Copula-based regression models for multivariate censored data, including bivariate right-censored data, bivariate interval-censored data, and right/interval-censored semi-competing risks data. Currently supports Clayton, Gumbel, Frank, Joe, AMH and Copula2 copula models. For marginal models, it supports parametric (Weibull, Loglogistic, Gompertz) and semiparametric (Cox and transformation) models. Includes methods for convenient prediction and plotting. Also provides a bivariate time-to-event simulation function and an information ratio-based goodness-of-fit test for copula. Method details can be found in Sun et.al (2019) Lifetime Data Analysis, Sun et.al (2021) Biostatistics, Sun et.al (2022) Statistical Methods in Medical Research, Sun et.al (2022) Biometrics, and Sun et al. (2023+) JRSSC.

This package provides a self-contained set of methods to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events using groupings by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.

This package provides a comprehensive toolkit for political linguistics featuring a museum of famous digital gaffes, phonetic transformation algorithms (Soundex, consonant shifts), QWERTY keyboard geometry for typo simulation, syllable parsing, word blending (portmanteau creation), and text corruption analysis. Originally inspired by the infamous "covfefe" tweet of 2017.

This package provides functions for the estimation of conditional copulas models, various estimators of conditional Kendall's tau (proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>), and measures of non-simplifyingness (proposed in Derumigny (2025) <doi:10.48550/arXiv.2504.07704>).

This package provides functions that format statistical output in a way that can be inserted into R Markdown documents. This is analogous to the apa_print() functions in the papaja package but prints Markdown or LaTeX syntax.

Classification method described in Dancik et al (2011) <doi:10.1158/0008-5472.CAN-11-2427> that classifies a sample according to the class with the maximum mean (or any other function of) correlation between the test and training samples with known classes.

In many cases, experiments must be repeated across multiple seasons or locations to ensure applicability of findings. A single experiment conducted in one location and season may yield limited conclusions, as results can vary under different environmental conditions. In agricultural research, treatment à location and treatment à season interactions play a crucial role. Analyzing a series of experiments across diverse conditions allows for more generalized and reliable recommendations. The CANE package facilitates the pooled analysis of experiments conducted over multiple years, seasons, or locations. It is designed to assess treatment interactions with environmental factors (such as location and season) using various experimental designs. The package supports pooled analysis of variance (ANOVA) for the following designs: (1) PooledCRD()': completely randomized design; (2) PooledRBD()': randomized block design; (3) PooledLSD()': Latin square design; (4) PooledSPD()': split plot design; and (5) PooledStPD()': strip plot design. Each function provides the following outputs: (i) Individual ANOVA tables based on independent analysis for each location or year; (ii) Testing of homogeneity of error variances among distinct locations using Bartlettâ s Chi-Square test; (iii) If Bartlettâ s test is significant, Aitkenâ s transformation, defined as the ratio of the response to the square root of the error mean square, is applied to the response variable; otherwise, the data is used as is; (iv) Combined analysis to obtain a pooled ANOVA table; (v) Multiple comparison tests, including Tukey's honestly significant difference (Tukey's HSD) test, Duncanâ s multiple range test (DMRT), and the least significant difference (LSD) test, for treatment comparisons. The statistical theory and steps of analysis of these designs are available in Dean et al. (2017)<doi:10.1007/978-3-319-52250-0> and Ruà z et al. (2024)<doi:10.1007/978-3-031-65575-3>. By broadening the scope of experimental conclusions, CANE enables researchers to derive robust, widely applicable recommendations. This package is particularly valuable in agricultural research, where accounting for treatment à location and treatment à season interactions is essential for ensuring the validity of findings across multiple settings.

This package implements the Cross-contribution Compensating Multiple standard Normalization (CCMN) method described in Redestig et al. (2009) Analytical Chemistry <doi:10.1021/ac901143w> and other normalization algorithms.

Estimation of population size of migratory caribou herds based on large scale aggregations monitored by radio telemetry. It implements the methodology found in the article by Rivest et al. (1998) about caribou abundance estimation. It also includes a function based on the Lincoln-Petersen Index as applied to radio telemetry data by White and Garrott (1990).

Interacts with the Lifebit Platform Cohort Browser API <https://cloudos.lifebit.ai>. Enables schema discovery, table exploration, and read-only SQL query execution with policy-aware behavior and team-based access control for cohort data analysis. Requires bastion-enabled workspaces for API access.

Evaluates the probability density function (PDF), cumulative distribution function (CDF), quantile function (QF), random numbers and maximum likelihood estimates (MLEs) of well-known complementary binomial-G, complementary negative binomial-G and complementary geometric-G families of distributions taking baseline models such as exponential, extended exponential, Weibull, extended Weibull, Fisk, Lomax, Burr-XII and Burr-X. The functions also allow computing the goodness-of-fit measures namely the Akaike-information-criterion (AIC), the Bayesian-information-criterion (BIC), the minimum value of the negative log-likelihood (-2L) function, Anderson-Darling (A) test, Cramer-Von-Mises (W) test, Kolmogorov-Smirnov test, P-value and convergence status. Moreover, some commonly used data sets from the fields of actuarial, reliability, and medical science are also provided. Related works include: a) Tahir, M. H., & Cordeiro, G. M. (2016). Compounding of distributions: a survey and new generalized classes. Journal of Statistical Distributions and Applications, 3, 1-35. <doi:10.1186/s40488-016-0052-1>.

While individual calibrated radiocarbon dates can span several centuries, combining multiple dates together with any chronological constraints can make a chronology much more robust and precise. This package uses Bayesian methods to enforce the chronological ordering of radiocarbon and other dates, for example for trees with multiple radiocarbon dates spaced at exactly known intervals (e.g., 10 annual rings). For methods see Christen 2003 <doi:10.11141/ia.13.2>. Another example is sites where the relative chronological position of the dates is taken into account - the ages of dates further down a site must be older than those of dates further up (Buck, Kenworthy, Litton and Smith 1991 <doi:10.1017/S0003598X00080534>; Nicholls and Jones 2001 <doi:10.1111/1467-9876.00250>). The paper accompanying this R package is Blaauw et al. 2024 <doi:10.1017/RDC.2024.56>.

CPP is a multiple criteria decision method to evaluate alternatives on complex decision making problems, by a probabilistic approach. The CPP was created and expanded by Sant'Anna, Annibal P. (2015) <doi:10.1007/978-3-319-11277-0>.

This package provides functions to work with data frames to prepare data for further analysis. The functions for imputation, encoding, partitioning, and other manipulation can produce log files to keep track of process.

This package implements cointegration tests with structural breaks designed for small sample sizes, following the methodology of Trinh (2022) <https://ideas.repec.org/p/ema/worpap/2022-01.html>. Supports models with no breaks, breaks in constant only, and breaks in both constant and slope. Provides endogenous break date detection using ADF or SSR minimization criteria, with small-sample adjusted critical values via response surface methodology.

This package provides a new method for identification of clusters of genomic regions within chromosomes. Primarily, it is used for calling clusters of cis-regulatory elements (COREs). CREAM uses genome-wide maps of genomic regions in the tissue or cell type of interest, such as those generated from chromatin-based assays including DNaseI, ATAC or ChIP-Seq. CREAM considers proximity of the elements within chromosomes of a given sample to identify COREs in the following steps: 1) It identifies window size or the maximum allowed distance between the elements within each CORE, 2) It identifies number of elements which should be clustered as a CORE, 3) It calls COREs, 4) It filters the COREs with lowest order which does not pass the threshold considered in the approach.

Create model-agnostic response-curve diagnostics for fitted prediction models. Supports profile curves, partial dependence, individual conditional expectation, and accumulated local effects; univariate curves, bivariate surfaces, ensemble summaries across multiple models, ALE-based interaction ranking, and optional raster-linked exploration with terra and shiny'. Static displays are returned as ggplot2 plots. For more details on the methods see Molnar (2025) <https://christophm.github.io/interpretable-ml-book/>.

This package performs the calibration procedure proposed by Sung et al. (2018+) <arXiv:1806.01453>. This calibration method is particularly useful when the outputs of both computer and physical experiments are binary and the estimation for the calibration parameters is of interest.

Choose the appropriate map projection for a given application, visualise the resulting distortion, and georeference data from unknown projections. The full functionalities of the package are described in Pozzi et al. (2026) <doi:10.31223/X5K17P> (pre-print).

One degree of freedom contrasts for lm', glm', gls', and geese objects.

This package provides a suite of functions for rapid and flexible analysis of codon usage bias. It provides in-depth analysis at the codon level, including relative synonymous codon usage (RSCU), tRNA weight calculations, machine learning predictions for optimal or preferred codons, and visualization of codon-anticodon pairing. Additionally, it can calculate various gene- specific codon indices such as codon adaptation index (CAI), effective number of codons (ENC), fraction of optimal codons (Fop), tRNA adaptation index (tAI), mean codon stabilization coefficients (CSCg), and GC contents (GC/GC3s/GC4d). It also supports both standard and non-standard genetic code tables found in NCBI, as well as custom genetic code tables.

Account for uncertainty when working with ranks. Estimate standard errors consistently in linear regression with ranked variables. Construct confidence sets of various kinds for positions of populations in a ranking based on values of a certain feature and their estimation errors. Theory based on Mogstad, Romano, Shaikh, and Wilhelm (2023)<doi:10.1093/restud/rdad006> and Chetverikov and Wilhelm (2023) <doi:10.48550/arXiv.2310.15512>.

Customized training is a simple technique for transductive learning, when the test covariates are known at the time of training. The method identifies a subset of the training set to serve as the training set for each of a few identified subsets in the training set. This package implements customized training for the glmnet() and cv.glmnet() functions.

Simulating bivariate survival data from various copula models. Estimating bivariate copula models with semiparametric or Weibull margins under various copulas. Two different ways to estimate the association parameter in copula models are implemented. A goodness-of-fit test for the Gumbel and Clayton copulas is also implemented for semiparametric models. See Emura, Lin and Wang (2010) <doi:10.1016/j.csda.2010.03.013> for details.