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Simulate Pedagogical Statistical Data
Univariate and multivariate normal data simulation. They also supply a brief summary of the analysis for each experiment/design: - Independent samples. - One-way and two-way Anova. - Paired samples (T-Test & Regression). - Repeated measures (Anova & Multiple Regression). - Clinical Assay.
Cox MultiBlock Survival
This software package provides Cox survival analysis for high-dimensional and multiblock datasets.
It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis,
including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression,
Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies,
and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle
high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources
for interpreting results. While references are available within the corresponding functions,
key literature is mentioned below.
Terry M Therneau (2024) < https://CRAN.R-project.org/package=survival>,
Noah Simon et al. (2011)
Classification of Algorithms
Implements the Bi-objective Lexicographical Classification method and Performance Assessment Ratio at 10% metric for algorithm classification. Constructs matrices representing algorithm performance under multiple criteria, facilitating decision-making in algorithm selection and evaluation. Analyzes and compares algorithm performance based on various metrics to identify the most suitable algorithms for specific tasks. This package includes methods for algorithm classification and evaluation, with examples provided in the documentation. Carvalho (2019) presents a statistical evaluation of algorithmic computational experimentation with infeasible solutions
'a la Carte' on Text (ConText) Embedding Regression
A fast, flexible and transparent framework to estimate context-specific word and short document embeddings using the 'a la carte'
embeddings approach developed by Khodak et al. (2018)
Item Analysis in Rasch Models
Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0).
Simulation Tool for Causal Inference Using Longitudinal Data
Implements a simulation study to assess the strengths and
weaknesses of causal inference methods for estimating policy effects
using panel data. See Griffin et al. (2021)
Model and Analyse Interval Data
Implements methodologies for modelling interval data by Normal and Skew-Normal distributions, considering appropriate parameterizations of the variance-covariance matrix that takes into account the intrinsic nature of interval data, and lead to four different possible configuration structures. The Skew-Normal parameters can be estimated by maximum likelihood, while Normal parameters may be estimated by maximum likelihood or robust trimmed maximum likelihood methods.
Spatial Prediction for Function Value Data
Kriging based methods are used for predicting functional data (curves) with spatial dependence.
Langevin Analysis in One and Two Dimensions
Estimate drift and diffusion functions from time series and generate synthetic time series from given drift and diffusion coefficients.
Estimate Entry Models
Tools for measuring empirically the effects of entry in concentrated markets, based in Bresnahan and Reiss (1991) < https://www.jstor.org/stable/2937655>.