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.

In Switzerland, the landscape of municipalities is changing rapidly
  mainly due to mergers. The Swiss Municipal Data Merger Tool 
  automatically detects these mutations and maps municipalities over time, i.e. municipalities of an old state
  to municipalities of a new state. This functionality is helpful when working 
  with datasets that are based on different spatial references. The package's idea and use 
  case is discussed in the following article: <doi:10.1111/spsr.12487>.

Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions.