Multivariate VAM Fitting

Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) and Broatch and Lohr (2012) , with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) , is used for the estimation of this joint generalized linear mixed model. The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) . This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.

User Interface for Collecting and Analysing Social Networks

A 'Shiny' application for the interactive visualisation and analysis of networks that also provides a web interface for collecting social media data using 'vosonSML'.

Inference for Linear Models with Nuisance Parameters

Efficient Frequentist profiling and Bayesian marginalization of parameters for which the conditional likelihood is that of a multivariate linear regression model. Arbitrary inter-observation error correlations are supported, with optimized calculations provided for independent-heteroskedastic and stationary dependence structures.

Model Evaluation and Analysis

Analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) .

Hive Plots of R Package Function Calls

Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers). Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank. FuncMap should be useful for comparing packages at a high level for their overall design. Plus, it's just plain fun. The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package. Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots.

R to Symbolic Data Analysis

Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.

Build Tables for Publication

Functions for building customized ready-to-export tables for publication.

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.

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) .

Predict Fish Hatch and Emergence Timing

Predict hatch and emergence timing for a wide range of wild fishes using the effective value framework (Sparks et al., (2019) ). 'hatchR' offers users access to established phenological models and the flexibility to incorporate custom parameterizations using external datasets.