Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2025) for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.

Graph/Network Visualization

Build graph/network structures using functions for stepwise addition and deletion of nodes and edges. Work with data available in tables for bulk addition of nodes, edges, and associated metadata. Use graph selections and traversals to apply changes to specific nodes or edges. A wide selection of graph algorithms allow for the analysis of graphs. Visualize the graphs and take advantage of any aesthetic properties assigned to nodes and edges.

Read Spectroscopic Data from Bruker OPUS Binary Files

Reads data from Bruker OPUS binary files of Fourier-Transform infrared spectrometers of the company Bruker Optics GmbH & Co. This package is released independently from Bruker, and Bruker and OPUS are registered trademarks of Bruker Optics GmbH & Co. KG. < https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/latest-release.html>. It lets you import both measurement data and parameters from OPUS files. The main method is `read_opus()`, which reads one or multiple OPUS files into a standardized list class. Behind the scenes, the reader parses the file header for assigning spectral blocks and reading binary data from the respective byte positions, using a reverse engineering approach. Infrared spectroscopy combined with chemometrics and machine learning is an established method to scale up chemical diagnostics in various industries and scientific fields.

Consensus Clustering Methods for Multiple Imputed Data

Provides tools for performing consensus clustering on multiple imputed datasets. The package supports a range of clustering algorithms across imputations, including hierarchical methods (e.g., Ward, single, complete, average) and partition-based approaches such as k-means, k-medoids (PAM), fuzzy clustering, model-based clustering ('mclust'), and methods for mixed or categorical data (k-modes and k-prototypes). A co-assignment matrix is constructed to quantify agreement between partitions, and consensus solutions are derived via hierarchical clustering applied to the resulting dissimilarity matrix. Additional functions are provided for validation and visualization of clustering results, facilitating robust analysis in the presence of missing data. Consensus clustering framework is based on Monti et al. (2003) , rank aggregation methods follow Pihur et al. (2007) , and the PAC (Proportion of Ambiguous Clustering) metric is based on Senbabaoglu et al. (2014) .

Interface to 'Lp_solve' v. 5.5 to Solve Linear/Integer Programs

Lp_solve is freely available (under LGPL 2) software for solving linear, integer and mixed integer programs. In this implementation we supply a "wrapper" function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems. This version calls lp_solve version 5.5.

Iterative Pruning Population Admixture Inference Framework

A data clustering package based on admixture ratios (Q matrix) of population structure. The framework is based on iterative Pruning procedure that performs data clustering by splitting a given population into subclusters until meeting the condition of stopping criteria the same as ipPCA, iNJclust, and IPCAPS frameworks. The package also provides a function to retrieve phylogeny tree that construct a neighbor-joining tree based on a similar matrix between clusters. By given multiple Q matrices with varying a number of ancestors (K), the framework define a similar value between clusters i,j as a minimum number K* that makes majority of members of two clusters are in the different clusters. This K* reflexes a minimum number of ancestors we need to splitting cluster i,j into different clusters if we assign K* clusters based on maximum admixture ratio of individuals. The publication of this package is at Chainarong Amornbunchornvej, Pongsakorn Wangkumhang, and Sissades Tongsima (2020) .

One-to-One Feature Matching

Statistical methods to match feature vectors between multiple datasets in a one-to-one fashion. Given a fixed number of classes/distributions, for each unit, exactly one vector of each class is observed without label. The goal is to label the feature vectors using each label exactly once so to produce the best match across datasets, e.g. by minimizing the variability within classes. Statistical solutions based on empirical loss functions and probabilistic modeling are provided. The 'Gurobi' software and its 'R' interface package are required for one of the package functions (match.2x()) and can be obtained at < https://www.gurobi.com/> (free academic license). For more details, refer to Degras (2022) "Scalable feature matching for large data collections" and Bandelt, Maas, and Spieksma (2004) "Local search heuristics for multi-index assignment problems with decomposable costs".

Easily Install and Load the 'Tidyverse'

The 'tidyverse' is a set of packages that work in harmony because they share common data representations and 'API' design. This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step. Learn more about the 'tidyverse' at < https://www.tidyverse.org>.

Ray-Based Mapping and Visualization of Level Sets (Excursion Sets)

An (upper) level set of a function is the set of inputs for which the function value is at or above a specified threshold. (Also called an excursion set). Applications of level sets include confidence or credible regions for parameters of statistical models, where the function is the likelihood or posterior density; regions where classification rules assign high probability to a given class; and scientific or engineering models where one is interested in input regions for which model output is above a threshold. This package maps out the boundary of a level set by finding its intersections with collections of 1-dimensional rays, generalizing a proposal by Kim and Lindsay (Statistica Sinica 21:923-948, 2011). Tools are provided to generate rays, find intersections, and visualize results. The package makes few assumptions about the studied function: it may be discontinuous, it may have a complicated feasible region, and the target level set may be non-convex or have multiple, disconnected parts. Vignettes describe package usage and show examples with two to five input space dimensions.

Stratified Randomized Experiments

Estimate average treatment effects (ATEs) in stratified randomized experiments. `sreg` supports a wide range of stratification designs, including matched pairs, n-tuple designs, and larger strata with many units — possibly of unequal size across strata. 'sreg' is designed to accommodate scenarios with multiple treatments and cluster-level treatment assignments, and accommodates optimal linear covariate adjustment based on baseline observable characteristics. 'sreg' computes estimators and standard errors based on Bugni, Canay, Shaikh (2018) ; Bugni, Canay, Shaikh, Tabord-Meehan (2024+) ; Jiang, Linton, Tang, Zhang (2023+) ; Bai, Jiang, Romano, Shaikh, and Zhang (2024) ; Bai (2022) ; Bai, Romano, and Shaikh (2022) ; Liu (2024+) ; and Cytrynbaum (2024) .