R: person("Florian", "Werner", email = "florian.werner@wzw.tum.de", role = c("aut", "cre"))
Description: 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.