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Applied Psychometric Theory
Contains functions useful for correlation theory, meta-analysis (validity-generalization), reliability, item analysis, inter-rater reliability, and classical utility.
Easy Model-Builder Functions for 'OpenMx'
Utilities for building certain kinds of common matrices and models in the extended structural equation modeling package, 'OpenMx'.
Coupled Chain Radiative Transfer Models
A set of radiative transfer models to quantitatively describe the absorption, reflectance and transmission of solar energy in vegetation, and model remotely sensed spectral signatures of vegetation at distinct spatial scales (leaf,canopy and stand). The main principle behind ccrtm is that many radiative transfer models can form a coupled chain, basically models that feed into each other in a linked chain (from leaf, to canopy, to stand, to atmosphere). It allows the simulation of spectral datasets in the solar spectrum (400-2500nm) using leaf models as PROSPECT5, 5b, and D which can be coupled with canopy models as 'FLIM', 'SAIL' and 'SAIL2'. Currently, only a simple atmospheric model ('skyl') is implemented. Jacquemoud et al 2008 provide the most comprehensive overview of these models
Decision Curve Analysis for Model Evaluation
Diagnostic and prognostic models are typically evaluated with
measures of accuracy that do not address clinical consequences.
Decision-analytic techniques allow assessment of clinical outcomes,
but often require collection of additional information may be
cumbersome to apply to models that yield a continuous result. Decision
curve analysis is a method for evaluating and comparing prediction
models that incorporates clinical consequences, requires only the data
set on which the models are tested, and can be applied to models that
have either continuous or dichotomous results. See the following references
for details on the methods: Vickers (2006)
Perform Inference on Algorithm-Agnostic Variable Importance
Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).
Simple Bootstrap Routines
Simple bootstrap routines.
Smoothing Methods for Nonparametric Regression and Density Estimation
This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Graphical Analysis of Structural Causal Models
A port of the web-based software 'DAGitty', available at < https://dagitty.net>, for analyzing structural causal models (also known as directed acyclic graphs or DAGs). This package computes covariate adjustment sets for estimating causal effects, enumerates instrumental variables, derives testable implications (d-separation and vanishing tetrads), generates equivalent models, and includes a simple facility for data simulation.
Supervised Principal Components
Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. 'Superpc' is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies.
Predictive (Classification and Regression) Models Homologator
Methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes methods such as K-Nearest Neighbors Schliep, K. P. (2004)