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 (JCGS, 2006). The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.

Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.

Provides 'R6' objects to perform parallelized hyperparameter
    optimization and cross-validation. Hyperparameter optimization can be
    performed with Bayesian optimization (via 'rBayesianOptimization'
    <https://cran.r-project.org/package=rBayesianOptimization>) and grid
    search. The optimized hyperparameters can be validated using k-fold
    cross-validation. Alternatively, hyperparameter optimization and
    validation can be performed with nested cross-validation. While
    'mlexperiments' focuses on core wrappers for machine learning
    experiments, additional learner algorithms can be supplemented by
    inheriting from the provided learner base class.