stats, graphics, utils,glmnet, matrixcalc, mvnfast
Qianxiang Zang [aut, cre],
Chen Xu [aut],
Kelly Burkett [aut],

stats, parallel, graphics, utils, glmnet, matrixcalc, mvnfast

Outlier Detection Using Statistical and Machine Learning Methods
Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.

Simulation and Estimation of Multi-State Discrete-Time
Semi-Markov and Markov Models
Vlad Stefan Barbu, Caroline Berard, Dominique Cellier, Mathilde Sautreuil and Nicolas Vergne
Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes.
For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions.
Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and
Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>.
Estimation and simulation of discrete-time k-th order Markov chains are also considered.

Soft Maximin Estimation for Large Scale Array-Tensor Models
Efficient design matrix free procedure for solving a soft maximin problem for  large scale array-tensor structured models. Currently Lasso and SCAD penalized estimation is implemented.

Efficient design matrix free procedure for solving a soft maximin problem for  large scale array-tensor structured models, see Lund, Mogensen and Hansen (2019) <arXiv:1805.02407>. Currently Lasso and SCAD penalized estimation is implemented.