Functions for comparing survival curves using the max-combo test at a single timepoint or repeatedly at successive respective timepoints while controlling type I error (i.e., the group sequential setting), as published by Prior (2020) <doi:10.1177/0962280220931560>.  The max-combo test is a generalization of the weighted log-rank test, which itself is a generalization of the log-rank test, which is a commonly used statistical test for comparing survival curves, e.g., during or after a clinical trial as part of an effort to determine if a new drug or therapy is more effective at delaying undesirable outcomes than an established drug or therapy or a placebo.

Fit Maximum Entropy Optimality Theory models to data sets,
  generate the predictions made by such models for novel data, and compare the 
  fit of different models using a variety of metrics. The package is described
  in Mayer, C., Tan, A., Zuraw, K. (in press) <https://sites.socsci.uci.edu/~cjmayer/papers/cmayer_et_al_maxent_ot_accepted.pdf>.

'C++' implementation of Maximum Entropy (Maxent) species distribution
    modeling with R bindings via 'Rcpp'. Provides a high-performance
    reimplementation of the Maxent algorithm for modeling species geographic
    distributions from occurrence data and environmental variables, following
    Phillips et al. (2006) <doi:10.1016/j.ecolmodel.2005.03.026>. Supports
    linear, quadratic, product, hinge, and threshold feature transformations,
    spatial projection in raw, logistic, and cloglog scales, and model
    diagnostics including Area Under the ROC Curve (AUC), variable importance,
    response curves, and Multivariate Environmental Similarity Surfaces (MESS)
    maps.

Complex niche models show low performance in identifying
    the most important range-limiting environmental variables and in
    transferring habitat suitability to novel environmental conditions
    (Warren and Seifert, 2011 <DOI:10.1890/10-1171.1>; Warren et al.,
    2014 <DOI:10.1111/ddi.12160>). This package helps to identify the
    most important set of uncorrelated variables and to fine-tune Maxent's
    regularization multiplier. In combination, this allows to constrain
    complexity and increase performance of Maxent niche models (assessed
    by information criteria, such as AICc (Akaike, 1974
    <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver
    operating characteristic (AUC) (Fielding and Bell, 1997
    <DOI:10.1017/S0376892997000088>). Users of this package should be
    familiar with Maxent niche modelling.