This package provides tools for estimating uncertainty in individual polygenic risk scores (PRSs) using both sampling-based and analytical methods, as well as the Best Linear Unbiased Estimator (BLUE). These methods quantify variability in PRS estimates for both binary and quantitative traits. See Henderson (1975) <doi:10.2307/2529430> for more details.

Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi:10.1016/j.jbankfin.2011.08.003> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .

Query for enriched data such as country, region, city, latitude & longitude, ZIP code, time zone, Autonomous System, Internet Service Provider, domain, net speed, International direct dialing (IDD) code, area code, weather station data, mobile data, elevation, usage type, address type, advertisement category, fraud score, and proxy data with an IP address. You can also query a list of hosted domain names for the IP address too. This package uses the IP2Location.io API to query this data. To get started with a free API key, sign up here <https://www.ip2location.io/sign-up?ref=1>.

This package provides a set of wrappers around rjags functions to run Bayesian analyses in JAGS (specifically, via libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the R2WinBUGS'/'R2OpenBUGS packages to allow easy switching between MCMC samplers.

This package provides tools for competing risks trials that allow simultaneous inference on recovery and mortality endpoints. Provides data preparation helpers, standard cumulative incidence estimators (restricted mean time gained/lost), and severity weighted extensions that integrate longitudinal ordinal outcomes to summarise treatment benefit. Methods follow Wen, Hu, and Wang (2023) Biometrics 79(3):1635-1645 <doi:10.1111/biom.13752>.

Miscellaneous tools and functions, including: generate descriptive statistics tables, format output, visualize relations among variables or check distributions, and generic functions for residual and model diagnostics.

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.

Leverages the yum package to implement a YAML ('YAML Ain't Markup Language', a human friendly standard for data serialization; see <https://yaml.org>) standard for documenting justifications, such as for decisions taken during the planning, execution and analysis of a study or during the development of a behavior change intervention as illustrated by Marques & Peters (2019) <doi:10.17605/osf.io/ndxha>. These justifications are both human- and machine-readable, facilitating efficient extraction and organisation.

This package provides a new class of Bayesian meta-analysis models that incorporates a model for internal and external validity bias. In this way, it is possible to combine studies of diverse quality and different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS).

In the observational study design stage, matching/weighting methods are conducted. However, when many background variables are present, the decision as to which variables to prioritize for matching/weighting is not trivial. Thus, the joint treatment-outcome variable importance plots are created to guide variable selection. The joint variable importance plots enhance variable comparisons via unadjusted bias curves derived under the omitted variable bias framework. The plots translate variable importance into recommended values for tuning parameters in existing methods. Post-matching and/or weighting plots can also be used to visualize and assess the quality of the observational study design. The method motivation and derivation is presented in "Prioritizing Variables for Observational Study Design using the Joint Variable Importance Plot" by Liao et al. (2024) <doi:10.1080/00031305.2024.2303419>. See the package paper by Liao and Pimentel (2024) <doi:10.21105/joss.06093> for a beginner friendly user introduction.

Estimates networks of conditional dependencies (Gaussian graphical models) from multiple classes of data (similar but not exactly, i.e. measurements on different equipment, in different locations or for various sub-types). Package also allows to generate simulation data and evaluate the performance. Implementation of the method described in Angelini, De Canditiis and Plaksienko (2022) <doi:10.3390/math10213983>.

Jointly estimates two-group means and covariances for matrix-variate data and calculates test statistics. This package implements the algorithms defined in Hornstein, Fan, Shedden, and Zhou (2018) <doi:10.1080/01621459.2018.1429275>.

Calculate statistical significance of Jaccard/Tanimoto similarity coefficients.

Generates image data for fractals (Julia and Mandelbrot sets) on the complex plane in the given region and resolution. Benoit B Mandelbrot (1982).

This package provides functions for grid square codes in Japan (<https://www.stat.go.jp/english/data/mesh/index.html>). Generates the grid square codes from longitude/latitude, geometries, and the grid square codes of different scales, and vice versa.

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.

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.

The free and open a statistical spreadsheet jamovi (<https://www.jamovi.org>) aims to make statistical analyses easy and intuitive. jamovi produces syntax that can directly be used in R (in connection with the R-package jmv'). Having import / export routines for the data files jamovi produces ('.omv') permits an easy transfer of data and analyses between jamovi and R.

The function get_parameters() is intended to be used within a docker container to read keyword arguments from a .json file automagically. A tool.yaml file contains specifications on these keyword arguments, which are then passed as input to containerized R tools in the [tool-runner framework](<https://github.com/hydrocode-de/tool-runner>). A template for a containerized R tool, which can be used as a basis for developing new tools, is available at the following URL: <https://github.com/VForWaTer/tool_template_r>.

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.

This package provides methods for fast segmentation of multivariate signals into piecewise constant profiles and for generating realistic copy-number profiles. A typical application is the joint segmentation of total DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.

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.

This is a set of simple utility functions to perform mutual conversion between the current Japanese calendar system that Wareki, the old Japanese calendar system that the Kyureki calendar and the Julian and Gregorian calendar. To calculate each calendar method, it converts to the Julian Day Number.

This package contains procedures to estimate the nine condensed Jacquard genetic identity coefficients (Jacquard, 1974) <doi:10.1007/978-3-642-88415-3> by constrained least squares (Graffelman et al., 2024) <doi:10.1101/2024.03.25.586682> and by the method of moments (Csuros, 2014) <doi:10.1016/j.tpb.2013.11.001>. These procedures require previous estimation of the allele frequencies. Functions are supplied that estimate relationship parameters that derive from the Jacquard coefficients, such as individual inbreeding coefficients and kinship coefficients.