Test for association between the observed data and their estimated latent variables. The jackstraw package provides a resampling strategy and testing scheme to estimate statistical significance of association between the observed data and their latent variables. Depending on the data type and the analysis aim, the latent variables may be estimated by principal component analysis (PCA), factor analysis (FA), K-means clustering, and related unsupervised learning algorithms. The jackstraw methods learn over-fitting characteristics inherent in this circular analysis, where the observed data are used to estimate the latent variables and used again to test against that estimated latent variables. When latent variables are estimated by PCA, the jackstraw enables statistical testing for association between observed variables and latent variables, as estimated by low-dimensional principal components (PCs). This essentially leads to identifying variables that are significantly associated with PCs. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds coherent groups in high-dimensional data. The jackstraw estimates statistical significance of cluster membership, by testing association between data and cluster centers. Clustering membership can be improved by using the resulting jackstraw p-values and posterior inclusion probabilities (PIPs), with an application to unsupervised evaluation of cell identities in single cell RNA-seq (scRNA-seq).

This package provides a function allowing to normalize a JSON string, for example by adding double quotes around the keys when they are missing. Also provides RStudio addins for the same purpose.

An R package that implements the JICO algorithm [Wang, P., Wang, H., Li, Q., Shen, D., & Liu, Y. (2024). <Journal of Computational and Graphical Statistics, 33(3), 763-773>]. It aims at solving the multi-group regression problem. The algorithm decomposes the responses from multiple groups into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the covariates respectively.

This package provides a convenience tool to create HTML with inline styles using juicyjuice and markdown packages. It is particularly useful when working on a content management system (CMS) whose code editor eliminates style and link tags. The main use case of the package is the learning management system, Moodle'. Additional helper functions for teaching purposes are provided. Learn more about juicedown at <https://kenjisato.github.io/juicedown/>.

This package provides functions to justify alpha levels for statistical hypothesis tests by avoiding Lindley's paradox, or by minimizing or balancing error rates. For more information about the package please read the following: Maier & Lakens (2021) <doi:10.31234/osf.io/ts4r6>).

This package provides data about the possible adverse events/reactions resulting from being injected with a vaccine/experimental gene therapy. Currently, this data set only includes information from six reference sources. Refer to the CITATION.cff file for the complete citations of the reference sources. For information about vaccination$/immunization$ hazards, visit <https://www.questionuniverse.com/rethink.html#vaccine>, <https://www.ecoccs.com/healing.html#vaccines>, <https://www.questionuniverse.com/rethink_current_crisis.html#cov_vaccin>, and <https://www.questionuniverse.com/vaccination.html>.

An implementation of the Jaya optimization algorithm for both single-objective and multi-objective problems. Jaya is a population-based, gradient-free optimization algorithm capable of solving constrained and unconstrained optimization problems without hyperparameters. This package includes features such as multi-objective Pareto optimization, adaptive population adjustment, and early stopping. For further details, see R.V. Rao (2016) <doi:10.5267/j.ijiec.2015.8.004>.

This package creates interactive trees that can be included in Shiny apps and R markdown documents. A tree allows to represent hierarchical data (e.g. the contents of a directory). Similar to the shinyTree package but offers more features and options, such as the grid extension, restricting the drag-and-drop behavior, and settings for the search functionality. It is possible to attach some data to the nodes of a tree and then to get these data in Shiny when a node is selected. Also provides a Shiny gadget allowing to manipulate one or more folders, and a Shiny module allowing to navigate in the server side file system.

Approximate joint-inclusion probabilities in Unequal Probability Sampling, or compute Monte Carlo approximations of the first and second-order inclusion probabilities of a general sampling design as in Fattorini (2006) <doi:10.1093/biomet/93.2.269>.

This package provides model fitting, prediction, and plotting for joint models of longitudinal and multiple time-to-event data, including methods from Rizopoulos (2012) <doi:10.1201/b12208>. Useful for handling complex survival and longitudinal data in clinical research.

The Jalaali calendar, also known as the Persian or Solar Hijri calendar, is the official calendar of Iran and Afghanistan. It starts on Nowruz, the spring equinox, and follows an astronomical system for determining leap years. Each year consists of 365 or 366 days, divided into 12 months. This package provides functions for converting dates between the Jalaali and Gregorian calendars. The conversion calculations are based on the work of Kazimierz M. Borkowski (1996) (<doi:10.1007/BF00055188>), who used an analytical model of Earth's motion to compute equinoxes from AD 550 to 3800 and determine leap years based on Tehran time.

These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area in a conservation analysis. The algorithm is described in: Miranda-Esquivel, D (2016) <DOI:10.1007/978-3-319-22461-9_11>.

Analysis of repeated measurements and time-to-event data via random effects joint models. Fits the joint models proposed by Henderson and colleagues <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a single continuous repeated measure. The time-to-event data is modelled using a (cause-specific) Cox proportional hazards regression model with time-varying covariates. The longitudinal outcome is modelled using a linear mixed effects model. The association is captured by a latent Gaussian process. The model is estimated using am Expectation Maximization algorithm. Some plotting functions and the variogram are also included. This project is funded by the Medical Research Council (Grant numbers G0400615 and MR/M013227/1).

Uses the Jaccard similarity index to account for population structure in sequencing studies. This method was specifically designed to detect population stratification based on rare variants, hence it will be especially useful in rare variant analysis.

Scientific journal numeric formatting policies implemented in code. Emphasis on formatting mean/upper/lower sets of values to pasteable text for journal submission. For example c(2e6, 1e6, 3e6) becomes "2.00 million (1.00--3.00)". Lancet and Nature have built-in styles for rounding and punctuation marks. Users may extend journal styles arbitrarily. Four metrics are supported; proportions, percentage points, counts and rates. Magnitudes for all metrics are discovered automatically.

Computes the Jackknife Mutual Information (JMI) between two random vectors and provides the p-value for dependence tests. See Zeng, X., Xia, Y. and Tong, H. (2018) <doi:10.1073/pnas.1715593115>.

Create and customize interactive trees using the jQuery jsTree <https://www.jstree.com/> plugin library and the htmlwidgets package. These trees can be used directly from the R console, from RStudio', in Shiny apps and R Markdown documents.

This package provides a Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.

Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.

This package contains a selection of color palettes and ggplot2 themes designed by the package author.

This package provides a set of functions to compute the Hodrick-Prescott (HP) filter with automatically selected jumps. The original HP filter extracts a smooth trend from a time series, and our version allows for a small number of automatically identified jumps. See Maranzano and Pelagatti (2024) <doi:10.2139/ssrn.4896170> for details.

Read Japanese city codes (<https://www.e-stat.go.jp/municipalities/cities>) to get city and prefecture names, or convert to city codes at different points in time. In addition, it merges or splits wards of designated cities and gets all city codes at a specific point in time.

This package provides a small package containing functions to perform a joint calibration of totals and quantiles. The calibration for totals is based on Deville and Särndal (1992) <doi:10.1080/01621459.1992.10475217>, the calibration for quantiles is based on Harms and Duchesne (2006) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20060019255>. The package uses standard calibration via the survey', sampling or laeken packages. In addition, entropy balancing via the ebal package and empirical likelihood based on codes from Wu (2005) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2005002/article/9051-eng.pdf> can be used. See the paper by BerÄ sewicz and Szymkowiak (2023) for details <arXiv:2308.13281>.

Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018, <doi:10.1101/058107>) and Janzen (2022, <doi:10.1111/1755-0998.13519>).