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.

Advice on R Package Building

Give advice about good practices when building R packages. Advice includes functions and syntax to avoid, package structure, code complexity, code formatting, etc.

Process GPX Files into R Data Structures

Process open standard GPX files into data.frames for further use and analysis in R.

Generate New Multivariate Normal Samples from Permutations

Given a vector of multivariate normal data, a matrix of covariates and the data covariance matrix, generate new multivariate normal samples that have the same covariance matrix based on permutations of the transformed data residuals.

Companion to the Forthcoming Book - R you Ready?

Package contains some data and functions that are used in my forthcoming "R you ready?" book.

Mark-Recapture Distance Sampling

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.

Download Data from the NISRA Data Portal

Download data from the Northern Ireland Statistics and Research Agency (NISRA) data portal, accessed at < https://data.nisra.gov.uk>. NISRA is a government agency and the principal source of official statistics and social research on Northern Ireland.

General Purpose 'GraphQL' Client

A 'GraphQL' client, with an R6 interface for initializing a connection to a 'GraphQL' instance, and methods for constructing queries, including fragments and parameterized queries. Queries are checked with the 'libgraphqlparser' C++ parser via the 'graphql' package.

Dynamic Function-Oriented 'Make'-Like Declarative Pipelines

Pipeline tools coordinate the pieces of computationally demanding analysis projects. The 'targets' package is a 'Make'-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise. The methodology in this package borrows from GNU 'Make' (2015, ISBN:978-9881443519) and 'drake' (2018, ).

General utilities, workspace organization, code and doc editing, live package maintenance, etc

Hierarchical workspace tree, code editing and backup, easy package prep, editing of packages while loaded, per-object lazy-loading, easy documentation, macro functions, and miscellaneous utilities. Needed by various packages including debug, offarray, and kinference.