Performs variable importance analysis using a causal
inference approach. This is done by fitting Population
Intervention Models. The default is to use a Targeted Maximum
Likelihood Estimator (TMLE). The other available estimators are
Inverse Probability of Censoring Weighted (IPCW), Double-Robust
IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators.
Inference can be obtained from the influence curve (plug-in) or
by bootstrapping.

Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.

c(person("Agnes Martine", "Nielsen", email = "agni@dtu.dk",
role = c("aut", "cre")),person("Daniela", "Witten", role = "aut"))
Fits the Multiple Random Dot Product Graph Model and performs a test
for whether two networks come from the same distribution. Both methods are
proposed in Nielsen, A.M., Witten, D., (2018) "The Multiple Random Dot Product Graph
Model", arXiv preprint <arXiv:1811.12172> (Submitted to Journal of Computational
and Graphical Statistics).
Agnes Martine Nielsen [aut, cre],
Daniela Witten [aut]

Calculating and Visualizing ROC Curves Across Multi-Class
Classifications
c(person("Runmin", "Wei", role = c("aut", "cre"), email = "runmin@hawaii.edu"),  person("Jingye", "Wang", role = "aut", email = "jingyew@hawaii.edu"), person("Wei", "Jia", role = "ctb", email = "wjia@cc.hawaii.edu"))
Tools to solve real-world problems with multiple classes by computing the areas under ROC curve via micro-averaging and macro-averaging. The methodology is described in V. Van Asch (2013) <https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf> and Pedregosa et al. (2011) <http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html>.
Runmin Wei [aut, cre],
Jingye Wang [aut],
Wei Jia [ctb]