Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 504 packages in 0.03 seconds

mrds — by Laura Marshall, a year ago

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.

horseshoe — by Stephanie van der Pas, 3 years ago

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.

nnls — by Katharine Mullen, 7 years ago

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.

scdensity — by Mark A. Wolters, a year ago

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) , Wolters (2012) , and Hall and Huang (2002) < http://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>. See the scdensity() help for for full citations.

simcausal — by Oleg Sofrygin, 5 months ago

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.

ltmle — by Joshua Schwab, 10 months ago

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.

ldat — by Jan van der Laan, a year ago

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.

MplusAutomation — by Michael Hallquist, 7 months ago

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.

ryouready — by Mark Heckmann, 4 years ago

Companion to the Forthcoming Book - R you Ready?

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

dendrometeR — by Ernst van der Maaten, 3 years ago

Analyzing Dendrometer Data

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