Is designed to interactively and reproducibly visualize and filter SNP (single-nucleotide polymorphism) datasets. This R-based implementation of SNP and genotype filters facilitates an interactive and iterative SNP filtering pipeline, which can be documented reproducibly via rmarkdown'. SNPfiltR contains functions for visualizing various quality and missing data metrics for a SNP dataset, and then filtering the dataset based on user specified cutoffs. All functions take vcfR objects as input, which can easily be generated by reading standard vcf (variant call format) files into R using the R package vcfR authored by Knaus and Grünwald (2017) <doi:10.1111/1755-0998.12549>. Each SNPfiltR function can return a newly filtered vcfR object, which can then be written to a local directory in standard vcf format using the vcfR package, for downstream population genetic and phylogenetic analyses.

The four functions svdcp() ('cp for column partitioned), svdbip() or svdbip2() ('bip for bipartitioned), and svdbips() ('s for a simultaneous optimization of a set of r solutions), correspond to a singular value decomposition (SVD) by blocks notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets x_i and y_j of variables and amount to estimate a link between x_i and y_j for the pair (x_i, y_j) relatively to the links associated to all the other pairs. These methods were first presented in: Lafosse R. & Hanafi M.,(1997) <https://eudml.org/doc/106424> and Hanafi M. & Lafosse, R. (2001) <https://eudml.org/doc/106494>.

Algorithms for computing and generating plots with and without error bars for Bayesian cluster validity index (BCVI) (O. Preedasawakul, and N. Wiroonsri, A Bayesian Cluster Validity Index, Computational Statistics & Data Analysis, 202, 108053, 2025. <doi:10.1016/j.csda.2024.108053>) based on several underlying cluster validity indexes (CVIs) including Calinski-Harabasz, Chou-Su-Lai, Davies-Bouldin, Dunn, Pakhira-Bandyopadhyay-Maulik, Point biserial correlation, the score function, Starczewski, and Wiroonsri indices for hard clustering, and Correlation Cluster Validity, the generalized C, HF, KWON, KWON2, Modified Pakhira-Bandyopadhyay-Maulik, Pakhira-Bandyopadhyay-Maulik, Tang, Wiroonsri-Preedasawakul, Wu-Li, and Xie-Beni indices for soft clustering. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). Though BCVI is compatible with any underlying existing CVIs, we recommend users to use either WI or WP as the underlying CVI.

Fit data to an ellipse, hyperbola, or parabola. Bootstrapping is available when needed. The conic curve can be rotated through an arbitrary angle and the fit will still succeed. Helper functions are provided to convert generator coefficients from one style to another, generate test data sets, rotate conic section parameters, and so on. References include Nikolai Chernov (2014) "Fitting ellipses, circles, and lines by least squares" <https://people.cas.uab.edu/~mosya/cl/>; A. W. Fitzgibbon, M. Pilu, R. B. Fisher (1999) "Direct Least Squares Fitting of Ellipses" IEEE Trans. PAMI, Vol. 21, pages 476-48; N. Chernov, Q. Huang, and H. Ma (2014) "Fitting quadratic curves to data points", British Journal of Mathematics & Computer Science, 4, 33-60; N. Chernov and H. Ma (2011) "Least squares fitting of quadratic curves and surfaces", Computer Vision, Editor S. R. Yoshida, Nova Science Publishers, pp. 285-302.

The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot.

omicsGMF is a Bioconductor package that uses the sgdGMF-framework of the \codesgdGMF package for highly performant and fast matrix factorization that can be used for dimensionality reduction, visualization and imputation of omics data. It considers data from the general exponential family as input, and therefore suits the use of both RNA-seq (Poisson or Negative Binomial data) and proteomics data (Gaussian data). It does not require prior transformation of counts to the log-scale, because it rather optimizes the deviances from the data family specified. Also, it allows to correct for known sample-level and feature-level covariates, therefore enabling visualization and dimensionality reduction upon batch correction. Last but not least, it deals with missing values, and allows to impute these after matrix factorization, useful for proteomics data. This Bioconductor package allows input of SummarizedExperiment, SingleCellExperiment, and QFeature classes.

This package performs risk analysis using general non-linear models. Risk models can be the sum or product of terms. Each term is the product of exponential/linear functions of covariates. Additionally sub-terms can be defined as a sum of exponential, linear threshold, and step functions. Cox Proportional hazards <https://en.wikipedia.org/wiki/Proportional_hazards_model>, Poisson <https://en.wikipedia.org/wiki/Poisson_regression>, and Fine-Gray competing risks <https://www.publichealth.columbia.edu/research/population-health-methods/competing-risk-analysis> regression are supported. This work was sponsored by NASA Grants 80NSSC19M0161 and 80NSSC23M0129 through a subcontract from the National Council on Radiation Protection and Measurements (NCRP). The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded in part by NSF grants CNS-1006860, EPS-1006860, EPS-0919443, ACI-1440548, CHE-1726332, and NIH P20GM113109.

This package implements code to identify lexical competitors in a given list of words. We include many of the standard competitor types used in spoken word recognition research, such as functions to find cohorts, neighbors, and rhymes, amongst many others. The package includes documentation for using a variety of lexicon files, including those with form codes made up of multiple letters (i.e., phoneme codes) and also basic orthographies. Importantly, the code makes use of multiple CPU cores and vectorization when possible, making it extremely fast and able to handle large lexicons. Additionally, the package contains documentation for users to easily write new functions, allowing researchers to examine other relationships within a lexicon. Preprint: <https://osf.io/preprints/psyarxiv/8dyru/>. Open access: <doi:10.3758/s13428-021-01667-6>. Citation: Li, Z., Crinnion, A.M. & Magnuson, J.S. (2021). <doi:10.3758/s13428-021-01667-6>.

An increasingly important source of health-related bibliographic content are preprints - preliminary versions of research articles that have yet to undergo peer review. The two preprint repositories most relevant to health-related sciences are medRxiv <https://www.medrxiv.org/> and bioRxiv, both of which are operated by the Cold Spring Harbor Laboratory. medrxivr provides programmatic access to the Cold Spring Harbour Laboratory (CSHL) API <https://api.biorxiv.org/>, allowing users to easily download medRxiv and bioRxiv preprint metadata (e.g. title, abstract, publication date, author list, etc) into R. medrxivr also provides functions to search the downloaded preprint records using regular expressions and Boolean logic, as well as helper functions that allow users to export their search results to a .BIB file for easy import to a reference manager and to download the full-text PDFs of preprints matching their search criteria.

An implementation of DuMouchel's (1999) <doi:10.1080/00031305.1999.10474456> Bayesian data mining method for the market basket problem. Calculates Empirical Bayes Geometric Mean (EBGM) and posterior quantile scores using the Gamma-Poisson Shrinker (GPS) model to find unusually large cell counts in large, sparse contingency tables. Can be used to find unusually high reporting rates of adverse events associated with products. In general, can be used to mine any database where the co-occurrence of two variables or items is of interest. Also calculates relative and proportional reporting ratios. Builds on the work of the PhViD package, from which much of the code is derived. Some of the added features include stratification to adjust for confounding variables and data squashing to improve computational efficiency. Includes an implementation of the EM algorithm for hyperparameter estimation loosely derived from the mederrRank package.

Optimal hard thresholding of singular values. The procedure adaptively estimates the best singular value threshold under unknown noise characteristics. The threshold chosen by ScreeNOT is optimal (asymptotically, in the sense of minimum Frobenius error) under the the so-called "Spiked model" of a low-rank matrix observed in additive noise. In contrast to previous works, the noise is not assumed to be i.i.d. or white; it can have an essentially arbitrary and unknown correlation structure, across either rows, columns or both. ScreeNOT is proposed to practitioners as a mathematically solid alternative to Cattell's ever-popular but vague Scree Plot heuristic from 1966. If you use this package, please cite our paper: David L. Donoho, Matan Gavish and Elad Romanov (2023). "ScreeNOT: Exact MSE-optimal singular value thresholding in correlated noise." Annals of Statistics, 2023 (To appear). <arXiv:2009.12297>.

Urban water and sanitation survey dataset collected by Water and Sanitation for the Urban Poor (WSUP) with technical support from Valid International. These citywide surveys have been collecting data allowing water and sanitation service levels across the entire city to be characterised, while also allowing more detailed data to be collected in areas of the city of particular interest. These surveys are intended to generate useful information for others working in the water and sanitation sector. Current release version includes datasets collected from a survey conducted in Dhaka, Bangladesh in March 2017. This survey in Dhaka is one of a series of surveys to be conducted by WSUP in various cities in which they operate including Accra, Ghana; Nakuru, Kenya; Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. This package will be updated once the surveys in other cities are completed and datasets have been made available.

Toolbox to process raw data from closed loop flux chamber (or tent) setups into ecosystem gas fluxes usable for analysis. It goes from a data frame of gas concentration over time (which can contain several measurements) and a meta data file indicating which measurement was done when, to a data frame of ecosystem gas fluxes including quality diagnostics. Organized with one function per step, maximizing user flexibility and backwards compatibility. Different models to estimate the fluxes from the raw data are available: exponential as described in Zhao et al (2018) <doi:10.1016/j.agrformet.2018.08.022>, exponential as described in Hutchinson and Mosier (1981) <doi:10.2136/sssaj1981.03615995004500020017x>, quadratic, and linear. Other functions include quality assessment, plotting for visual check, calculation of fluxes based on the setup specific parameters (chamber size, plot area, ...), gross primary production and transpiration rate calculation, and light response curves.

This package provides row-level data provenance tracking for analytical pipelines. Tags datasets with unique lineage identifiers that persist through filter, join, and derive operations. Requires documented reasons for every row exclusion, capturing who was removed, why, and at which pipeline stage. Variable derivations are registered as structured specifications linking output variables back to their source. Any row in any downstream dataset can be traced back to its origin via lg_trace(). Generates structured HTML provenance reports suitable for regulatory submissions, internal audit, or analytical documentation. General-purpose: works for clinical data, machine learning pipelines, financial modelling, epidemiology, or any workflow where row-level accountability matters. Optional features support pharmaceutical users including population flag definitions, source-to-analysis variable mapping, and Reviewer's Guide-aligned report output. Complements the regulog package for tamper-evident session-level audit logging. For more details see <https://reprostats.org/lineager/>.

This package provides basic functionalities to calculate the position of satellites given a known state vector. The package includes implementations of the SGP4 and SDP4 simplified perturbation models to propagate orbital state vectors, as well as utilities to read TLE files and convert coordinates between different frames of reference. Several of the functionalities of the package (including the high-precision numerical orbit propagator) require the coefficients and data included in the asteRiskData package, available in a drat repository. To install this data package, run install.packages("asteRiskData", repos="https://rafael-ayala.github.io/drat/")'. Felix R. Hoots, Ronald L. Roehrich and T.S. Kelso (1988) <https://celestrak.org/NORAD/documentation/spacetrk.pdf>. David Vallado, Paul Crawford, Richard Hujsak and T.S. Kelso (2012) <doi:10.2514/6.2006-6753>. Felix R. Hoots, Paul W. Schumacher Jr. and Robert A. Glover (2014) <doi:10.2514/1.9161>.

This package provides functions to compute fuzzy versions of species occurrence patterns based on presence-absence data (including inverse distance interpolation, trend surface analysis, and prevalence-independent favourability obtained from probability of presence), as well as pair-wise fuzzy similarity (based on fuzzy logic versions of commonly used similarity indices) among those occurrence patterns. Includes also functions for model consensus and comparison (overlap and fuzzy similarity, fuzzy loss, fuzzy gain), and for data preparation, such as obtaining unique abbreviations of species names, defining the background region, cleaning and gridding (thinning) point occurrence data onto raster maps, selecting among (pseudo)absences to address survey bias, converting species lists (long format) to presence-absence tables (wide format), transposing part of a data frame, selecting relevant variables for models, assessing the false discovery rate, or analysing and dealing with multicollinearity. Initially described in Barbosa (2015) <doi:10.1111/2041-210X.12372>.

Extends vectors to include graph relationships between their elements, and offers tools to compute useful summaries of the graph structure for use in summarising, filtering, and otherwise manipulating the graph. Node identity is positional rather than value-based, so isolated nodes and repeated values are represented without special handling. Three complementary data structures are provided, each an ordinary vector that stays a column in a data frame and slices consistently with it: node_vec is vectorised along the nodes of a graph, edge_vec is vectorised along its edges, and agg_vec (with the tabular agg_df') represents the aggregation structure common in data analysis, such as a total row over a set of categories. This makes graph relationships a native part of tidy rectangular data analysis workflows, alongside tools such as those in dplyr'. Each of these can also be converted to igraph objects for further analysis.

This package implements Lagrangian multiplier smoothing splines for flexible nonparametric regression and function estimation. Provides tools for fitting, prediction, and inference using a constrained optimization approach to enforce smoothness. Supports generalized linear models, Weibull accelerated failure time (AFT) models, Cox proportional hazards models, quadratic programming constraints, and customizable working-correlation structures, with options for parallel fitting. The core spline construction builds on Ezhov et al. (2018) <doi:10.1515/jag-2017-0029>. Quadratic-programming and SQP details follow Goldfarb & Idnani (1983) <doi:10.1007/BF02591962> and Nocedal & Wright (2006) <doi:10.1007/978-0-387-40065-5>. For smoothing spline and penalized spline background, see Wahba (1990) <doi:10.1137/1.9781611970128> and Wood (2017) <doi:10.1201/9781315370279>. For variance-component and correlation-parameter estimation, see Searle et al. (2006) <ISBN:978-0470009598>. The default multivariate partitioning step uses k-means clustering as in MacQueen (1967).

Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ä evid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ä evid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. SDModels provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.1080/10618600.2025.2569602>).

This package implements a physics-informed one-dimensional convolutional neural network (CNN1D-PINN) for estimating the complete soil water retention curve (SWRC) as a continuous function of matric potential, from soil texture, organic carbon, bulk density, and depth. The network architecture ensures strict monotonic decrease of volumetric water content with increasing suction by construction, through cumulative integration of non-negative slope outputs (monotone integral architecture). Four physics-based residual constraints adapted from Norouzi et al. (2025) <doi:10.1029/2024WR038149> are embedded in the loss function: (S1) linearity at the dry end (pF in [5, 7.6]); (S2) non-negativity at pF = 6.2; (S3) non-positivity at pF = 7.6; and (S4) a near-zero derivative in the saturated plateau region (pF in [-2, -0.3]). Includes tools for data preparation, model training, dense prediction, performance metrics, texture classification, and publication-quality visualisation.

General linear modeling with multiple responses (MANCOVA). An overall p-value for each model term is calculated by the 50-50 MANOVA method by Langsrud (2002) <doi:10.1111/1467-9884.00320>, which handles collinear responses. Rotation testing, described by Langsrud (2005) <doi:10.1007/s11222-005-4789-5>, is used to compute adjusted single response p-values according to familywise error rates and false discovery rates (FDR). The approach to FDR is described in the appendix of Moen et al. (2005) <doi:10.1128/AEM.71.4.2086-2094.2005>. Unbalanced designs are handled by Type II sums of squares as argued in Langsrud (2003) <doi:10.1023/A:1023260610025>. Furthermore, the Type II philosophy is extended to continuous design variables as described in Langsrud et al. (2007) <doi:10.1080/02664760701594246>. This means that the method is invariant to scale changes and that common pitfalls are avoided.

This package provides a set of accessible and automated functions to apply statistical models such as Simple Linear Regression (RLS, from the Spanish Regresión Lineal Simple'), Multiple Linear Regression (RLM, from the Spanish Regresión Lineal Múltiple'), Generalized Linear Models (GLM), and time series analysis through Autoregressive Integrated Moving Average (ARIMA) models. Designed to support teaching at the Universidad Autónoma Chapingo, it facilitates results interpretation and assumption validation through automatic graphical diagnostics. Developed as part of an undergraduate thesis at the Universidad Autónoma Chapingo, under the supervision of Dr. Julio César Buendà a Espinoza (thesis advisor), with the participation of the thesis committee: Diego Ernesto Lira González (secretary), Israel Lerma Serna (member), Juan Uriel Avelar Roblero (alternate), and Elisa del Carmen Martà nez Ochoa (alternate). Methods for regression and time series are based on Montgomery et al. (2021, ISBN:978-1119570141) and Box & Jenkins (1970, ISBN:978-0816211043).

Datasets and functions for the book "Modélisation statistique par la pratique avec R", F. Bertrand, E. Claeys and M. Maumy-Bertrand (2019, ISBN:9782100793525, Dunod, Paris). The first chapter of the book is dedicated to an introduction to the R statistical software. The second chapter deals with correlation analysis: Pearson, Spearman and Kendall simple, multiple and partial correlation coefficients. New wrapper functions for permutation tests or bootstrap of matrices of correlation are provided with the package. The third chapter is dedicated to data exploration with factorial analyses (PCA, CA, MCA, MDA) and clustering. The fourth chapter is dedicated to regression analysis: fitting and model diagnostics are detailed. The exercises focus on covariance analysis, logistic regression, Poisson regression, two-way analysis of variance for fixed or random factors. Various example datasets are shipped with the package: for instance on pokemon, world of warcraft, house tasks or food nutrition analyses.

Multilevel models (mixed effects models) are the statistical tool of choice for analyzing multilevel data (Searle et al, 2009). These models account for the correlated nature of observations within higher level units by adding group-level error terms that augment the singular residual error of a standard OLS regression. Multilevel and mixed effects models often require specialized data pre-processing and further post-estimation derivations and graphics to gain insight into model results. The package presented here, mlmtools', is a suite of pre- and post-estimation tools for multilevel models in R'. Package implements post-estimation tools designed to work with models estimated using lme4''s (Bates et al., 2014) lmer() function, which fits linear mixed effects regression models. Searle, S. R., Casella, G., & McCulloch, C. E. (2009, ISBN:978-0470009598). Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014) <doi:10.18637/jss.v067.i01>.