Solve optimization problems using an R interface to NLopt. NLopt is a 
    free/open-source library for nonlinear optimization, providing a common
    interface for a number of different free optimization routines available
    online as well as original implementations of various other algorithms.
    See <https://nlopt.readthedocs.io/en/latest/NLopt_Algorithms/> for more
    information on the available algorithms. Building from included sources 
    requires 'CMake'. On Linux and 'macOS', if a suitable system build of 
    NLopt (2.7.0 or later) is found, it is used; otherwise, it is built 
    from included sources via 'CMake'. On Windows, NLopt is obtained through 
    'rwinlib' for 'R <= 4.1.x' or grabbed from the appropriate toolchain for
    'R >= 4.2.0'.

Basic classes and methods for Natural Language Processing.

Stanford 'CoreNLP' annotation client.
  Stanford 'CoreNLP' <https://stanfordnlp.github.io/CoreNLP/index.html> integrates all 
  NLP tools from the Stanford Natural Language Processing Group, 
  including a part-of-speech (POS) tagger, a named entity recognizer (NER), 
  a parser, and a coreference resolution system, and provides model files 
  for the analysis of English. More information can be found in the README.

Methods for obtaining improved estimates of non-linear cross-validated risks are obtained using targeted minimum loss-based estimation, estimating equations, and one-step estimation (Benkeser, Petersen, van der Laan (2019), <doi:10.1080/01621459.2019.1668794>). Cross-validated area under the receiver operating characteristics curve (LeDell, Petersen, van der Laan (2015), <doi:10.1214/15-EJS1035>) and other metrics are included.

Provides computational tools for nonlinear longitudinal models, in particular the intrinsically nonlinear models, in four scenarios: (1) univariate longitudinal processes with growth factors, with or without covariates including time-invariant covariates (TICs) and time-varying covariates (TVCs); (2) multivariate longitudinal processes that facilitate the assessment of correlation or causation between multiple longitudinal variables; (3) multiple-group models for scenarios (1) and (2) to evaluate differences among manifested groups, and (4) longitudinal mixture models for scenarios (1) and (2), with an assumption that trajectories are from multiple latent classes. The methods implemented are introduced in Jin Liu (2023) <arXiv:2302.03237v2>.