c(person("Martin", "Bladt", email = "martinbladt@gmail.com", role = c("aut", "cre")),
person("Jorge", "Yslas", email = "jorge.yslas1@gmail.com",
role = "aut"),
person("Alaric", "Müller", email = "muelleralaric@gmail.com",
role = "ctb")
)
Tools for phase-type distributions including the following variants:
continuous, discrete, multivariate, in-homogeneous, right-censored, and regression.
Methods for functional evaluation, simulation and estimation using the
expectation-maximization (EM) algorithm are provided for all models.
The methods of this package are based on the following references.
Asmussen, S., Nerman, O., & Olsson, M. (1996) <https://www.jstor.org/stable/4616418>,
Olsson, M. (1996) <https://www.jstor.org/stable/4616419>,
Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>,
Albrecher, H., Bladt, M., & Yslas, J. (2020) <doi:10.1111/sjos.12505>,
Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>,
Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>,
Bladt, M. (2022) <doi:10.1017/asb.2021.40>,
Bladt, M. (2022). <arXiv:2110.05179>,
Albrecher, H., Bladt, M., & Mueller, A. (2022) <arXiv:2207.01279>.
Martin Bladt [aut, cre],
Jorge Yslas [aut],
Alaric Müller [ctb]

Matrix-based Partial Least Squares estimation
c(person("Mikko", "Rönkkö", role=c("aut", "cre"),
email = "mikko.ronkko@aalto.fi"))
matrixpls is implements Partial Least Squares Path Modeling algorithm and related algorithms. The algorithm implementations aim for computational efficiency using matrix algebra and covariance data. The package is designed toward Monte Carlo simulations and includes functions to perform simple Monte Carlo simulations.
assertive, matrixcalc, plspm, simsem, boot, psych, lavaan,
MASS, RUnit, parallel

assertive, matrixcalc, psych, lavaan, MASS
plspm, simsem, RUnit, parallel, semPLS, boot

matrixpls is implements Partial Least Squares Path Modeling
algorithm and related algorithms. The algorithm implementations aim for
computational efficiency using matrix algebra and covariance data. The
package is designed toward Monte Carlo simulations and includes functions
to perform simple Monte Carlo simulations.