This package provides functions to model and decompose time series into principal components using singular spectrum analysis (de Carvalho and Rua (2017) <doi:10.1016/j.ijforecast.2015.09.004>; de Carvalho et al (2012) <doi:10.1016/j.econlet.2011.09.007>).

An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.

Another implementation of object-orientation in R. It provides syntactic sugar for the S4 class system and two alternative new implementations. One is an experimental version built around S4 and the other one makes it more convenient to work with lists as objects.

Calculates the necessary quantities to perform Bayesian multigroup equivalence testing. Currently the package includes the Bayesian models and equivalence criteria outlined in Pourmohamad and Lee (2023) <doi:10.1002/sta4.645>, but more models and equivalence testing features may be added over time.

This package provides a simple runner for fuzz-testing functions in an R package's public interface. Fuzz testing helps identify functions lacking sufficient argument validation, and uncovers problematic inputs that, while valid by function signature, may cause issues within the function body.

Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials.

This package implements the Bayesian calibration model described in Pratola and Chkrebtii (2018) <DOI:10.5705/ss.202016.0403> for stochastic and deterministic simulators. Additive and multiplicative discrepancy models are currently supported. See <http://www.matthewpratola.com/software> for more information and examples.

Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.

This package provides a time series usually does not have a uniform growth rate. Compound Annual Growth Rate measures the average annual growth over a given period. More details can be found in Bardhan et al. (2022) <DOI:10.18805/ag.D-5418>.

Implementation of different algorithms for analyzing randomly truncated data, one-sided and two-sided (i.e. doubly) truncated data. It serves to compute empirical cumulative distributions and also kernel density and hazard functions using different bandwidth selectors. Several real data sets are included.

This package provides functions and an example dataset for the psychometric theory of knowledge spaces. This package implements data analysis methods and procedures for simulating data and quasi orders and transforming different formulations in knowledge space theory. See package?DAKS for an overview.

This package provides a group-specific recommendation system to use dependency information from users and items which share similar characteristics under the singular value decomposition framework. Refer to paper A Group-Specific Recommender System <doi:10.1080/01621459.2016.1219261> for the details.

Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodal or bimodal.

This package provides tools for non-parametric Fourier deconvolution using the N-Power Fourier Deconvolution (NPFD) method. This package includes methods for density estimation (densprf()) and sample generation (createSample()), enabling users to perform statistical analyses on mixed or replicated data sets.

This package implements projected sparse Gaussian process Kriging ('Ingram et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the intamap package. More details on implementation ('Barillec et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).

The letters qe in the package title stand for "quick and easy," alluding to the convenience goal of the package. We bring together a variety of machine learning (ML) tools from standard R packages, providing wrappers with a simple, convenient, and uniform interface.

It's a Super K-Nearest Neighbor(SKNN) classification method with using kernel density to describe weight of the distance between a training observation and the testing sample. Comparison of performance between SKNN and KNN shows that SKNN is significantly superior to KNN.

Compile Typst files using the typst-cli (<https://typst.app>) command line tool. Automatically falls back to rendering via embedded Typst from Quarto (<https://quarto.org>) if Typst is not installed. Includes utilities to check for typst-cli availability and run Typst commands.

This package provides an extension to the Partial Credit Model and Generalized Partial Credit Models which allows for an additional person parameter that characterizes the uncertainty of the person. The method was originally proposed by Tutz and Schauberger (2020) <doi:10.1177/0146621620920932>.

This package provides a shiny app for accurate estimation of vaccine induced immunogenicity with bivariate linear modeling. Method is detailed in: Lhomme, Hejblum, Lacabaratz, Wiedemann, Lelievre, Levy, Thiebaut & Richert (2020). Journal of Immunological Methods, 477:112711. <doi:10.1016/j.jim.2019.112711>.

Streamlines the process of transitioning between data formats commonly used in survival analysis. Functions convert longitudinal data between formats used as input for survival models as well as support overall preparation. Users are able to focus on model building rather than data wrangling.

This package implements the estimation of local (and global) association measures: Lewontin's D, Ducher's Z, pointwise mutual information, normalized pointwise mutual information and chi-squared residuals. The significance of local (and global) association is accessed using p-values estimated by permutations.

This package provides a robust alternative to the traditional principal component estimator is proposed within the framework of factor models, known as Robust Exponential Factor Analysis, specifically designed for the modeling of high-dimensional datasets with heavy-tailed distributions. The algorithm estimates the latent factors and the loading by minimizing the exponential squared loss function. To determine the appropriate number of factors, we propose a modified rank minimization technique, which has been shown to significantly enhance finite-sample performance. For more detail of Robust Exponential Factor Analysis, please refer to Hu et al. (2026) <doi:10.1016/j.jmva.2025.105567>.

This package provides four boolean matrix factorization (BMF) methods. BMF has many applications like data mining and categorical data analysis. BMF is also known as boolean matrix decomposition (BMD) and was found to be an NP-hard (non-deterministic polynomial-time) problem. Currently implemented methods are Asso Miettinen, Pauli and others (2008) <doi:10.1109/TKDE.2008.53>, GreConD R. Belohlavek, V. Vychodil (2010) <doi:10.1016/j.jcss.2009.05.002> , GreConDPlus R. Belohlavek, V. Vychodil (2010) <doi:10.1016/j.jcss.2009.05.002> , topFiberM A. Desouki, M. Roeder, A. Ngonga (2019) <arXiv:1903.10326>.