This package provides routines to check identifiability of linear structural equation models and factor analysis models. The routines are based on the graphical representation of structural equation models.

Decompose a time series into seasonal, trend, and remainder components using an implementation of Seasonal Decomposition of Time Series by Loess (STL) that provides several enhancements over the STL method in the stats package. These enhancements include handling missing values, providing higher order (quadratic) loess smoothing with automated parameter choices, frequency component smoothing beyond the seasonal and trend components, and some basic plot methods for diagnostics.

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L. (1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <DOI:10.1007/s10994-017-5635-7>.

This package provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not. Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package VCA'. All kinds of, possibly transformed (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.

This package implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726.

Load Avro Files into Apache Spark using sparklyr'. This allows to read files from Apache Avro <https://avro.apache.org/>.

Implementation of the SAM prior and generation of its operating characteristics for dynamically borrowing information from historical data. For details, please refer to Yang et al. (2023) <doi:10.1111/biom.13927>.

This package provides a framework for visualizing and exploring results of a Management Strategy Evaluation (MSE). The publication quality figures and tables can be developed directly from the R console, or interactively explored with the Slick App. For more details, see the `Slick` website <https://slick.bluematterscience.com>.

Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm (Bogdan et al. 2015). Supported models include ordinary least-squares regression, binomial regression, multinomial regression, and Poisson regression. Both dense and sparse predictor matrices are supported. In addition, the package features predictor screening rules that enable fast and efficient solutions to high-dimensional problems.

This package implements confidence interval and sample size methods that are especially useful in psychological research. The methods can be applied in 1-group, 2-group, paired-samples, and multiple-group designs and to a variety of parameters including means, medians, proportions, slopes, standardized mean differences, standardized linear contrasts of means, plus several measures of correlation and association. Confidence interval and sample size functions are given for single parameters as well as differences, ratios, and linear contrasts of parameters. The sample size functions can be used to approximate the sample size needed to estimate a parameter or function of parameters with desired confidence interval precision or to perform a variety of hypothesis tests (directional two-sided, equivalence, superiority, noninferiority) with desired power. For details see: Statistical Methods for Psychologists, Volumes 1 â 4, <https://dgbonett.sites.ucsc.edu/>.

An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. Also includes all his song details such as lyrics and albums. Data extracted from: <http://brucebase.wikidot.com/>.

Enables the creation of Chain Event Graphs over spatial areas, with an optional Shiny user interface. Allows users to fully customise both the structure and underlying model of the Chain Event Graph, offering a high degree of flexibility for tailored analyses. For more details on Chain Event Graphs, see Freeman, G., & Smith, J. Q. (2011) <doi:10.1016/j.jmva.2011.03.008>, Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604) and Barclay, L. M., Hutton, J. L., & Smith, J. Q. (2014) <doi:10.1214/13-BA843>.

Quantify stratigraphic disorder using the metrics defined by Burgess (2016) <doi:10.2110/jsr.2016.10>. Contains a range of utility tools to construct and manipulate stratigraphic columns.

Transfers/imputes statistics among Spanish spatial polygons (census sections or postal code areas) from different moments in time (2001-2023) without need of spatial files, just linking statistics to the ID codes of the spatial units. The data available in the census sections of a partition/division (cartography) into force in a moment of time is transferred to the census sections of another partition/division employing the geometric approach (also known as areal weighting or polygon overlay). References: Goerlich (2022) <doi:10.12842/WPIVIE_0322>. Pavà a and Cantarino (2017a, b) <doi:10.1111/gean.12112>, <doi:10.1016/j.apgeog.2017.06.021>. Pérez and Pavà a (2024a, b) <doi:10.4995/CARMA2024.2024.17796>, <doi:10.38191/iirr-jorr.24.057>. Acknowledgements: The authors wish to thank Consellerà a de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACIO/2023/031), Consellerà a de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257), Ministerio de Economà a e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre for supporting this research.

This package implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates. Zhang, B., Small, D. S. (2018). <arXiv:1812.00215>.

Stress Response score (SRscore) is a stress responsiveness measure for transcriptome datasets and is based on the vote-counting method. The SRscore is determined to evaluate and score genes on the basis of the consistency of the direction of their regulation (Up-regulation, Down-regulation, or No change) under stress conditions across multiple analyzed research projects. This package is based on the HN-score (score based on the ratio of gene expression between hypoxic and normoxic conditions) proposed by Tamura and Bono (2022) <doi:10.3390/life12071079>, and can calculate both the original method and an extended calculation method described in Fukuda et al. (2025) <doi:10.1093/plphys/kiaf105>.

Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.

This package provides methods to detect structural changes in time series or random fields (spatial data). Focus is on the detection of abrupt changes or trends in independent data, but the package also provides a function to de-correlate data with dependence. The functions are based on the test suggested in Schmidt (2024) <DOI:10.3150/23-BEJ1686> and the work in Görz and Fried (2025) <DOI:10.48550/arXiv.2512.11599>.

This package contains functionality for regression standardization. Four general classes of models are allowed; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models and shared frailty gamma-Weibull models. Sjolander, A. (2016) <doi:10.1007/s10654-016-0157-3>.

This package provides some code to run simulations of state-space models, and then use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle filter based particle Markov chain Monte Carlo (PMCMC) algorithm (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>). Also provides functions to plot and summarise the outputs.

This package provides a statistical method for reducing the number of covariates in an analysis by evaluating Variable Importance Measures (VIMPs) derived from the Random Forest algorithm. It performs statistical tests on the VIMPs and outputs whether the covariate is significant along with the p-values.

This package provides a step-down procedure for controlling the False Discovery Proportion (FDP) in a competition-based setup, implementing Dong et al. (2020) <arXiv:2011.11939>. Such setups include target-decoy competition (TDC) in computational mass spectrometry and the knockoff construction in linear regression.

This package provides functions for the stratigraphic analysis of phylogenetic trees.