An user-friendly framework to preprocess raw item scores of questionnaires into factors or scores and standardize them. Standardization can be made either by their normalization in representative sample, or by import of premade scoring table.

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) for polytomous items. Package includes functions for generating a sequential relation table and a treegram to visualize the sequential relations between pairs of items.

Unofficial client for Sentry <https://sentry.io>, a self-hosted or cloud-based error-monitoring service. It will inform about errors in real-time, and includes integration with the Plumber package.

It allows to quickly perform permutation-based closed testing by sum-based global tests, and construct lower confidence bounds for the TDP, simultaneously over all subsets of hypotheses. As a main feature, it produces simultaneous lower confidence bounds for the proportion of active voxels in different clusters for fMRI cluster analysis. Details may be found in Vesely, Finos, and Goeman (2020) <arXiv:2102.11759>.

Catch advice for data-limited vertebrate and invertebrate fisheries managed by harvest slot limits using the SlotLim harvest control rule. The package accompanies the manuscript "SlotLim: catch advice for data-limited vertebrate and invertebrate fisheries managed by harvest slot limits" (Pritchard et al., in prep). Minimum data requirements: at least two consecutive years of catch data, lengthâ frequency distributions, and biomass or abundance indices (all from fishery-dependent sources); species-specific growth rate parameters (either von Bertalanffy, Gompertz, or Schnute); and either the natural mortality rate ('M') or the maximum observed age ('tmax'), from which M is estimated. The following functions have optional plotting capabilities that require ggplot2 installed: prop_target(), TBA(), SAM(), catch_advice(), catch_adjust(), and slotlim_once().

Sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition. The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468). The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al. The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al. The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test. The other functions in the package are internal functions.

An implementation of ranked sparsity methods, including penalized regression methods such as the sparsity-ranked lasso, its non-convex alternatives, and elastic net, as well as the sparsity-ranked Bayesian Information Criterion. As described in Peterson and Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked sparsity is a philosophy with methods primarily useful for variable selection in the presence of prior informational asymmetry, which occurs in the context of trying to perform variable selection in the presence of interactions and/or polynomials. Ultimately, this package attempts to facilitate dealing with cumbersome interactions and polynomials while not avoiding them entirely. Typically, models selected under ranked sparsity principles will also be more transparent, having fewer falsely selected interactions and polynomials than other methods.

Short and understandable commands that generate tabulated, formatted, and rounded survey estimates. Mostly a wrapper for the survey package (Lumley (2004) <doi:10.18637/jss.v009.i08> <https://CRAN.R-project.org/package=survey>) that identifies low-precision estimates using the National Center for Health Statistics (NCHS) presentation standards (Parker et al. (2017) <https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf>, Parker et al. (2023) <doi:10.15620/cdc:124368>).

This package provides functions for modeling Soil Organic Matter decomposition in terrestrial ecosystems with linear and nonlinear systems of differential equations. The package implements models according to the compartmental system representation described in Sierra and others (2012) <doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014) <doi:10.5194/gmd-7-1919-2014>.

The Swash-Backwash Model for the Single Epidemic Wave was developed by Cliff and Haggett (2006) <doi:10.1007/s10109-006-0027-8> to model the velocity of spread of infectious diseases across space. This package enables the calculation of the Swash-Backwash Model for user-supplied panel data on regional infections. The package provides additional functions for bootstrap confidence intervals, country comparison, visualization of results, and data management. Furthermore, it contains several functions for analysis and visualization of (spatial) infection data.

Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.

Conduct latent trajectory class analysis with longitudinal data. Our method supports longitudinal continuous, binary and count data. For more methodological details, please refer to Hart, K.R., Fei, T. and Hanfelt, J.J. (2020), Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics <doi:10.1111/biom.13366>.

This package provides a function sfc() to compute the substance flow with the input files --- "data" and "model". If sample.size is set more than 1, uncertainty analysis will be executed while the distributions and parameters are supplied in the file "data".

In population management, data come at more or less regular intervals over time in sampling batches (bouts) and decisions should be made with the minimum number of samples and as quickly as possible. This package provides tools to implement, produce charts with stop lines, summarize results and assess sequential analyses that test hypotheses about population sizes. Two approaches are included: the sequential test of Bayesian posterior probabilities (Rincon, D.F. et al. 2025 <doi:10.1111/2041-210X.70053>), and the sequential probability ratio test (Wald, A. 1945 <http://www.jstor.org/stable/2235829>).

Computes the sit coefficient between two vectors x and y, possibly all paired coefficients for a matrix. The reference for the methods implemented here is Zhang, Yilin, Canyi Chen, and Liping Zhu. 2022. "Sliced Independence Test." Statistica Sinica. <doi:10.5705/ss.202021.0203>. This package incorporates the Galton peas example.

Renders plots to a temporary image using the ragg graphics device and returns knitr::include_graphics() output. Optionally saves the image to a specified path. This helps ensure consistent appearance across interactive sessions, saved files, and knitted documents. For more details see Pedersen and Shemanarev (2025) <doi: 10.32614/CRAN.package.ragg>.

Preview spatial data as leaflet maps with minimal effort. smartmap is optimized for interactive use and distinguishes itself from similar packages because it does not need real spatial ('sp or sf') objects an input; instead, it tries to automatically coerce everything that looks like spatial data to sf objects or leaflet maps. It - for example - supports direct mapping of: a vector containing a single coordinate pair, a two column matrix, a data.frame with longitude and latitude columns, or the path or URL to a (possibly compressed) shapefile'.

Access Amazon Web Service Simple Storage Service ('S3') <https://aws.amazon.com/s3/> as if it were a file system. Interface based on the R package fs'.

Import, plot, and diagnose results from statistical catch-at-age models, used in fisheries stock assessment.

Print function signatures and find overly complicated code.

This package implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of fitted probabilities and percentiles. For multiple experts, a weighted linear pool can be calculated. Also includes functions for eliciting beliefs about population distributions; eliciting multivariate distributions using a Gaussian copula; eliciting a Dirichlet distribution; eliciting distributions for variance parameters in a random effects meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are included.

Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide.

Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) <doi:10.1214/088342304000000558> and Oja (2010) <doi:10.1007/978-1-4419-0468-3>.

This package implements stacked elastic net regression (Rauschenberger 2021 <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005 <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).