Estimating Conditional Distributions (TempleMetrics)
Estimates conditional distributions and conditional quantiles. The versions of the methods in this package are primarily for use in multiple step procedures where the first step is to estimate a conditional distribution. In particular, there are functions for implementing distribution regression. Distribution regression provides a way to flexibly model the distribution of some outcome Y conditional on covariates X without imposing parametric assumptions on the conditional distribution but providing more structure than fully nonparametric estimation (See Foresi and Peracchi (1995) <doi:10.2307/2291056> and Chernozhukov, Fernandez-Val, and Melly (2013) <doi:10.3982/ECTA10582>).

Personalized Disease Network (PDN)
Building patient level networks for prediction of medical outcomes and draw the cluster of network. This package is based on paper Personalized disease networks for understanding and predicting cardiovascular diseases and other complex processes (See Cabrera et al. <http://…/A14957> ).

Quadratically Regularized Functional Canonical Correlation Analysis (QRFCCA)
Conduct quadratically regularized functional canonical correlation analysis. The details of the method are explained in Nan Lin, Yun Zhu, Ruzhong Fan and Momiaoxiong (2017) <DOI:10.1371/journal.pcbi.1005788>.

Connect to ‘R-hub’ (rhub)
Run ‘R CMD check’ on any of the ‘R-hub’ architectures, from the command line. The current architectures include ‘Windows’, ‘macOS’, ‘Solaris’ and various ‘Linux’ distributions.

Spatial Early Warning Signals of Ecosystem Degradation (spatialwarnings)
Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation on raster data sets. EWS are metrics derived from the observed spatial structure of an ecosystem — e.g. spatial autocorrelation — that increase before an ecosystem undergoes a non-linear transition (Kefi et al. (2014) <doi:10.1371/journal.pone.0092097>).

Collaborative Targeted Maximum Likelihood Estimation (ctmle)
Implements the general template for collaborative targeted maximum likelihood estimation. It also provides several commonly used C-TMLE instantiation, like the vanilla/scalable variable-selection C-TMLE (Ju et al. (2017) <doi:10.1177/0962280217729845>) and the glmnet-C-TMLE algorithm (Ju et al. (2017) <arXiv:1706.10029>).