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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.

Implementation of the Horseshoe Prior

Contains functions for applying the horseshoe prior to high- dimensional linear regression, yielding the posterior mean and credible intervals, amongst other things. The key parameter tau can be equipped with a prior or estimated via maximum marginal likelihood estimation (MMLE). The main function, horseshoe, is for linear regression. In addition, there are functions specifically for the sparse normal means problem, allowing for faster computation of for example the posterior mean and posterior variance. Finally, there is a function available to perform variable selection, using either a form of thresholding, or credible intervals.

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.

Shape-Constrained Kernel Density Estimation

Implements methods for obtaining kernel density estimates
subject to a variety of shape constraints (unimodality, bimodality,
symmetry, tail monotonicity, bounds, and constraints on the number of
inflection points). Enforcing constraints can eliminate unwanted waves or
kinks in the estimate, which improves its subjective appearance and can
also improve statistical performance. The main function scdensity() is
very similar to the density() function in 'stats', allowing
shape-restricted estimates to be obtained with little effort. The
methods implemented in this package are described in Wolters and Braun
(2017)

Simulating Longitudinal Data with Causal Inference Applications

A flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.

Longitudinal Targeted Maximum Likelihood Estimation

Targeted Maximum Likelihood Estimation (TMLE) of treatment/censoring specific mean outcome or marginal structural model for point-treatment and longitudinal data.

Large Data Sets

Tools for working with vectors and data sets that are too large to keep in memory. Extends the basic functionality provided in the 'lvec' package. Provides basis statistical functionality of 'lvec' objects, such as arithmetic operations and calculating means and sums. Also implements 'data.frame'-like objects storing its data in 'lvec' objects.

An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus

Leverages the R language to automate latent variable model estimation and interpretation using 'Mplus', a powerful latent variable modeling program developed by Muthen and Muthen (< http://www.statmodel.com>). Specifically, this package provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics.

Companion to the Forthcoming Book - R you Ready?

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

Analyzing Dendrometer Data

Various functions to import, verify, process and plot high-resolution dendrometer using daily and stem-cycle approaches.