This package performs analysis of categorical-variable with missing values. Implements methods from Schafer, JL, Analysis of Incomplete Multivariate Data, Chapman and Hall.

Concept maps are versatile tools used across disciplines to enhance understanding, teaching, brainstorming, and information organization. This package provides functions for processing and visualizing concept mapping data, involving the sequential use of cluster analysis (for sorting participants and statements), multidimensional scaling (for positioning statements in a conceptual space), and visualization techniques, including point cluster maps and dendrograms. The methodology and its validity are discussed in Kampen, J.K., Hageman, J.A., Breuer, M., & Tobi, H. (2025). "The validity of concept mapping: let's call a spade a spade." Qual Quant. <doi:10.1007/s11135-025-02351-z>.

This package provides a simple, fast algorithm to find the neighbors and similarities of users in user-based filtering systems, to break free from the complex computation of existing similarity formulas and the ability to solve big data.

Given the hypothesis of a bi-modal distribution of cells for each marker, the algorithm constructs a binary tree, the nodes of which are subpopulations of cells. At each node, observed cells and markers are modeled by both a family of normal distributions and a family of bi-modal normal mixture distributions. Splitting is done according to a normalized difference of AIC between the two families. Method is detailed in: Commenges, Alkhassim, Gottardo, Hejblum & Thiebaut (2018) <doi: 10.1002/cyto.a.23601>.

This package provides a set of tools for evaluating clustering robustness using proportion of ambiguously clustered pairs (Senbabaoglu et al. (2014) <doi:10.1038/srep06207>), as well as similarity across methods and method stability using element-centric clustering comparison (Gates et al. (2019) <doi:10.1038/s41598-019-44892-y>). Additionally, this package enables stability-based parameter assessment for graph-based clustering pipelines typical in single-cell data analysis.

Easily cache and retrieve computation results. The package works seamlessly across interactive R sessions, R scripts and Rmarkdown documents.

It fits linear regression models for censored spatial data. It provides different estimation methods as the SAEM (Stochastic Approximation of Expectation Maximization) algorithm and seminaive that uses Kriging prediction to estimate the response at censored locations and predict new values at unknown locations. It also offers graphical tools for assessing the fitted model. More details can be found in Ordonez et al. (2018) <doi:10.1016/j.spasta.2017.12.001>.

These functions implement collocation-inference for continuous-time and discrete-time stochastic processes. They provide model-based smoothing, gradient-matching, generalized profiling and forwards prediction error methods.

Implementation of the Contextual Importance and Utility (CIU) concepts for Explainable AI (XAI). A description of CIU can be found in e.g. Främling (2020) <doi:10.1007/978-3-030-51924-7_4>.

This package provides classes (S4) of circular-linear, symmetric copulas with corresponding methods, extending the copula package. These copulas are especially useful for modeling correlation in discrete-time movement data. Methods for density, (conditional) distribution, random number generation, bivariate dependence measures and fitting parameters using maximum likelihood and other approaches. The package also contains methods for visualizing movement data and copulas.

This package provides a covariate-augmented overdispersed Poisson factor model is proposed to jointly perform a high-dimensional Poisson factor analysis and estimate a large coefficient matrix for overdispersed count data. More details can be referred to Liu et al. (2024) <doi:10.1093/biomtc/ujae031>.

Convex Partition is a black-box optimisation algorithm for single objective real-parameters functions. The basic principle is to progressively estimate and exploit a regression tree similar to a CART (Classification and Regression Tree) of the objective function. For more details see de Paz (2024) <doi:10.1007/978-3-031-62836-8_3> and Loh (2011) <doi:10.1002/widm.8> .

Cluster analysis of a set of variables. Variables can be quantitative, qualitative or a mixture of both.

Enables creation of visualizations using the CanvasXpress framework in R. CanvasXpress is a standalone JavaScript library for reproducible research with complete tracking of data and end-user modifications stored in a single PNG image that can be played back. See <https://www.canvasxpress.org> for more information.

Get text from images of text using Captricity Optical Character Recognition (OCR) API. Captricity allows you to get text from handwritten forms --- think surveys --- and other structured paper documents. And it can output data in form a delimited file keeping field information intact. For more information, read <https://shreddr.captricity.com/developer/overview/>.

Discover causality for bivariate categorical data. This package aims to enable users to discover causality for bivariate observational categorical data. See Ni, Y. (2022) <arXiv:2209.08579> "Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation. Advances in Neural Information Processing Systems 35 (in press)".

This package provides tools to process and analyze chest expansion using 3D marker data from motion capture systems. Includes functions for data processing, marker position adjustment, volume calculation using convex hulls, and visualization in 2D and 3D. Barber et al. (1996) <doi:10.1145/235815.235821>. TAMIYA Hiroyuki et al. (2021) <doi:10.1038/s41598-021-01033-8>.

Create a custom sized Core Collection based on a distance matrix and applying the A-NE (accession nearest entry), E-NE (entry nearest entry) or E-E (entry entry) method as introduced in Jansen and van Hintum (2007) <doi:10.1007/s00122-006-0433-9> and further elaborated on in Odong, T.L. (2012) <https://edepot.wur.nl/212422>. Optionally a list of preselected accessions to be included into the core can be set. For each accession in the computed core, if available nearby accessions are retrievable that can be used as an alternative.

This package provides a tool for easily matching spatial data when you have a list of place/region names. You might have a data frame that came from a spreadsheet tracking some data by suburb or state. This package can convert it into a spatial data frame ready for plotting. The actual map data is provided by other packages (or your own code).

Several functions for working with mixed effects regression models for limited dependent variables. The functions facilitate post-estimation of model predictions or margins, and comparisons between model predictions for assessing or probing moderation. Additional helper functions facilitate model comparisons and implements simulation-based inference for model predictions of alternative-specific outcome models. See also, Melamed and Doan (2024, ISBN: 978-1032509518).

Offers a set of objects tailored to simplify working with choice data. It enables the computation of choice probabilities and the likelihood of various types of choice models based on given data.

Classification of climate according to Koeppen - Geiger, of aridity indices, of continentality indices, of water balance after Thornthwaite, of viticultural bioclimatic indices. Drawing climographs: Thornthwaite, Peguy, Bagnouls-Gaussen.

Clustering method to cluster both effects curves, through quantile regression coefficient modeling, and curves in functional data analysis. Sottile G. and Adelfio G. (2019) <doi:10.1007/s00180-018-0817-8>.

Data stored in text file can be processed chunkwise using dplyr commands. These are recorded and executed per data chunk, so large files can be processed with limited memory using the LaF package.