Routines to select and visualize the maxima for a given strict partial order. This especially includes the computation of the Pareto frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. (2001) <doi:10.1109/ICDE.2001.914855>), and some generalizations known as database preferences (see Kieà ling (2002) <doi:10.1016/B978-155860869-6/50035-4>).

Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.

Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> and the slope comparing adaptive repeated median by Borowski and Fried (2013) <doi:10.1007/s11222-013-9391-7> choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393> for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter with automatic outlier replacement and shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.

Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>. Further discussion on robust mediation analysis can be found in Alfons & Schley (2025) <doi:10.1002/wics.70051>.

Recursive partitioning methods to build classification trees for ordinal responses within the CART framework. Trees are grown using the Generalized Gini impurity function, where the misclassification costs are given by the absolute or squared differences in scores assigned to the categories of the response. Pruning is based on the total misclassification rate or on the total misclassification cost.

This package provides functions to calculate Sample Number and Average Sample Number for Repetitive Group Sampling Plan Based on Cpk as given in Aslam et al. (2013) (<DOI:10.1080/00949655.2012.663374>).

Goldwin-Pierre correlogram. Research of critical periods in the past. Integrates a time series in a given window.

R-based access to mass-spectrometry (MS) data. While many packages exist to process MS data, many of these make it difficult to access the underlying mass-to-charge ratio (m/z), intensity, and retention time of the files themselves. This package is designed to format MS data in a tidy fashion and allows the user perform the plotting and analysis.

Accesses the California Academy of Sciences Eschmeyer's Catalog of Fishes in R using web requests. The Catalog of fishes is the leading authority in fish taxonomy. Functions in the package allow users to search for fish taxa and valid names, retrieve taxonomic references, retrieve monthly taxonomic changes, obtain natural history collection information, and see the number of species by taxonomic group. For more information on the Catalog: Fricke, R., Eschmeyer, W. N. & R. van der Laan (eds) 2025. ESCHMEYER'S CATALOG OF FISHES <https://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp>.

This package provides a collection of efficient implementations of popular offline change-point detection algorithms, featuring a consistent, object-oriented interface for practical use.

Quantitative Structure-Activity Relationship (QSAR) modeling is a valuable tool in computational chemistry and drug design, where it aims to predict the activity or property of chemical compounds based on their molecular structure. In this vignette, we present the rQSAR package, which provides functions for variable selection and QSAR modeling using Multiple Linear Regression (MLR), Partial Least Squares (PLS), and Random Forest algorithms.

This package provides functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.

This package provides a robust Partial Least-Squares (PLS) method is implemented that is robust to outliers in the residuals as well as to leverage points. A specific weighting scheme is applied which avoids iterations, and leads to a highly efficient robust PLS estimator.

This package provides functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.

Robust kernel center matrix, robust kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A, Fukumizu, K., Wang Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>. Alam, M. A, Calhoun, C. D., Wang Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.

Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.

Enables researchers to conduct multivariate statistical analyses of survey data with randomized response technique items from several designs, including mirrored question, forced question, and unrelated question. This includes regression with the randomized response as the outcome and logistic regression with the randomized response item as a predictor. In addition, tools for conducting power analysis for designing randomized response items are included. The package implements methods described in Blair, Imai, and Zhou (2015) Design and Analysis of the Randomized Response Technique, Journal of the American Statistical Association <https://graemeblair.com/papers/randresp.pdf>.

This package provides a set of functions to see and interactively adjust a distribution of lessons by day, aiming at homogenizing individual distributions (for each class and teacher).

Algorithms for solving a self-calibrated l1-regularized quadratic programming problem without parameter tuning. The algorithm, called DECODE, can handle high-dimensional data without cross-validation. It is found useful in high dimensional portfolio selection (see Pun (2018) <https://ssrn.com/abstract=3179569>) and large precision matrix estimation and sparse linear discriminant analysis (see Pun and Hadimaja (2019) <https://ssrn.com/abstract=3422590>).

Client for various CrossRef APIs', including metadata search with their old and newer search APIs', get citations in various formats (including bibtex', citeproc-json', rdf-xml', etc.), convert DOIs to PMIDs', and vice versa', get citations for DOIs', and get links to full text of articles when available.

Detects copy number alteration events in targeted exon sequencing data for tumor samples without matched normal controls. The advantage of this method is that it can be applied to smaller sequencing panels including evaluations of exon, transcript, gene, or even user specified genetic regions of interest. Functions in the package include steps for GC-content correction, calculation of quantile based normal karyotype ranges, and calculation of feature score. Cutoffs for "normal" quantile and score are user-adjustable.

These functions are especially helpful when writing reports of data analysis using "Sweave".

The main purpose of this package is to streamline the generation of exams that include random elements in exercises. Exercises can be defined in a table, based on text and figures, and may contain gaps to be filled with provided options. Exam documents can be generated in various formats. It allows us to generate a version for conducting the assessment and another version that facilitates correction, linked through a code.

Displays palette of 5 colors based on photos depicting the unique and vibrant culture of Punjab in Northern India. Since Punjab translates to ``Land of 5 Rivers there are 5 colors per palette. If users need more than 5 colors, they can merge 2 to 3 palettes to create their own color-combination, or they can cherry-pick their own custom colors. Users can view up to 3 palettes together. Users can also list all the palette choices. And last but not least, users can see the photo that inspired a particular palette.