Vault Client for Secrets and Sensitive Data

Provides an interface to a 'HashiCorp' vault server over its http API (typically these are self-hosted; see < https://www.vaultproject.io>). This allows for secure storage and retrieval of secrets over a network, such as tokens, passwords and certificates. Authentication with vault is supported through several backends including user name/password and authentication via 'GitHub'.

Sample Size for SMART Designs in Non-Surgical Periodontal Trials

Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) .

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; functions are provided for implementing it in both known and unknown precision profile situations. The package provides tools for precision profile weighted Deming (PWD) regression. It 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 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. Further information on mathematical derivations and applications can be found on arXiv: Hawkins and Kraker (2025) .

Reference Limits using QQ Methodology

A collection of routines for finding reference limits using, where appropriate, QQ methodology. All use a data vector X of cases from the reference population. The default is to get the central 95% reference range of the population, namely the 2.5 and 97.5 percentile, with optional adjustment of the range. Along with the reference limits, we want confidence intervals which, for historical reasons, are typically at 90% confidence. A full analysis provides six numbers: – the upper and the lower reference limits, and - each of their confidence intervals. For application details, see Hawkins and Esquivel (2024) .

Cohen's D_p Computation with Confidence Intervals

Computing Cohen's d_p in any experimental designs (between-subject, within-subject, and single-group design). Cousineau (2022) < https://github.com/dcousin3/CohensdpLibrary>; Cohen (1969, ISBN: 0-8058-0283-5).

Functions and Datasets Required for ST370 Class

Provides functions and datasets required for the ST 370 course at North Carolina State University.

Data sets from "Introductory Statistics for Engineering Experimentation"

Datasets from Nelson, Coffin and Copeland "Introductory Statistics for Engineering Experimentation" (Elsevier, 2003) with sample code.

Advanced Processing and Chart Generation from activPAL Events Files

Contains functions to generate pre-defined summary statistics from activPAL events files < https://www.palt.com/>. The package also contains functions to produce informative graphics that visualise physical activity behaviour and trends. This includes generating graphs that align physical activity behaviour with additional time based observations described by other data sets, such as sleep diaries and continuous glucose monitoring data.

A Distributed Worker Launcher Framework

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'NNG'-powered 'mirai' R package by Gao (2023) is a sleek and sophisticated scheduler that efficiently processes these intense workloads. The 'crew' package extends 'mirai' with a unifying interface for third-party worker launchers. Inspiration also comes from packages. 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) < https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischel, and Surmann (2017) .

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.