Estimates the parameters of models with a single change-point in the hazard rate for time-to-event data. Supported models include the exponential (Gijbels & Gürler (2003) <doi:10.1023/B:LIDA.0000012424.71723.9d>, Matthews & Farewell (1982) <doi:10.2307/2530460>), Exponential-Lindley (Joshi & Rattihalli (2020) <doi:10.1007/978-981-15-5414-8_29>), Lindley (Joshi, Jose, & Bhati (2016) <doi:10.1080/03610918.2015.1096381>), log-logistic (Nadar, Upadhyay, & Joshi (2025) <doi:10.3390/math13091457>), and Weibull (Williams & Kim (2013) <doi:10.1080/03610926.2011.600505>) hazard change-point models. Provides functions for generating random variates and evaluating the probability density function (PDF) and the cumulative distribution function (CDF) of the fitted change-point models. Includes Kaplan-Meier and Nelson-Aalen diagnostic plots, together with goodness-of-fit measures such as the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), distance metrics such as the L1-norm and L2-norm, and the Kolmogorov-Smirnov (K-S) statistic for model evaluation.

This package provides functions for differential expression analysis of read counts from messenger RNA (mRNA) sequencing (RNA-Seq) data or micro RNA (miRNA) expression values generated by the Comprehensive Analysis Pipeline for microRNA Sequencing (CAP-miRSeq) expression_reports.sh script. The workflow follows the edgeR'-'limma expression data analysis pipeline providing options for different approaches, such as "pure" edgeR', voom or paired samples. The functions in the package generate text files with differential expression lists, optionally annotated with information from biomart', expression summary plots as well as several quality control (QC) plots. The main function, diffExpr(), is a convenience wrapper performing all steps automatically based on sensible defaults. Methods are described in Robinson, McCarthy and Smyth (2010) <doi:10.1093/bioinformatics/btp616>, Ritchie et al. (2015) <doi:10.1093/nar/gkv007>, Law et al. (2014) <doi:10.1186/gb-2014-15-2-r29> and Sun et al. (2014) <doi:10.1186/1471-2164-15-423>.

Offers calculation, visualization and comparison of algorithmic fairness metrics. Fair machine learning is an emerging topic with the overarching aim to critically assess whether ML algorithms reinforce existing social biases. Unfair algorithms can propagate such biases and produce predictions with a disparate impact on various sensitive groups of individuals (defined by sex, gender, ethnicity, religion, income, socioeconomic status, physical or mental disabilities). Fair algorithms possess the underlying foundation that these groups should be treated similarly or have similar prediction outcomes. The fairness R package offers the calculation and comparisons of commonly and less commonly used fairness metrics in population subgroups. These methods are described by Calders and Verwer (2010) <doi:10.1007/s10618-010-0190-x>, Chouldechova (2017) <doi:10.1089/big.2016.0047>, Feldman et al. (2015) <doi:10.1145/2783258.2783311> , Friedler et al. (2018) <doi:10.1145/3287560.3287589> and Zafar et al. (2017) <doi:10.1145/3038912.3052660>. The package also offers convenient visualizations to help understand fairness metrics.

This package provides statistical workflows used in the evaluation of in vitro diagnostic reagents. Facilities include method comparison, commutability assessment, Bland-Altman and receiver operating characteristic analysis, qualitative agreement, C5 and C95 estimation, precision and variance-component analysis, linearity, interference, dilution and spiking studies, high-dose hook assessment, measurement uncertainty, reference-material bias, reference intervals, stability studies, quality-control charts, curve fitting, analytical sensitivity, outlier and normality assessment, and sample-size calculations. For methodological details, see Bland and Altman (1986) <doi:10.1016/S0140-6736(86)90837-8>, Passing and Bablok (1983) <doi:10.1515/cclm.1983.21.11.709>, Linnet (1993) <doi:10.1093/clinchem/39.3.424>, Hawkins and Kraker (2026) <doi:10.1093/jalm/jfaf183>, Hanley and McNeil (1982) <doi:10.1148/radiology.143.1.7063747>, Horn et al. (1998) <doi:10.1093/clinchem/44.3.622>, Westgard et al. (1981) <doi:10.1093/clinchem/27.3.493>, and Lu et al. (2016) <doi:10.1515/ijb-2015-0039>.

This package provides a modular toolkit for interpretable survival machine learning with a unified interface for fitting, prediction, evaluation, and interpretation. It includes semiparametric, parametric, tree-based, ensemble, boosting, kernel, and deep-learning survival learners, together with benchmarking, scoring, calibration, and model-agnostic interpretation utilities. Representative methodological anchors include Cox (1972) <doi:10.1111/j.2517-6161.1972.tb00899.x>, Royston and Parmar (2002) <doi:10.1002/sim.1203>, Ishwaran et al. (2008) <doi:10.1214/08-AOAS169>, Jaeger et al. (2019) <doi:10.1214/19-AOAS1261>, Harrell et al. (1982) <doi:10.1001/jama.1982.03320430047030>, Graf et al. (1999) <doi:10.1002/(SICI)1097-0258(19990915/30)18:17/18%3C2529::AID-SIM274%3E3.0.CO;2-5>, Friedman (2001) <doi:10.1214/aos/1013203451>, Apley and Zhu (2020) <doi:10.1111/rssb.12377>, and Lundberg and Lee (2017) <https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions>, and other related methods for survival modeling, prediction, and interpretation.

This package implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardà a M, López-Vizcaà no E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.

Enables the analysis of spectroscopy data such as infrared ('IR'), Raman, and nuclear magnetic resonance ('NMR') using the tidy data framework from the tidyverse'. The tidyspec package provides functions for data transformation, normalization, baseline correction, smoothing, derivatives, and both interactive and static visualization. It promotes structured, reproducible workflows for spectral data exploration and preprocessing. Implemented methods include Savitzky and Golay (1964) "Smoothing and Differentiation of Data by Simplified Least Squares Procedures" <doi:10.1021/ac60214a047>, Sternberg (1983) "Biomedical Image Processing" <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1654163>, Zimmermann and Kohler (1996) "Baseline correction using the rolling ball algorithm" <doi:10.1016/0168-583X(95)00908-6>, Beattie and Esmonde-White (2021) "Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra" <doi:10.1177/0003702820987847>, Wickham et al. (2019) "Welcome to the tidyverse" <doi:10.21105/joss.01686>, and Kuhn, Wickham and Hvitfeldt (2024) "recipes: Preprocessing and Feature Engineering Steps for Modeling" <https://CRAN.R-project.org/package=recipes>.

The centralized empirical cumulative average deviation function is utilized to develop both Ada-plot and Uda-plot as alternatives to Ad-plot and Ud-plot introduced by the author. Analogous to Ad-plot, Ada-plot can identify symmetry, skewness, and outliers of the data distribution. The Uda-plot is as exceptional as Ud-plot in assessing normality. The d-value that quantifies the degree of proximity between the Uda-plot and the graph of the estimated normal density function helps guide to make decisions on confirmation of normality. Extreme values in the data can be eliminated using the 1.5IQR rule to create its robust version if user demands. Full description of the methodology can be found in the article by Wijesuriya (2025a) <doi:10.1080/03610926.2025.2558108>. Further, the development of Ad-plot and Ud-plot is contained in both article and the adplots R package by Wijesuriya (2025b & 2025c) <doi:10.1080/03610926.2024.2440583> and <doi:10.32614/CRAN.package.adplots>.

In total it has 7 functions, three for calculating machine calibration, which determine application rate (L/ha), nozzle flow (L/min) and amount of product (L or kg) to be added. to the tank with each sprayer filling. Two functions for graphs of the flow distribution of the nozzles (L/min) in the application bar and, of the temporal variability of the meteorological conditions (air temperature, relative humidity of the air and wind speed). Two functions to determine the spray deposit (uL/cm2), through the methodology called spectrophotometry, with the aid of bright blue (Palladini, L.A., Raetano, C.G., Velini, E.D. (2005), <doi:10.1590/S0103-90162005000500005>) or metallic markers (Chaim, A., Castro, V.L.S.S., Correles, F.M., Galvão, J.A.H., Cabral, O.M.R., Nicolella, G. (1999), <doi:10.1590/S0100-204X1999000500003>). The package supports the analysis and representation of information, using a single free software that meets the most diverse areas of activity in application technology.

This package implements the non-iterative conditional expectation (NICE) algorithm of the g-formula algorithm (Robins (1986) <doi:10.1016/0270-0255(86)90088-6>, Hernán and Robins (2024, ISBN:9781420076165)). The g-formula can estimate an outcome's counterfactual mean or risk under hypothetical treatment strategies (interventions) when there is sufficient information on time-varying treatments and confounders. This package can be used for discrete or continuous time-varying treatments and for failure time outcomes or continuous/binary end of follow-up outcomes. The package can handle a random measurement/visit process and a priori knowledge of the data structure, as well as censoring (e.g., by loss to follow-up) and two options for handling competing events for failure time outcomes. Interventions can be flexibly specified, both as interventions on a single treatment or as joint interventions on multiple treatments. See McGrath et al. (2020) <doi:10.1016/j.patter.2020.100008> for a guide on how to use the package.

S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in a finite setting. It contains routines for handling various types of set functions such as games or capacities. It can be used to compute several non-additive integrals: the Choquet integral, the Sugeno integral, and the symmetric and asymmetric Choquet integrals. An analysis of capacities in terms of decision behavior can be performed through the computation of various indices such as the Shapley value, the interaction index, the orness degree, etc. The well-known Möbius transform, as well as other equivalent representations of set functions can also be computed. Kappalab further contains seven capacity identification routines: three least squares based approaches, a method based on linear programming, a maximum entropy like method based on variance minimization, a minimum distance approach and an unsupervised approach based on parametric entropies. The functions contained in Kappalab can for instance be used in the framework of multicriteria decision making or cooperative game theory.

Mutational signatures are carcinogenic exposures or aberrant cellular processes that can cause alterations to the genome. We created musicatk (MUtational SIgnature Comprehensive Analysis ToolKit) to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk enables users to count and combine multiple mutation types, including SBS, DBS, and indels. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type. Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. Musicatk provides functions for visualization and downstream exploratory analysis including the ability to compare signatures between cohorts and find matching signatures in COSMIC V2 or COSMIC V3.

Fits functional generalized estimating equations for longitudinal functional outcomes and covariates using a one-step estimator that is fast even for large cluster sizes or large numbers of clusters. The package supports quasi-likelihoods derived from a range of distributions, with substantial simulations run for quasi-likelihoods derived from Gaussian, binomial, Poisson, negative binomial, Gamma and beta families. It supports common link functions and several working correlation structures. An optimized engine constructs cluster score and sensitivity statistics in one pass, provides coefficient-space Gaussian cross-validation, analytic-gradient fast cluster cross-validation, and an experimental sandwich-scaled working restricted quasi-likelihood selector. Internal compiled routines provide symmetric positive-definite Cholesky solves and exact tridiagonal precision operations for irregularly sampled continuous-time AR(1) working correlations. Uncertainty quantification is based on sandwich variance estimators and studentized wild cluster bootstrap procedures for cluster-robust pointwise intervals and optional simultaneous bands. The package implements methods described in Loewinger et al. (2025) <https://pmc.ncbi.nlm.nih.gov/articles/PMC12306803/>.

Statistical analysis of survey data with full support for survey weights, grouped operations, and tidyverse integration. Provides 80 functions for data import/export ('SPSS', Stata', SAS', Excel') with label roundtripping and tagged NA preservation, label management (variable labels, value labels, type conversions, missing value declaration), data transformation (recoding, dummy coding, standardization, centering), descriptive statistics, codebook generation, hypothesis testing, correlation analysis, post-hoc comparisons, weighted statistics, scale analysis, regression, non-parametric tests, exact tests, factorial ANOVA, and ANCOVA. Every analysis offers compact print() and detailed summary() output with toggleable sections. Statistical results are validated against SPSS version 29 within documented per-tier tolerances (see the compatibility vignette for per-function status). Methods follow the published algorithms of IBM Corp. (2023, "IBM SPSS Statistics Algorithms"), the Lilliefors-corrected normality test of Dallal and Wilkinson (1986) <doi:10.1080/00031305.1986.10475419>, and the adjusted standardized residuals of Haberman (1973) <doi:10.2307/2529686>. Designed for survey researchers, social scientists, and students working with complex survey designs.

This package provides a modeling tool dedicated to biological network modeling (Bertrand and others 2020, <doi:10.1093/bioinformatics/btaa855>). It allows for single or joint modeling of, for instance, genes and proteins. It starts with the selection of the actors that will be the used in the reverse engineering upcoming step. An actor can be included in that selection based on its differential measurement (for instance gene expression or protein abundance) or on its time course profile. Wrappers for actors clustering functions and cluster analysis are provided. It also allows reverse engineering of biological networks taking into account the observed time course patterns of the actors. Many inference functions are provided and dedicated to get specific features for the inferred network such as sparsity, robust links, high confidence links or stable through resampling links. Some simulation and prediction tools are also available for cascade networks (Jung and others 2014, <doi:10.1093/bioinformatics/btt705>). Example of use with microarray or RNA-Seq data are provided.

This package provides functions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in igraph objects. Intended to extend mvtnorm to take igraph structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.

Simplifies the process of estimating above ground biomass components for teak trees using a few basic inputs, based on the equations taken from the journal "Allometric equations for estimating above ground biomass and leaf area of planted teak (Tectona grandis) forests under agroforestry management in East Java, Indonesia" (Purwanto & Shiba, 2006) <doi:10.60409/forestresearch.76.0_1>. This function is most reliable when applied to trees from the same region where the equations were developed, specifically East Java, Indonesia. This function help to estimate the stem diameter at the lowest major living branch (DB) using the stem diameter at breast height with R^2 = 0.969. Estimate the branch dry weight (WB) using the stem diameter at breast height and tree height (R^2 = 0.979). Estimate the stem weight (WS) using the stem diameter at breast height and tree height (R^2 = 0.997. Also estimate the leaf dry weight (WL) using the stem diameter at the lowest major living branch (R^2 = 0.996).

This package provides a Boolean network is a particular kind of discrete dynamical system where the variables are simple binary switches. Despite its simplicity, Boolean network modeling has been a successful method to describe the behavioral pattern of various phenomena. Applying stochastic noise to Boolean networks is a useful approach for representing the effects of various perturbing stimuli on complex systems. A number of methods have been developed to control noise effects on Boolean networks using parameters integrated into the update rules. This package provides functions to examine three such methods: Boolean network with perturbations (BNp), described by Trairatphisan et al. (2013) <doi:10.1186/1478-811X-11-46>, stochastic discrete dynamical systems (SDDS), proposed by Murrugarra et al. (2012) <doi:10.1186/1687-4153-2012-5>, and Boolean network with probabilistic edge weights (PEW), presented by Deritei et al. (2022) <doi:10.1371/journal.pcbi.1010536>. This package includes source code derived from the BoolNet package, which is licensed under the Artistic License 2.0.

Optimizers for torch deep learning library. These functions include recent results published in the literature and are not part of the optimizers offered in torch'. Prospective users should test these optimizers with their data, since performance depends on the specific problem being solved. The packages includes the following optimizers: (a) adabelief by Zhuang et al (2020), <arXiv:2010.07468>; (b) adabound by Luo et al.(2019), <arXiv:1902.09843>; (c) adahessian by Yao et al.(2021) <arXiv:2006.00719>; (d) adamw by Loshchilov & Hutter (2019), <arXiv:1711.05101>; (e) madgrad by Defazio and Jelassi (2021), <arXiv:2101.11075>; (f) nadam by Dozat (2019), <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) qhadam by Ma and Yarats(2019), <arXiv:1810.06801>; (h) radam by Liu et al. (2019), <arXiv:1908.03265>; (i) swats by Shekar and Sochee (2018), <arXiv:1712.07628>; (j) yogi by Zaheer et al.(2019), <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.

This package provides Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of RcppArmadillo to speed up the computationally intensive parts of the functions. For more information, see

1. "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, https://doi.org/10.18637/jss.v001.i04;

2. "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, https://doi.org/10.1145/1772690.1772862;

3. "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, https://doi.org/10.21105/joss.00026;

4. "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, https://doi.org/10.1126/science.1136800.

Datasets from most recent CCIIO DIY entry in a tidy format. These support the Centers for Medicare and Medicaid Services (CMS) risk adjustment Do-It-Yourself (DIY) process, which allows health insurance issuers to calculate member risk profiles under the Health and Human Services-Hierarchical Condition Categories (HHS-HCC) regression model. This regression model is used to calculate risk adjustment transfers. Risk adjustment is a selection mitigation program implemented under the Patient Protection and Affordable Care Act (ACA or Obamacare) in the USA. Under the ACA, health insurance issuers submit claims data to CMS in order for CMS to calculate a risk score under the HHS-HCC regression model. However, CMS does not inform issuers of their average risk score until after the data submission deadline. These data sets can be used by issuers to calculate their average risk score mid-year. More information about risk adjustment and the HHS-HCC model can be found here: <https://www.cms.gov/mmrr/Articles/A2014/MMRR2014_004_03_a03.html>.

Calculate, plot and animate the configuration of Jupiter's four largest satellites (known as Galilean satellites) for a given date and time (UTC - Coordinated Universal Time). The galsat() function returns numerical values of the satellitesâ positions. x â the apparent rectangular coordinate of the satellite with respect to the center of Jupiterâ s disk in the equatorial plane in the units of Jupiterâ s equatorial radius; X is positive toward the west, y â the apparent rectangular coordinate of the satellite with respect to the center of Jupiterâ s disk from the equatorial plane in the units of Jupiterâ s equatorial radius; Y is positive toward the north. For more details see Meeus (1988, ISBN 0-943396-22-0) "Astronomical Formulae for Calculators". The galsat_animate() function creates an animation of the Galilean satellites positions. You provide the starting time, duration, the time step between frames, and the pause between frames. The function delta_t() returns the value of delta-T in units of seconds.

This package provides a native client for Apache Iceberg', the open table format used by Snowflake', Databricks', BigQuery', AWS and Dremio'. R has otherwise been able to read Iceberg tables only by routing through DuckDB as an intermediary, which rules out writes, snapshot management and catalog integration. This package talks to Iceberg directly: it connects to REST and AWS Glue catalogs, lists namespaces and tables, reads the schema and partition specification of a table, scans data with predicates and projections pushed down, travels back through snapshot history, and appends new data. Apache Arrow is the interchange layer throughout, so scan results arrive in R without a serialisation round trip. Built on iceberg-rust', the Apache-governed Rust implementation, via extendr'. Supports table spec versions 1 and 2; see the README for the full matrix of supported and unsupported features. This is a community package, not affiliated with or endorsed by The Apache Software Foundation; Apache', Apache Iceberg and Iceberg are trademarks of The Apache Software Foundation.

Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) <doi:10.1111/2041-210X.12141> in a few user-friendly R functions. All functions handle data obtained with Ahmes 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes.