This package provides a unified workflow for probing, plotting, and assessing the robustness of cross-level interaction effects in two-level mixed-effects models fitted with lme4 (Bates et al., 2015) <doi:10.18637/jss.v067.i01>. Implements simple slopes analysis following Aiken and West (1991, ISBN:9780761907121), Johnson-Neyman intervals following Johnson and Fay (1950) <doi:10.1007/BF02288864> and Bauer and Curran (2005) <doi:10.1207/s15327906mbr4003_5>, and grand- or group-mean centering as described in Enders and Tofighi (2007) <doi:10.1037/1082-989X.12.2.121>. Includes a slope variance decomposition that separates fixed-effect uncertainty from random-slope variance (tau11), a contour surface plot of predicted outcomes over the full predictor-by-moderator space, and robustness diagnostics comprising intraclass correlation coefficient shift analysis and leave-one-cluster-out (LOCO) stability checks. Designed for researchers in education, psychology, biostatistics, epidemiology, organizational science, and other fields where outcomes are clustered within higher-level units.

Implementation of a framework for cluster analysis with selection of the final number of clusters and an optional variable selection procedure. The package is designed to integrate the results of multiple imputed datasets while accounting for the uncertainty that the imputations introduce in the final results. In addition, the package can also be used for a cluster analysis of the complete cases of a single dataset. The package also includes specific methods to summarize and plot the results. The methods are described in Basagana et al. (2013) <doi:10.1093/aje/kws289>.

Supports matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry workflows from raw Bruker spectra to cohort-level peak matrices. Provides spectrum loading, Savitzky-Golay smoothing, baseline correction (SNIP and TopHat), Gaussian kernel-regression-based peak detection including shoulder peaks, peak-quality assessment, filtering, and cohort feature analysis. Computationally intensive routines are implemented in C++ using Rcpp'. The implemented signal-processing methods include those described by Savitzky and Golay (1964) <doi:10.1021/ac60214a047>, Ryan et al. (1988) <doi:10.1016/0168-583X(88)90063-8>, Stanford, Bagley and Solomon (2016) <doi:10.1186/s12953-016-0107-8>, and Nadaraya-Watson kernel regression (Nadaraya (1964) <doi:10.1137/1109020>; Watson (1964) <https://www.jstor.org/stable/25049340>).

This package provides a set of functions for weather and climate data manipulation, and other helper functions, to support dynamic ecological modeling, particularly crop and crop disease modeling.

This package performs causal mediation analysis for count and zero-inflated count data without or with a post-treatment confounder; calculates power to detect prespecified causal mediation effects, direct effects, and total effects; performs sensitivity analysis when there is a treatment- induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) <doi:10.1177/0962280216686131>. Implements Instrumental Variable (IV) method to estimate the controlled (natural) direct and mediation effects, and compute the bootstrap Confidence Intervals as described by Guo, Z., Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111/rssc.12233>. This software was made possible by Grant R03DE028410 from the National Institute of Dental and Craniofacial Research, a component of the National Institutes of Health.

This package provides a flexible computational framework for mixture distributions with the focus on the composite models.

Magic functions to obtain results from for loops.

Generates Raven like matrices according to different rules and the response list associated to the matrix. The package can generate matrices composed of 4 or 9 cells, along with a response list of 11 elements (the correct response + 10 incorrect responses). The matrices can be generated according to both logical rules (i.e., the relationships between the elements in the matrix are manipulated to create the matrix) and visual-spatial rules (i.e., the visual or spatial characteristics of the elements are manipulated to generate the matrix). The graphical elements of this package are based on the DescTools package. This package has been developed within the PRIN2020 Project (Prot. 20209WKCLL) titled "Computerized, Adaptive and Personalized Assessment of Executive Functions and Fluid Intelligence" and founded by the Italian Ministry of Education and Research.

This package implements differential methylation region (DMR) detection using a multistage Markov chain Monte Carlo (MCMC) algorithm based on the alpha-skew generalized normal (ASGN) distribution. Version 0.2.0 removes the Anderson-Darling test stage, improves computational efficiency of the core ASGN and multistage MCMC routines, and adds convenience functions for summarizing and visualizing detected DMRs. The methodology is based on Yang (2025) <https://www.proquest.com/docview/3218878972>.

This package performs maximal interaction two-mode clustering, permutation tests, scree plots, and interaction visualizations for bicluster analysis. See Ahmed et al. (2025) <doi:10.17605/OSF.IO/AWGXB>, Ahmed et al. (2023) <doi:10.1007/s00357-023-09434-2>, Ahmed et al. (2021) <doi:10.1007/s11634-021-00441-y>.

Mixtures of skewed and elliptical distributions are implemented using mixtures of multivariate skew power exponential and power exponential distributions, respectively. A generalized expectation-maximization framework is used for parameter estimation. See citation() for how to cite.

This package implements Moreau-Yosida Markov chain Monte Carlo ('MCMC') importance sampling for parameter estimation and Bayesian inference under smooth, non-differentiable, or light-tailed target posterior distributions and arbitrary probability models with complete or censored data. Users supply user-defined probability density functions, optional distribution functions, parameter ranges, and observations subject to complete, right, left, interval, Type-I, Type-II, progressive Type-II, first-failure, or truncation schemes. Constructs Moreau-Yosida envelopes, gradient-based proposals ('MALA', HMC', or RWM'), self-normalized importance weights, batch-means asymptotic variance estimates, and Bayesian marginal quantiles. Methodologies are based on Shukla', Vats', and Chi (2025) <doi:10.48550/arXiv.2501.02228>, Pereyra (2016) <doi:10.1111/sjos.12208>, Durmus and others (2022) <doi:10.1214/22-EJS2027>, Chen and Shao (1999) <doi:10.1214/ss/1009211804>, Roberts and Rosenthal (1998) <doi:10.1214/aoap/1028903378>, Geweke (1989) <doi:10.2307/2290062>, Hesterberg (1995) <doi:10.1080/00031305.1995.10476138>, and Balakrishnan and Aggarwala (2000, ISBN:978-0-8176-4001-9).

Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.

Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani, T. G. Vaughan and T. Stadler (2018) <doi:10.1098/rsif.2018.0512>.

Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.

This package provides a framework for multiple imputation for proteomics is proposed by Marie Chion, Christine Carapito and Frederic Bertrand (2021) <doi:10.1371/journal.pcbi.1010420>. It is dedicated to dealing with multiple imputation for proteomics.

This package contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale Multifractal Analysis (MMA). The MFDFA() function proposed in this package was used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). See references for more information. Interested users can find a parallel version of the MFDFA() function on GitHub.

An implementation of Multi-Task Logistic Regression (MTLR) for R. This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves by learning feature weights which vary across time. This model was further extended to account for left and interval censored data.

The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.

Gene Expression datasets for the MM2S package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594'). Deena Gendoo et al. (2015) <doi:10.1016/j.ygeno.2015.05.002>.

Quantify exposure-outcome causal effects with adjustment for multiple biases. The functions can simultaneously adjust for any combination of uncontrolled confounding, exposure/outcome misclassification, and selection bias. The underlying method generalizes the combination of inverse probability of selection weighting with predictive value weighting. Simultaneous multi-bias analysis can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.

Calculates and differentiates probabilities and density of (conditional) multivariate normal distribution and Gaussian copula (with various marginal distributions) using methods described in A. Genz (2004) <doi:10.1023/B:STCO.0000035304.20635.31>, A. Genz, F. Bretz (2009) <doi:10.1007/978-3-642-01689-9>, H. I. Gassmann (2003) <doi:10.1198/1061860032283> and E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>.

Run multiple Large Language Model predictions against a table. The predictions run row-wise over a specified column. It works using a one-shot prompt, along with the current row's content. The prompt that is used will depend of the type of analysis needed.

An API wrapper for the Monash University Probabilistic Footy Tipping Competition <https://probabilistic-footy.monash.edu/~footy/index.shtml>. Allows users to submit tips directly to the competition from R.