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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).
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.
Data sets from "Introductory Statistics for Engineering Experimentation"
Datasets from Nelson, Coffin and Copeland "Introductory Statistics for Engineering Experimentation" (Elsevier, 2003) with sample code.
Functions and Datasets Required for ST370 Class
Provides functions and datasets required for the ST 370 course at North Carolina State University.
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)
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)'
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)