Estimation of treatment hierarchies in network meta-analysis using a novel frequentist approach based on treatment choice criteria (TCC) and probabilistic ranking models, as described by Evrenoglou et al. (2024) <DOI:10.48550/arXiv.2406.10612>. The TCC are defined using a rule based on the smallest worthwhile difference (SWD). Using the defined TCC, the NMA estimates (i.e., treatment effects and standard errors) are first transformed into treatment preferences, indicating either a treatment preference (e.g., treatment A > treatment B) or a tie (treatment A = treatment B). These treatment preferences are then synthesized using a probabilistic ranking model, which estimates the latent ability parameter of each treatment and produces the final treatment hierarchy. This parameter represents each treatments ability to outperform all the other competing treatments in the network. Here the terms ability to outperform indicates the propensity of each treatment to yield clinically important and beneficial effects when compared to all the other treatments in the network. Consequently, larger ability estimates indicate higher positions in the ranking list.

Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.

Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).

Data and code for the paper by Ehm, Gneiting, Jordan and Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings', JRSS-B, 2016 <DOI:10.1111/rssb.12154>).

This will allow easier management of a CRAN-style repository on local networks (i.e. not on CRAN). This might be necessary where hosted packages contain intellectual property owned by a corporation.

Companion package of Carrion-i-Silvestre & Sansó (2026): "Testing for Constant Unconditional Variance in Heavy-Tailed Time Series". It implements the Modified Iterative Cumulative Sum of Squares Algorithm, which is an extension of the Iterative Cumulative Sum of Squares (ICSS) Algorithm of Inclan and Tiao (1994), and it checks for changes in the unconditional variance of a time series controlling for the tail index of the underlying distribution. The fourth order moment is estimated non-parametrically to avoid the size problems when the innovations are non-Gaussian (see, Sansó et al., 2004). Critical values and p-values are generated using a Generalized Extreme Value distribution approach. References Carrion-i-Silvestre J.J & Sansó A (2026) <doi:10.1080/03610918.2026.2615207>. Inclan C & Tiao G.C (1994) <doi:10.1080/01621459.1994.10476824>, Sansó A & Aragó V & Carrion-i-Silvestre J.L (2004) <https://dspace.uib.es/xmlui/bitstream/handle/11201/152078/524035.pdf>.

This package provides a graphical user interface to apply an advanced method optimization algorithm to various sampling and analysis instruments. This includes generating experimental designs, uploading and viewing data, and performing various analyses to determine the optimal method. Details of the techniques used in this package are published in Gamble, Granger, & Mannion (2024) <doi:10.1021/acs.analchem.3c05763>.

Allows the estimation and downstream statistical analysis of the mitochondrial DNA Heteroplasmy calculated from single-cell datasets <https://github.com/ScialdoneLab/MitoHEAR/tree/master>.

Distributions that are typically used for exposure rating in general insurance, in particular to price reinsurance contracts. The vignette shows code snippets to fit the distribution to empirical data. See, e.g., Bernegger (1997) <doi:10.2143/AST.27.1.563208> freely available on-line.

Estimates the precision of transdimensional Markov chain Monte Carlo (MCMC) output, which is often used for Bayesian analysis of models with different dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible jump MCMC) relies on sampling a discrete model-indicator variable to estimate the posterior model probabilities. If only few switches occur between the models, precision may be low and assessment based on the assumption of independent samples misleading. Based on the observed transition matrix of the indicator variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2019, Statistics & Computing, 29, 631-643) <doi:10.1007/s11222-018-9828-0> draws posterior samples of the stationary distribution to (a) assess the uncertainty in the estimated posterior model probabilities and (b) estimate the effective sample size of the MCMC output.

Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.

Calculate morphine milligram equivalents (MME) for opioid dose comparison using standardized methods. Can directly call the NIH HEAL MME Online Calculator <https://research-mme.wakehealth.edu/api> API or replicate API calculations on the user's local machine from the comfort of R'. Creation of the NIH HEAL MME Online Calculator and the MME calculations implemented in this package are described in Adams MCB, Sward KA, Perkins ML, Hurley RW (2025) <doi:10.1097/j.pain.0000000000003529>.

Dimension reduction for multivariate data of extreme events with a PCA like procedure as described in Reinbott, Janà en, (2024), <doi:10.48550/arXiv.2408.10650>. Tools for necessary transformations of the data are provided.

This package provides new data-structure support for multi-precision computing for R users. The package supports 16-bit, 32-bit, and 64-bit operations. To the best of our knowledge, MPCR differs from the currently available packages in the following: MPCR introduces a new data structure that supports three different precisions (16-bit, 32-bit, and 64-bit), allowing for optimized memory allocation based on the desired precision. This feature offers significant advantages in memory optimization. MPCR extends support to all basic linear algebra methods across different precisions. Optional GPU acceleration via CUDA is available for 32-bit and 64-bit operations when CUDA Toolkit is detected during installation, while 16-bit operations are GPU-only and limited to matrix-matrix multiplication. MPCR maintains a consistent interface with normal R functions, allowing for seamless code integration and a user-friendly experience.

This package provides functions to perform sensitivity analysis on a model with multivariate output.

This package provides tools to generate random landscape graphs, evaluate species occurrence in dynamic landscapes, simulate future landscape occupation and evaluate range expansion when new empty patches are available (e.g. as a result of climate change). References: Mestre, F., Canovas, F., Pita, R., Mira, A., Beja, P. (2016) <doi:10.1016/j.envsoft.2016.03.007>; Mestre, F., Risk, B., Mira, A., Beja, P., Pita, R. (2017) <doi:10.1016/j.ecolmodel.2017.06.013>; Mestre, F., Pita, R., Mira, A., Beja, P. (2020) <doi:10.1186/s12898-019-0273-5>.

Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).

Model infectious disease dynamics in populations with multiple subgroups having different vaccination rates, transmission characteristics, and contact patterns. Calculate final and intermediate outbreak sizes, form age-structured contact models with automatic fetching of U.S. census data, and explore vaccination scenarios with an interactive shiny dashboard for a model with two subgroups, as described in Nguyen et al. (2024) <doi:10.1016/j.jval.2024.03.039> and Duong et al. (2026) <doi:10.1093/ofid/ofaf695.217>.

This package provides a comprehensive suite for assessing multivariate normality using six statistical tests (Mardia, Henzeâ Zirkler, Henzeâ Wagner, Royston, Doornikâ Hansen, Energy). Also includes univariate diagnostics, bivariate density visualization, robust outlier detection, power transformations (e.g., Boxâ Cox, Yeoâ Johnson), and imputation strategies ("mean", "median", "mice") for handling missing data. Bootstrap resampling is supported for selected tests to improve p-value accuracy in small samples. Diagnostic plots are available via both ggplot2 and interactive plotly visualizations. See Korkmaz et al. (2014) <https://journal.r-project.org/articles/RJ-2014-031/RJ-2014-031.pdf>.

Read, inspect and process corpus files for quantitative corpus linguistics. Obtain concordances via regular expressions, tokenize texts, and compute frequencies and association measures. Useful for collocation analysis, keywords analysis and variationist studies (comparison of linguistic variants and of linguistic varieties).

Calculates mean cumulative count (MCC) to estimate the expected cumulative number of recurrent events per person over time in the presence of competing risks and censoring. Implements both the Dong-Yasui equation method and sum of cumulative incidence method described in Dong, et al. (2015) <doi:10.1093/aje/kwu289>. Supports inverse probability weighting for causal inference as outlined in Gaber, et al. (2023) <doi:10.1093/aje/kwad031>. Provides S3 methods for printing, summarizing, plotting, and extracting results. Handles grouped analyses and integrates with ggplot2 <https://ggplot2.tidyverse.org/> for visualization.

Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) The constrained- unconstrained, residual- and double centering- approaches are estimated via lavaan (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via modsem it self. Alternatively model can be estimated via Mplus (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). <doi:10.1207/S15328007SEM0801_3>. "A note on estimating the Jöreskog-Yang model for latent variable interaction using LISREL 8.3." Klein, A., & Moosbrugger, H. (2000). <doi:10.1007/BF02296338>. "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). <doi:10.1080/00273170701710205>. "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). <doi:10.1080/10705511.2010.488999>. "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). <doi:10.1207/s15328007sem1304_1>. "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). <doi:10.1037/1082-989X.9.3.275>. "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus Userâ s Guide. Eighth Edition." <https://www.statmodel.com/>. Rosseel Y (2012). <doi:10.18637/jss.v048.i02>. "'lavaan': An R Package for Structural Equation Modeling.".

This package implements the multivariate autoregressive distributed lag (ARDL) unit root test proposed by Sam, McNown, Goh, and Goh (2024) <doi:10.1080/03796205.2024.2439101>. The test augments the standard ADF regression with lagged levels of a covariate to improve power when cointegration exists. Bootstrap critical values ensure correct size regardless of nuisance parameters. Provides automatic lag selection via AIC/BIC, diagnostic tests, and comprehensive inference tables following the four-case framework.

Information of the centroids and geographical limits of the regions, departments, provinces and districts of Peru.