RStudio addins and Shiny modules for descriptive statistics, regression and survival analysis.

This package implements delete-d jackknife resampling for robust statistical estimation. The package provides both weighted (HC3-adjusted) and unweighted versions of jackknife estimation, with parallel computation support. Suitable for biomedical research and other fields requiring robust variance estimation.

This package provides a graphics device that translates R plotting operations into JSON and streams them over a local connection to an external display application. The device acts as a pure recorder with no rendering dependencies; all rendering occurs in that application (e.g. a VS Code extension or a web browser). Official display applications are available from the project homepage.

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the jagstargets R package is leverages targets and R2jags to ease this burden. jagstargets makes it super easy to set up scalable JAGS pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than targets alone. For the underlying methodology, please refer to the documentation of targets <doi:10.21105/joss.02959> and JAGS (Plummer 2003) <https://www.r-project.org/conferences/DSC-2003/Proceedings/Plummer.pdf>.

Bayesian methods for estimating developmental age from ordinal dental data. For an explanation of the model used, see Konigsberg (2015) <doi:10.3109/03014460.2015.1045430>. For details on the conditional correlation correction, see Sgheiza (2022) <doi:10.1016/j.forsciint.2021.111135>. Dental scoring is based on Moorrees, Fanning, and Hunt (1963) <doi:10.1177/00220345630420062701>.

An R-safe profile of RFC 8259 JSON: parsing and generation backed by the Jansson C library, linked as a system library where one is available and compiled from the bundled sources otherwise. The parser rejects, with classed conditions carrying line, column, and byte position: malformed or truncated input, trailing content, duplicate object keys at any depth, invalid UTF-8, escapes encoding a null character, reals overflowing double, and integer literals whose magnitude exceeds 2^53, the range within which a double represents every integer exactly. Number literals with a fraction or exponent convert by ordinary correctly rounded IEEE 754 double conversion. Objects decode to named lists in key order, arrays to unnamed lists, and scalars to length-one vectors. The encoder maps named lists to objects in insertion order, unnamed lists to arrays, guarantees that every finite double, signed zero included, round-trips to the exact same value (whole-number doubles are written as integers), and refuses values with no faithful JSON representation (NA, NaN, infinities, named atomic vectors, classed objects) instead of guessing. No R package dependencies.

The age is estimated by calculating the Dirichlet Normal Energy (DNE) on the whole auricular surface and the apex of the auricular surface. It involves three estimation methods: principal component discriminant analysis (PCQDA), and principal component logistic regression analysis (PCLR) methods, principal component regression analysis with Southeast Asian (A_PCR), and principal component regression analysis with multipopulation (M_PCR). The package is created with the data from the Louis Lopes Collection in Lisbon, the 21st Century Identified Human Remains Collection in Coimbra, and the CAL Milano Cemetery Skeletal Collection in Milan, and the skeletal collection at Khon Kaen University (KKU) Human Skeletal Research Centre (HSRC), housed in the Department of Anatomy in the Faculty of Medicine at KKU in Khon Kaen.

Jade Lizard and Reverse Jade Lizard Option Strategies are presented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability. Russell A. Stultz (â The option strategy desk reference: an essential reference for option traders (First edition.)â , 2019, ISBN: 9781949443912).

This package provides method used to check whether data have outlier in efficiency measurement of big samples with data envelopment analysis (DEA). In this jackstrap method, the package provides two criteria to define outliers: heaviside and k-s test. The technique was developed by Sousa and Stosic (2005) "Technical Efficiency of the Brazilian Municipalities: Correcting Nonparametric Frontier Measurements for Outliers." <doi:10.1007/s11123-005-4702-4>.

This package performs Joins and Minus Queries on Excel Files fulljoinXL() Merges all rows of 2 Excel files based upon a common column in the files. innerjoinXL() Merges all rows from base file and join file when the join condition is met. leftjoinXL() Merges all rows from the base file, and all rows from the join file if the join condition is met. rightjoinXL() Merges all rows from the join file, and all rows from the base file if the join condition is met. minusXL() Performs 2 operations source-minus-target and target-minus-source If the files are identical all output files will be empty. Choose two Excel files via a dialog box, and then follow prompts at the console to choose a base or source file and columns to merge or minus on.

This package provides methods to perform Joint graph Regularized Single-Cell Kullback-Leibler Sparse Non-negative Matrix Factorization ('jrSiCKLSNMF', pronounced "junior sickles NMF") on quality controlled single-cell multimodal omics count data. jrSiCKLSNMF specifically deals with dual-assay scRNA-seq and scATAC-seq data. This package contains functions to extract meaningful latent factors that are shared across omics modalities. These factors enable accurate cell-type clustering and facilitate visualizations. Methods for pre-processing, clustering, and mini-batch updates and other adaptations for larger datasets are also included. For further details on the methods used in this package please see Ellis, Roy, and Datta (2023) <doi:10.3389/fgene.2023.1179439>.

This package provides a collection of popular/useful JavaScript utilities, including the terser minifier, sass compiler, typescript transpiler, and more.

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.

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>.

The function jskm() creates publication quality Kaplan-Meier plot with at risk tables below. svyjskm() provides plot for weighted Kaplan-Meier estimator.

This package provides a highly configurable jQuery plugin offering a simple interface to create complex queries/filters in Shiny'. The outputted rules can easily be parsed into a set of R and/or SQL queries and used to filter data. Custom parsing of the rules is also supported. For more information about jQuery QueryBuilder see <https://querybuilder.js.org/>.

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.

This package performs a permutation test on the difference between two location parameters, a permutation correlation test, a permutation F-test, the Siegel-Tukey test, a ratio mean deviance test. Also performs some graphing techniques, such as for confidence intervals, vector addition, and Fourier analysis; and includes functions related to the Laplace (double exponential) and triangular distributions. Performs power calculations for the binomial test.

The jscore() function in the package calculates the J-Score metric between two clustering assignments. The score is designed to address some problems with existing common metrics such as problem of matching. The details of J-score is described in Ahmadinejad and Liu. (2021) <arXiv:2109.01306>.

Estimate risk caused by two extreme and dependent forcing variables using bivariate extreme value models as described in Zheng, Westra, and Sisson (2013) <doi:10.1016/j.jhydrol.2013.09.054>; Zheng, Westra and Leonard (2014) <doi:10.1002/2013WR014616>; Zheng, Leonard and Westra (2015) <doi:10.2166/hydro.2015.052>.

Maximum likelihood estimation for the semiparametric joint modeling of survival and longitudinal data. Refer to the Journal of Statistical Software article: <doi:10.18637/jss.v093.i02>.

This package provides diagnostic tools for understanding and debugging data frame joins. Analyzes key columns before joining to detect duplicates, mismatches, encoding issues, and other common problems. Explains unexpected row count changes and provides safe join wrappers with cardinality enforcement. Concepts and diagnostics build on tidy data principles as described in Wickham (2014) <doi:10.18637/jss.v059.i10>.

Generates interactive Jellyfish plots to visualize spatiotemporal tumor evolution by integrating sample and phylogenetic trees into a unified plot. This approach provides an intuitive way to analyze tumor heterogeneity and evolution over time and across anatomical locations. The Jellyfish plot visualization design was first introduced by Lahtinen, Lavikka, et al. (2023, <doi:10.1016/j.ccell.2023.04.017>). This package also supports visualizing ClonEvol results, a tool developed by Dang, et al. (2017, <doi:10.1093/annonc/mdx517>), for analyzing clonal evolution from multi-sample sequencing data. The clonevol package is not available on CRAN but can be installed from its GitHub repository (<https://github.com/hdng/clonevol>).

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.