This package provides a leadership-inference framework for multivariate time series. The framework for multiple-faction-leadership inference from coordinated activities or mFLICA uses a notion of a leader as an individual who initiates collective patterns that everyone in a group follows. Given a set of time series of individual activities, our goal is to identify periods of coordinated activity, find factions of coordination if more than one exist, as well as identify leaders of each faction. For each time step, the framework infers following relations between individual time series, then identifying a leader of each faction whom many individuals follow but it follows no one. A faction is defined as a group of individuals that everyone follows the same leader. mFLICA reports following relations, leaders of factions, and members of each faction for each time step. Please see Chainarong Amornbunchornvej and Tanya Berger-Wolf (2018) <doi:10.1137/1.9781611975321.62> for methodology and Chainarong Amornbunchornvej (2021) <doi:10.1016/j.softx.2021.100781> for software when referring to this package in publications.

Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

This package provides a stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis <doi:10.1007/BF02289447>). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component. Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.

Computes partial eta-squared effect sizes for fixed effects in linear mixed models fitted with the lme4 package. Supports crossed, nested, and mixed (crossed-and-nested) random effects structures with any number of grouping factors. Mixed designs handle cases where grouping factors are simultaneously crossed with some variables and nested within others (e.g., photos nested within models, but both crossed with participants). Factor predictors are supported directly, and a single factor-level (omnibus) effect size can be obtained for a multi-level factor or multi-df interaction. Random slope variances are translated to the outcome scale using a variance decomposition approach, correctly accounting for predictor scaling and interaction terms. Both general and operative effect sizes are provided, with optional parametric bootstrap confidence intervals. For correlated predictors, per-predictor effect sizes use unique (semipartial) variance by default. Methods are based on Correll, Mellinger, McClelland, and Judd (2020) <doi:10.1016/j.tics.2019.12.009>, Correll, Mellinger, and Pedersen (2022) <doi:10.3758/s13428-021-01687-2>, and Rights and Sterba (2019) <doi:10.1037/met0000184>.

This package provides threshold sweep methods for Qualitative Comparative Analysis (QCA). Implements Condition Threshold Sweep (CTS, for one or several conditions), Outcome Threshold Sweep (OTS), and Dual Threshold Sweep (DTS) for systematic exploration of threshold calibration effects on crisp-set QCA results. These methods extend traditional robustness approaches by treating threshold variation as an explicit analytical dimension and recording the sufficiency solution obtained at each threshold setting. Also provides Fiss (2011) <doi:10.5465/amj.2011.60263120> core/peripheral condition classification via compute_fiss_core() and generate_fiss_chart(), enabling four-symbol configuration charts that distinguish core conditions (conditions of the parsimonious term contained in each configuration) from peripheral conditions (intermediate only). Built on top of the QCA package by Dusa (2019) <doi:10.1007/978-3-319-75668-4>, with function arguments following QCA conventions. Based on set-theoretic methods by Ragin (2008) <doi:10.7208/chicago/9780226702797.001.0001> and established robustness protocols by Oana and Schneider (2024) <doi:10.1177/00491241211036158>. This package supersedes TSQCA'; see the NEWS file for migration guidance.

This package provides a system for calculating the optimal sampling effort, based on the ideas of "Ecological cost-benefit optimization" as developed by A. Underwood (1997, ISBN 0 521 55696 1). Data is obtained from simulated ecological communities with prep_data() which formats and arranges the initial data, and then the optimization follows the following procedure of four functions: (1) prep_data() takes the original dataset and creates simulated sets that can be used as a basis for estimating statistical power and type II error. (2) sim_beta() is used to estimate the statistical power for the different sampling efforts specified by the user. (3) sim_cbo() calculates then the optimal sampling effort, based on the statistical power and the sampling costs. Additionally, (4) scompvar() calculates the variation components necessary for (5) Underwood_cbo() to calculate the optimal combination of number of sites and samples depending on either an economic budget or on a desired statistical accuracy. Lastly, (6) plot_power() helps the user visualize the results of sim_beta().

This package provides a set of functions for the design and analysis of Regression Discontinuity Designs as local randomized experiments within the potential outcome approach as formalized in Li, Mattei and Mealli (2015) <doi:10.1214/15-AOAS809>. A subset of functions implements the design phase of the study, where the focus is on the selection of suitable subpopulations for which valid causal inference can be drawn. These functions provide summary statistics of pre- and post-treatment variables by treatment status and select suitable subpopulations around the threshold where pre-treatment variables are well balanced between treatment groups, using randomization-based tests with adjustment for multiplicities. Functions for a visual inspection of the results are also provided. Finally, the package includes a set of functions for drawing inference on causal effects for the selected subpopulations using randomization-based modes of inference. Specifically, the Fisher Exact p-value and Neyman approaches are implemented for the analysis of both sharp and fuzzy Regression Discontinuity designs. The approach is illustrated in a study concerning the effects of university grants on student dropout.

This package implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include InterVA4 by Byass et al (2012) <doi:10.3402/gha.v5i0.19281>, InterVA5 by Byass at al (2019) <doi:10.1186/s12916-019-1333-6>, InSilicoVA by McCormick et al (2016) <doi:10.1080/01621459.2016.1152191>, NBC by Miasnikof et al (2015) <doi:10.1186/s12916-015-0521-2>, and a replication of Tariff method by James et al (2011) <doi:10.1186/1478-7954-9-31> and Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The NBC method is implemented by the nbc4va package that can be installed from <https://github.com/rrwen/nbc4va>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the Tariff method.

This package implements cognitive models for data from subjective (Likert or analog) scales and from decision making tasks with reaction times and choice data. Provides random generation, density functions, and custom response distributions for Bayesian estimation with brms', covering discrete beta, ordered beta and choice-confidence models for subjective ratings; reaction time distributions such as the ex-Gaussian and the shifted log-normal, Wald, gamma and Weibull; and sequential sampling models of choice and reaction time, including the drift diffusion model (DDM), the racing diffusion model (RDM), the lognormal race model (LNR) and the linear ballistic accumulator (LBA). The website provides examples and tutorials for using and interpreting the models. Methods are described in Ratcliff and McKoon (2008) <doi:10.1162/neco.2008.12-06-420>, Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>, Rouder et al. (2015) <doi:10.1007/s11336-013-9396-3>, Tillman et al. (2020) <doi:10.3758/s13423-020-01719-6>, Kubinec (2023) <doi:10.1017/pan.2022.20>, and Sciandra et al. (2024) <doi:10.1007/s10651-023-00592-5>.

This package provides a compact and explicit machine learning framework for supervised learning, resampling-based evaluation, hyperparameter tuning, learner comparison, interpretation, and plug-in g-computation. The package uses standard formulas for model specification and provides stable S3 interfaces for fitting, evaluation, tuning, interpretation, and causal estimation across a learner registry with multiple backend engines. Implemented interpretation methods build on established approaches such as permutation-based variable importance, partial dependence, individual conditional expectation, accumulated local effects, SHAP, and LIME; see Friedman (2001) <doi:10.1214/aos/1013203451>, Goldstein et al. (2015) <doi:10.1080/10618600.2014.907095>, Apley and Zhu (2020) <doi:10.1111/rssb.12377>, Lundberg and Lee (2017) <doi:10.48550/arXiv.1705.07874>, and Ribeiro et al. (2016) <doi:10.48550/arXiv.1602.04938>. The framework is intentionally opinionated: preprocessing is expected to occur outside the modeling step, and the API emphasizes explicit inputs, consistent object contracts, and compact interfaces rather than feature-by-feature competition with larger machine learning ecosystems. Plug-in g-computation follows Naimi, Cole, and Kennedy (2016) <doi:10.1093/ije/dyw323>.

This package provides a new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.

This software package provides Cox survival analysis for high-dimensional and multiblock datasets. It encompasses a suite of functions dedicated from the classical Cox regression to newest analysis, including Cox proportional hazards model, Stepwise Cox regression, and Elastic-Net Cox regression, Sparse Partial Least Squares Cox regression (sPLS-COX) incorporating three distinct strategies, and two Multiblock-PLS Cox regression (MB-sPLS-COX) methods. This tool is designed to adeptly handle high-dimensional data, and provides tools for cross-validation, plot generation, and additional resources for interpreting results. While references are available within the corresponding functions, key literature is mentioned below. Terry M Therneau (2024) <https://CRAN.R-project.org/package=survival>, Noah Simon et al. (2011) <doi:10.18637/jss.v039.i05>, Philippe Bastien et al. (2005) <doi:10.1016/j.csda.2004.02.005>, Philippe Bastien (2008) <doi:10.1016/j.chemolab.2007.09.009>, Philippe Bastien et al. (2014) <doi:10.1093/bioinformatics/btu660>, Kassu Mehari Beyene and Anouar El Ghouch (2020) <doi:10.1002/sim.8671>, Florian Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>.

This package provides functions for visualization and decomposition in hierarchical linear models (HLM) for applications in education, psychology, and the social sciences. Includes variance decomposition for two-level and three-level data structures following Snijders and Bosker (2012, ISBN:9781849202015), intraclass correlation (ICC) estimation and design effect computation as described in Shrout and Fleiss (1979) <doi:10.1037/0033-2909.86.2.420>, and contextual effect decomposition via the Mundlak (1978) <doi:10.2307/1913646> specification distinguishing within- and between-cluster components, with the uncertainty of the contextual contrast obtained from the full fixed-effect covariance matrix. Teaching displays cover simulated intraclass correlations, partial pooling of cluster means, random-slope heterogeneity, and cross-level interaction with an observed Level-2 moderator, following Hofmann and Gavin (1998) <doi:10.1177/014920639802400504> and Hamaker and Muthen (2020) <doi:10.1037/met0000239>. Multilevel models are estimated using lme4 (Bates et al., 2015 <doi:10.18637/jss.v067.i01>). An optional Shiny application enables interactive exploration of model components and parameter variation. The implementation follows the multilevel modeling framework of Raudenbush and Bryk (2002, ISBN:9780761919049).

The Ontario Marginalization Index is a socioeconomic model that is built on Statistics Canada census data. The model consists of four dimensions: In 2021, these dimensions were updated to "Material Resources" (previously called "Material Deprivation"), "Households and Dwellings" (previously called "Residential Instability"), "Age and Labour Force" (previously called "Dependency"), and "Racialized and Newcomer Populations" (previously called "Ethnic Concentration"). This update reflects a movement away from deficit-based language. 2021 data will load with these new dimension names, wheras 2011 and 2016 data will load with the historical dimension names. Each of these dimensions are imported for a variety of geographic levels (DA, CD, etc.) for the 2021, 2011 and 2016 administrations of the census. These data sets contribute to community analysis of equity with respect to Ontario's Anti-Racism Act. The Ontario Marginalization Index data is retrieved from the Public Health Ontario website: <https://www.publichealthontario.ca/en/data-and-analysis/health-equity/ontario-marginalization-index>. The shapefile data is retrieved from the Statistics Canada website: <https://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-eng.cfm>.

This package provides a tool that "multiply imputes" missing data in a single cross-section (such as a survey), from a time series (like variables collected for each year in a country), or from a time-series-cross-sectional data set (such as collected by years for each of several countries). Amelia II implements our bootstrapping-based algorithm that gives essentially the same answers as the standard IP or EMis approaches, is usually considerably faster than existing approaches and can handle many more variables. Unlike Amelia I and other statistically rigorous imputation software, it virtually never crashes (but please let us know if you find to the contrary!). The program also generalizes existing approaches by allowing for trends in time series across observations within a cross-sectional unit, as well as priors that allow experts to incorporate beliefs they have about the values of missing cells in their data. Amelia II also includes useful diagnostics of the fit of multiple imputation models. The program works from the R command line or via a graphical user interface that does not require users to know R.

Geospatial data computation is parallelized by grid, hierarchy, or raster files. Based on future (Bengtsson, 2024 <doi:10.32614/CRAN.package.future>) and mirai (Gao et al., 2025 <doi:10.32614/CRAN.package.mirai>) parallel back-ends, terra (Hijmans et al., 2025 <doi:10.32614/CRAN.package.terra>) and sf (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) functions as well as convenience functions in the package can be distributed over multiple threads. The simplest way of parallelizing generic geospatial computation is to start from par_pad_*() functions to par_grid(), par_hierarchy(), or par_multirasters() functions. Virtually any functions accepting classes in terra or sf packages can be used in the three parallelization functions. A common raster-vector overlay operation is provided as a function extract_at(), which uses exactextractr (Baston, 2023 <doi:10.32614/CRAN.package.exactextractr>), with options for kernel weights for summarizing raster values at vector geometries. Other convenience functions for vector-vector operations including simple areal interpolation (summarize_aw()) and summation of exponentially decaying weights (summarize_sedc()) are also provided.

Detects concept drift and data drift in streams produced by deployed machine learning models, using a tidy interface that composes with the tidymodels ecosystem. Detectors are specified, fitted on a baseline period, and advanced over new batches of observations, returning tibbles annotated with warning and drift flags. A catalogue of 22 sequential drift detectors is provided. Error-based methods include the Drift Detection Method (DDM) of Gama et al. (2004) <doi:10.1007/978-3-540-28645-5_29>, the Early Drift Detection Method (EDDM) of Baena-Garcia et al. (2006), the Hoeffding's inequality based Drift Detection Methods (HDDM) of Frias-Blanco et al. (2015) <doi:10.1109/TKDE.2014.2345382>, and the Exponentially Weighted Moving Average (EWMA) chart of Ross et al. (2012) <doi:10.1016/j.patrec.2011.08.019>. Distribution-based methods include Adaptive Windowing (ADWIN) of Bifet and Gavalda (2007) <doi:10.1137/1.9781611972771.42>, Kolmogorov-Smirnov Windowing (KSWIN) of Raab et al. (2020) <doi:10.1016/j.neucom.2019.11.111>, and the Page-Hinkley test of Page (1954) <doi:10.1093/biomet/41.1-2.100>.

It is a computer tool to estimate the item-sum score's reliability (composite reliability, CR) in multidimensional scales with overlapping items. An item that measures more than one domain construct is called an overlapping item. The estimation is based on factor models allowing unlimited cross-factor loadings such as exploratory structural equation modeling (ESEM) and Bayesian structural equation modeling (BSEM). The factor models include correlated-factor models and bi-factor models. Specifically for bi-factor models, a type of hierarchical factor model, the package estimates the CR hierarchical subscale/hierarchy and CR subscale/scale total. The CR estimator Omega-generic was proposed by Mai, Srivastava, and Krull (2021) <https://whova.com/embedded/subsession/enars_202103/1450751/1452993/>. The current version can only handle continuous data. Yujiao Mai contributes to the algorithms, R programming, and application example. Deo Kumar Srivastava contributes to the algorithms and the application example. Kevin R. Krull contributes to the application example. The package OmegaG was sponsored by American Lebanese Syrian Associated Charities (ALSAC). However, the contents of OmegaG do not necessarily represent the policy of the ALSAC.

Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) <doi:10.1002/sim.8315>, the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) <doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2023) <doi:10.1080/00224065.2022.2081104>, and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) <doi:10.1080/24725854.2019.1696496>.

Estimates the local average treatment effect (LATE) and the local average treatment effect on the treated (LATT) using observational data with a binary instrument, implementing the complete estimator suite of Sloczynski, Uysal, and Wooldridge: the doubly robust estimators of Sloczynski, Uysal, and Wooldridge (2022) <doi:10.48550/arXiv.2208.01300> -- inverse probability weighted regression adjustment (IPWRA), inverse probability weighting (IPW), augmented inverse probability weighting (AIPW), and regression adjustment (RA) -- and the Abadie-kappa weighting estimators of Sloczynski, Uysal, and Wooldridge (2025) <doi:10.1080/07350015.2024.2332763>. Supports linear, logistic, probit, Poisson, and fractional (fractional-logit and fractional-probit) outcome and treatment models, and instrument propensity scores estimated by maximum likelihood, covariate balancing (CBPS), or inverse probability tilting (IPT). Standard errors are computed jointly for all estimation stages by stacking the moment conditions of every model into a single M-estimation system; weak-instrument-robust Fieller confidence sets, cluster-aware bootstrap inference, design diagnostics, and a doubly robust Hausman-type test of unconfoundedness are included. Estimates and standard errors are validated against the authors Stata commands drlate (Statistical Software Components S459708) and kappalate (S459257).

Easily export R graphs and statistical output to Microsoft Office / LibreOffice', Latex and HTML Documents, using sensible defaults that result in publication-quality output with simple, straightforward commands. Output to Microsoft Office is in editable DrawingML vector format for graphs, and can use corporate template documents for styling. This enables the production of standardized reports and also allows for manual tidy-up of the layout of R graphs in Powerpoint before final publication. Export of graphs is flexible, and functions enable the currently showing R graph or the currently showing R stats object to be exported, but also allow the graphical or tabular output to be passed as objects. The package relies on package officer for export to Office documents,and output files are also fully compatible with LibreOffice'. Base R', ggplot2 and lattice plots are supported, as well as a wide variety of R stats objects, via wrappers to xtable(), broom::tidy() and stargazer(), including aov(), lm(), glm(), lme(), glmnet() and coxph() as well as matrices and data frames and many more...

Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

In a typical protein labelling procedure, proteins are chemically tagged with a functional group, usually at specific sites, then digested into peptides, which are then analyzed using matrix-assisted laser desorption ionization - time of flight mass spectrometry (MALDI-TOF MS) to generate peptide fingerprint. Relative to the control, peptides that are heavier by the mass of the labelling group are informative for sequence determination. Searching for peptides with such mass shifts, however, can be difficult. This package, designed to tackle this inconvenience, takes as input the mass list of two or multiple MALDI-TOF MS mass lists, and makes pairwise comparisons between the labeled groups vs. control, and restores centroid mass spectra with highlighted peaks of interest for easier visual examination. Particularly, peaks differentiated by the mass of the labelling group are defined as a â pairâ , those with equal masses as a â matchâ , and all the other peaks as a â mismatchâ .For more bioanalytical background information, refer to following publications: Jingjing Deng (2015) <doi:10.1007/978-1-4939-2550-6_19>; Elizabeth Chang (2016) <doi:10.7171/jbt.16-2702-002>.

This package provides the Saylac Shiny application for multidisciplinary analysis of global, national, and regional indicators observed across places and years. SAYLAC abbreviates Spatial Analysis of Yearly, Longitudinal, and Areal Change. The platform supports spatial diagnostics, longitudinal data exploration, time-series diagnostics, comparative forecasting, and automated reporting for development, health, education, economic, environmental, and social indicators. The application implements three connected modules: Spatial Analysis Workflow for exploratory spatial data analysis, Single Model Diagnostics for stationarity testing, trend diagnostics, and comparative forecasting, and Spatial Uncertainty and Reporting Analysis Dashboard for forecast mapping, spatial clustering, and reporting. The application supports choropleth mapping, Moran's I, Geary's C, Local Indicators of Spatial Association, Getis-Ord Gi star statistics, spatial correlograms, Theil-Sen trend estimation, Mann-Kendall testing, Autoregressive Integrated Moving Average models, Exponential Smoothing State Space models, neural network autoregression, BATS, TBATS, theta forecasting, symmetric mean absolute percentage error model comparison, and report generation. The platform was first applied in Touryare and Mohamud (2026) <doi:10.1007/s43621-026-04022-x> for integrated spatial-temporal forecasting of educational attainment in Eastern Africa toward Sustainable Development Goal 4.