Identifying disease-associated significant SNPs using clustering approach. This package is implementation of method proposed in Xu et al (2019) <DOI:10.1038/s41598-019-50229-6>.

Allows get address and port of the free proxy server, from one of two services <http://gimmeproxy.com/> or <https://getproxylist.com/>. And it's easy to redirect your Internet connection through a proxy server.

Writes SAS code to get predicted values from every tree of a gbm.object.

Organize a so-called ragged array as generalized arrays, which is simply an array with sub-dimensions denoting the subdivision of dimensions (grouping of members within dimensions). By the margins (names of dimensions and sub-dimensions) in generalized arrays, operators and utility functions provided in this package automatically match the margins, doing map-reduce style parallel computation along margins. Generalized arrays are also cooperative to R's native functions that work on simple arrays.

This package provides a suite of custom R Markdown formats and templates for authoring web pages styled with the GOV.UK Design System.

The goal of gnonadd is to simplify workflows in the analysis of non-additive effects of sequence variants. This includes variance effects (Ivarsdottir et. al (2017) <doi:10.1038/ng.3928>), correlation effects, interaction effects and dominance effects. The package also includes convenience functions for visualization.

An intuitive interface to simulate (1) superimposed (marked) point patterns with vectorized parameterization of random point pattern and distribution of marks; and (2) grouped hyper data frame based on population parameters and subject-specific random effects.

Design and analysis of group sequential designs for negative binomial outcomes, as described by T Mütze, E Glimm, H Schmidli, T Friede (2018) <doi:10.1177/0962280218773115>.

You can use this function to easily draw a combined histogram and restricted cubic spline. The function draws the graph through ggplot2'. RCS fitting requires the use of the rcs() function of the rms package. Can fit cox regression, logistic regression. This method was described by Per Kragh (2003) <doi:10.1002/sim.1497>.

Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.

Graphical tools and goodness-of-fit tests for right-censored data: 1. Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests, which use the empirical distribution function for complete data and are extended for right-censored data. 2. Generalized chi-squared-type test, which is based on the squared differences between observed and expected counts using random cells with right-censored data. 3. A series of graphical tools such as probability or cumulative hazard plots to guide the decision about the most suitable parametric model for the data. These functions share several features as they can handle both complete and right-censored data, and they provide parameter estimates for the distributions under study.

An interface for retrieving and displaying the information returned online by Google Trends is provided. Trends (number of hits) over the time as well as geographic representation of the results can be displayed.

This package implements readers and writers for file formats associated with genetics data. Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations. Other functions are readr wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files). There are also make functions for FAM and BIM tables with default values to go with simulated genotype data.

Estimate gender from names in Spanish and Portuguese. Works with vectors and dataframes. The estimation works not only for first names but also full names. The package relies on a compilation of common names with it's most frequent associated gender in both languages which are used as look up tables for gender inference.

Facilitate reporting for regression and correlation modeling, hypothesis testing, variance analysis, outlier detection, and detailed descriptive statistics.

This package provides tools for decomposing Global Value Chain (GVC) participation and value-added trade. It implements the frameworks proposed by Borin and Mancini (2023) 10.1080/09535314.2022.2153221> for source-based and sink-based decompositions, and by Borin, Mancini, and Taglioni (2025) 10.1093/wber/lhaf017> for tripartite and output-based GVC measures.

Estimation, forecasting, and simulation of generalized autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013) <doi:10.1002/jae.1279> and Harvey (2013) <doi:10.1017/cbo9781139540933>. Model specification allows for various data types and distributions, different parametrizations, exogenous variables, joint and separate modeling of exogenous variables and dynamics, higher score and autoregressive orders, custom and unconditional initial values of time-varying parameters, fixed and bounded values of coefficients, and missing values. Model estimation is performed by the maximum likelihood method.

This package provides a novel statistical model to detect the joint genetic and dynamic gene-environment (GxE) interaction with continuous traits in genetic association studies. It uses varying-coefficient models to account for different GxE trajectories, regardless whether the relationship is linear or not. The package includes one function, GxEtest(), to test a single genetic variant (e.g., a single nucleotide polymorphism or SNP), and another function, GxEscreen(), to test for a set of genetic variants. The method involves a likelihood ratio test described in Crainiceanu, C. M., and Ruppert, D. (2004) <doi:10.1111/j.1467-9868.2004.00438.x>.

Firstly, both functions of the univariate Poisson dispersion index (DI) for count data and the univariate exponential variation index (VI) for nonnegative continuous data are performed. Next, other functions of univariate indexes such the binomial dispersion index (DIb), the negative binomial dispersion index (DInb) and the inverse Gaussian variation index (VIiG) are given. Finally, we are computed some multivariate versions of these functions such that the generalized dispersion index (GDI) with its marginal one (MDI) and the generalized variation index (GVI) with its marginal one (MVI) too.

Density, distribution function, quantile function and random generation for the Generalized Gamma proposed in Stacy, E. W. (1962) <doi:10.1214/aoms/1177704481>.

Package for Genetic Epidemiologic Methods Developed at MSKCC. It contains functions to calculate haplotype specific odds ratio and the power of two stage design for GWAS studies.

Enables calculation of image textures (Haralick 1973) <doi:10.1109/TSMC.1973.4309314> from grey-level co-occurrence matrices (GLCMs). Supports processing images that cannot fit in memory.

The function gggap() streamlines the creation of segments on the y-axis of ggplot2 plots which is otherwise not a trivial task to accomplish.

This package provides a collection of custom ggplot2'-based visualizations for data exploration and analysis. Each function handles data preprocessing and returns a object that can be further customized using standard ggplot2 syntax.