Fitting the full likelihood proportional hazards model and
    extracting the residuals.

A pair of functions that allow for the generation and tracking of coordinate data clouds without a time dimension,
    primarily for use in super-resolution plant micro-tubule image segmentation.

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>.

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.

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.

Efficient procedure for solving the soft maximin problem for large scale heterogeneous data, see Lund, Mogensen and Hansen (2022) <doi:10.1111/sjos.12580>. Currently Lasso and SCAD penalized estimation is implemented. Note this package subsumes and replaces the SMMA package.

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. 
    Reliability indicators such as reliability, maintainability, availability, 
    BMP-failure rate, RG-failure rate, mean time to failure and mean time to 
    repair are available as well. 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.