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Targeted Maximum Likelihood Estimation
Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Fast Multivariate Normal and Student's t Methods
Provides computationally efficient tools related to the multivariate normal and Student's t distributions. The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API.
Interface with Google Cloud Storage API
Interact with Google Cloud Storage < https://cloud.google.com/storage/> API in R. Part of the 'cloudyr' < https://cloudyr.github.io/> project.
Miscellaneous, Analytic R Kernels
Miscellaneous functions and wrappers for development in other packages created, maintained by Jordan Mark Barbone.
Hacks for 'ggplot2'
A 'ggplot2' extension that does a variety of little helpful things. The package extends 'ggplot2' facets through customisation, by setting individual scales per panel, resizing panels and providing nested facets. Also allows multiple colour and fill scales per plot. Also hosts a smaller collection of stats, geoms and axis guides.
Safe, Multiple, Simultaneous String Substitution
Designed to enable simultaneous substitution in strings in a safe fashion. Safe means it does not rely on placeholders (which can cause errors in same length matches).
Web Application Framework for R
Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.
The Scalable Highly Adaptive Lasso
A scalable implementation of the highly adaptive lasso algorithm,
including routines for constructing sparse matrices of basis functions of the
observed data, as well as a custom implementation of Lasso regression tailored
to enhance efficiency when the matrix of predictors is composed exclusively of
indicator functions. For ease of use and increased flexibility, the Lasso
fitting routines invoke code from the 'glmnet' package by default. The highly
adaptive lasso was first formulated and described by MJ van der Laan (2017)
The Lawson-Hanson Algorithm for Non-Negative Least Squares (NNLS)
An R interface to the Lawson-Hanson implementation of an algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non-positive constraints.
Phylogenetic Linear Regression
Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree. Other tools include functions to test the adequacy of a population tree.