Precision Profile Weighted Deming Regression

Weighted Deming regression, also known as 'errors-in-variable' regression, is applied with suitable weights. Weights are modeled via a precision profile; thus the methods implemented here are referred to as precision profile weighted Deming (PWD) regression. The package covers two settings – one where the precision profiles are known either from external studies or from adequate replication of the X and Y readings, and one in which there is a plausible functional form for the precision profiles but the exact (unknown) function must be estimated from the (generally singlicate) readings. The function set includes tools for: estimated standard errors (via jackknifing); standardized-residual analysis function with regression diagnostic tools for normality, linearity and constant variance; and an outlier analysis identifying significant outliers for closer investigation. The following reference provides further information on mathematical derivations and applications. Hawkins, D.M., and J.J. Kraker (2026). 'Precision Profile Weighted Deming Regression for Methods Comparison'. The Journal of Applied Laboratory Medicine 11, 379-392 .

Inferring Differentially Expressed Genes using Generalized Linear Mixed Models

Genes that are differentially expressed between two or more experimental conditions can be detected in RNA-Seq. A high biological variability may impact the discovery of these genes once it may be divergent between the fixed effects. However, this variability can be covered by the random effects. 'DEGRE' was designed to identify the differentially expressed genes considering fixed and random effects on individuals. These effects are identified earlier in the experimental design matrix. 'DEGRE' has the implementation of preprocessing procedures to clean the near zero gene reads in the count matrix, normalize by 'RLE' published in the 'DESeq2' package, 'Love et al. (2014)' and it fits a regression for each gene using the Generalized Linear Mixed Model with the negative binomial distribution, followed by a Wald test to assess the regression coefficients.

Nonparametric Methods for Cognitive Diagnosis

An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.

Downloading, Reading and Analyzing POF Microdata - Package in Development

Provides tools for downloading, reading and analyzing the POF, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website < https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.

Robust Garch(1,1) Model

A method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) .

Analysis Functions of Respiratory Data

Provides functions for the complete analysis of respiratory data. Consists of a set of functions that allow to preprocessing respiratory data, calculate both regular statistics and nonlinear statistics, conduct group comparison and visualize the results. Especially, Power Spectral Density ('PSD') (A. Eke (2000) ), 'MultiScale Entropy(MSE)' ('Madalena Costa(2002)' ) and 'MultiFractal Detrended Fluctuation Analysis(MFDFA)' ('Jan W.Kantelhardt' (2002) ) were applied for the analysis of respiratory data.

Fitting GLMs with Missing Data in Both Responses and Covariates

Fits generalized linear models (GLMs) when there is missing data in both the response and categorical covariates. The functions implement likelihood-based methods using the Expectation and Maximization (EM) algorithm and optionally apply Firth’s bias correction for improved inference. See Pradhan, Nychka, and Bandyopadhyay (2025) , Maiti and Pradhan (2009) , Maity, Pradhan, and Das (2019) for further methodological details.

Compute Decision Interval and Average Run Length for CUSUM Charts

Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media.

Pedigree-Based Mixed-Effects Models

Fit pedigree-based mixed-effects models.

Data Sets for Psychometric Modeling

Collection of data sets from various assessments that can be used to evaluate psychometric models. These data sets have been analyzed in the following papers that introduced new methodology as part of the application section: Jimenez, A., Balamuta, J. J., & Culpepper, S. A. (2023) , Culpepper, S. A., & Balamuta, J. J. (2021) , Yinghan Chen et al. (2021) , Yinyin Chen et al. (2020) , Culpepper, S. A. (2019a) , Culpepper, S. A. (2019b) , Culpepper, S. A., & Chen, Y. (2019) , Culpepper, S. A., & Balamuta, J. J. (2017) , and Culpepper, S. A. (2015) .