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T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation
An R wrapper around the fast T-distributed Stochastic Neighbor Embedding implementation by Van der Maaten (see < https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).
Multivariate Imputation by Chained Equations
Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm as described in Van Buuren and
Super Learner Prediction
Implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
Linear and Nonlinear Mixed Effects Models
Fit and compare Gaussian linear and nonlinear mixed-effects models.
Test Coverage for Packages
Track and report code coverage for your package and (optionally) upload the results to a coverage service like 'Codecov' < http://codecov.io> or 'Coveralls' < http://coveralls.io>. Code coverage is a measure of the amount of code being exercised by a set of tests. It is an indirect measure of test quality and completeness. This package is compatible with any testing methodology or framework and tracks coverage of both R code and compiled C/C++/FORTRAN code.
Distances and Routes on Geographical Grids
Calculate distances and routes on geographic grids.
Dynamic Documents for R
Convert R Markdown documents into a variety of formats.
Fitting Generalized Linear Models
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm.
Solving Linear Inverse Models
Functions that (1) find the minimum/maximum of a linear or quadratic function: min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(a_i*x_i) subject to equality constraints Ex=f and/or inequality constraints Gx>=h, (2) sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b, (3) solve a linear system Ax=B for the unknown x. It includes banded and tridiagonal linear systems. The package calls Fortran functions from 'LINPACK'.
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.