Approximate Marginal Inference for Regression-Scale Models

Likelihood inference based on higher order approximations for linear nonnormal regression models.

Estimating Infection Rates from Serological Data

Translates antibody levels measured in cross-sectional population samples into estimates of the frequency with which seroconversions (infections) occur in the sampled populations. Replaces the previous `seroincidence` package.

Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios

Portfolio optimization and analysis routines and graphics.

Bayesian Estimation of an Exploratory Deterministic Input, Noisy and Gate Model

Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) .

Exploratory Reduced Reparameterized Unified Model Estimation

Perform a Bayesian estimation of the exploratory reduced reparameterized unified model (ErRUM) described by Culpepper and Chen (2018) .

Discrimination Mitigation for Machine Learning Models

Based on different statistical definitions of discrimination, several methods have been proposed to detect and mitigate social inequality in machine learning models. This package aims to provide an alternative to fairness treatment in predictive models. The ROC method implemented in this package is described by Kamiran, Karim and Zhang (2012) < https://ieeexplore.ieee.org/document/6413831/>.

Approximate Conditional Inference for Logistic and Loglinear Models

Higher order likelihood-based inference for logistic and loglinear models.

Computation and Plots of Influence Functions for Risk and Performance Measures

Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Chen and Martin (2018) < https://www.ssrn.com/abstract=3085672>, as well as in Zhang et al. (2019) < https://www.ssrn.com/abstract=3415903>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.

Bayesian Modeling via Frequentist Goodness-of-Fit

A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (< https://www.nature.com/articles/s41598-018-28130-5 >).

Choi and Hall Style Data Sharpening

Functions for use in perturbing data prior to use of nonparametric smoothers and clustering.