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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)
Models for Data from Unmarked Animals
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023)
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.
Useful Tools for Structural Equation Modeling
Provides miscellaneous tools for structural equation modeling,
many of which extend the 'lavaan' package. For example, latent
interactions can be estimated using product indicators (Lin et al.,
2010,
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.
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.
Generalized Multicomponent Latent Trait Model for Diagnosis
Provides Bayesian estimation of Item Response Theory models
that decompose item difficulty into cognitive operations or rules.
Implements the Linear Logistic Test Model (LLTM; Fischer (1973)
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)