Small-Area-Estimation Unit/Area Models and Methods for Estimation in R

Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) , Li et al. (2019) , Wu et al. (DHS Spatial Analysis Reports No. 21, 2021), and Li et al. (2023) .

Convert Statistical Objects into Tidy Tibbles

Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

Generalized Additive Extreme Value Models for Location, Scale and Shape

Fits generalized additive models for the location, scale and shape parameters of a generalized extreme value response distribution. The methodology is based on Rigby, R.A. and Stasinopoulos, D.M. (2005), and implemented using functions from the 'gamlss' package .

Modelling of Extreme Values

Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes (Dombry, Engelke and Oesting, 2016, , R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, ) and Extremal Student (Thibaud and Opitz, 2015, ). Threshold selection methods, including Wadsworth (2016) , and Northrop and Coleman (2014) . Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) .

Forecasting and Early Outbreak Detection for Sparse Count Data

Functions for fitting, forecasting, and early detection of outbreaks in sparse surveillance count time series. Supports negative binomial (NB), self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates can be included in the regression component and/or the zero-modified components. Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with utilities for prediction and diagnostics.

Read Text Documents into R

A small collection of convenience tools for reading text documents into R.

Total, Between-, and Within-Cluster Spearman Rank Correlations for Clustered Data

Estimates the total, between-, and within-cluster Spearman rank correlations for continuous and ordinal clustered data. See Tu et al. (2024) for details.

Bayesian Projection of Life Expectancy

Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model . Subnational projections are also supported.

Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software

An interface to the 'mclust' package to easily carry out latent profile analysis ("LPA"). Provides functionality to estimate commonly-specified models. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Also has functions to interface to the commercial 'MPlus' software via the 'MplusAutomation' package.

Compute and Visualize Wildfire Exposure

Methods for computing and visualizing wildfire ignition exposure and directional vulnerability that are published in a series of scientific publications are automated by the functions in this package. See Beverly et al. (2010) , Beverly et al. (2021) , and Beverly and Forbes (2023) for background and methodology.