We provide functions to compute the nonparametric 
  maximum likelihood estimator (MLE) for 
  the bivariate distribution of (X,Y), when 
  realizations of (X,Y) cannot be observed directly. 
  To be more precise, we consider the situation 
  where we observe a set of rectangles in R^2 that are known 
  to contain the unobservable realizations of (X,Y). We
  compute the MLE based on such a set of rectangles. 
  The methods can also be used for univariate censored data (see data set
  'cosmesis'), and for 
  censored data with competing risks (see data set 'menopause'). 
  We also provide functions to visualize the observed data and the MLE.

Maximum likelihood Gaussian process modeling for
        univariate and multi-dimensional outputs with diagnostic plots 
        following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>.
        Contact the maintainer for a package version that includes 
        sensitivity analysis.

Values below the limit of detection (LOD) are a problem in several fields of science, and there are numerous approaches for replacing the missing data. We present a new mathematical solution for maximum likelihood estimation that allows us to estimate the true values of the mean and standard deviation for normal distributions and is significantly faster than previous implementations. The article with the details was submitted to JSS and can be currently seen on <https://www2.arnes.si/~tverbo/LOD/Verbovsek_Sega_2_Manuscript.pdf>.

Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (2006) <DOI: 10.1198/106186006X133069>. The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.