Synthesize Data Based on Empirical Quantile Functions and Rank Order Matching

Data is synthesized using a combination of inverse transform sampling using the empirical quantile functions for each variable, and then copying the rank order structure from the original dataset. The syntesizer method has a tunable parameter allowing to gradually move from realistic and possibly unsafe synthetic data to decorrelated data of less utility.

Adjusted Limited Dependent Variable Mixture Models

The goal of the package 'aldvmm' is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package 'aldvmm' uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) using normal component distributions and a multinomial logit model of probabilities of component membership.

Geological Time Scale Plotting

Functionality for adding the geological timescale to bivariate plots.

Authenticate and Create Google APIs

Create R functions that interact with OAuth2 Google APIs < https://developers.google.com/apis-explorer/> easily, with auto-refresh and Shiny compatibility.

Core Methods and Classes Used by 'aroma.*' Packages Part of the Aroma Framework

Core methods and classes used by higher-level 'aroma.*' packages part of the Aroma Project, e.g. 'aroma.affymetrix' and 'aroma.cn'.

Conditional Visualization for Statistical Models

Exploring fitted models by interactively taking 2-D and 3-D sections in data space.

Fit, Simulate and Diagnose Exponential-Family Models for Networks

An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGMs). 'ergm' is a part of the Statnet suite of packages for network analysis. See Hunter, Handcock, Butts, Goodreau, and Morris (2008) and Krivitsky, Hunter, Morris, and Klumb (2023) .

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) < https://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>. See the scdensity() help for for full citations.

FHWA TPM Score Calculation Functions

Contains functions for calculating the Federal Highway Administration (FHWA) Transportation Performance Management (TPM) performance measures. Currently, the package provides methods for the System Reliability and Freight (PM3) performance measures calculated from travel time data provided by The National Performance Management Research Data Set (NPMRDS), including Level of Travel Time Reliability (LOTTR), Truck Travel Time Reliability (TTTR), and Peak Hour Excessive Delay (PHED) metric scores for calculating statewide reliability performance measures. Implements < https://www.fhwa.dot.gov/tpm/guidance/pm3_hpms.pdf>.

R Interface with Google Compute Engine

Interact with the 'Google Compute Engine' API in R. Lets you create, start and stop instances in the 'Google Cloud'. Support for preconfigured instances, with templates for common R needs.