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Portfolio Analysis, Including Numerical Methods for Optimization of Portfolios
Portfolio optimization and analysis routines and graphics.
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
Data sets from "Introductory Statistics for Engineering Experimentation"
Datasets from Nelson, Coffin and Copeland "Introductory Statistics for Engineering Experimentation" (Elsevier, 2003) with sample code.
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)'
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.
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:
Yinghan Chen et al. (2021)
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'.
Lightweight Reproducible Reporting
Order, create and store reports from R. By defining a lightweight interface around the inputs and outputs of an analysis, a lot of the repetitive work for reproducible research can be automated. We define a simple format for organising and describing work that facilitates collaborative reproducible research and acknowledges that all analyses are run multiple times over their lifespans.
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)