Quantitative genetics tool supporting the modelling of multivariate genetic variance structures in quantitative data. It allows fitting different models through multivariate genetic-relationship-matrix (GRM) structural equation modelling (SEM) in unrelated individuals, using a maximum likelihood approach. Specifically, it combines genome-wide genotyping information, as captured by GRMs, with twin-research-based SEM techniques, St Pourcain et al. (2017) <doi:10.1016/j.biopsych.2017.09.020>, Shapland et al. (2020) <doi:10.1101/2020.08.14.251199>.

Conducts causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability.

Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.

This package provides ggplot2 extensions for creating skewed boxplots using several statistical methods (Kimber, 1990 <doi:10.2307/2347808>; Hubert and Vandervieren, 2008 <doi:10.1016/j.csda.2007.11.008>; Adil et al., 2015 <doi:10.18187/pjsor.v11i1.500>; Babura et al., 2017 <doi:10.1063/1.4982872>; Walker et al., 2018 <doi:10.1080/00031305.2018.1448891>). The package implements custom statistical transformations and geometries to visualize data distributions with an emphasis on skewness.

Facilitates the post-Genome Wide Association Studies (GWAS) and Quantitative Trait Loci (QTL) analysis of identifying candidate genes within user-defined search window, based on the identified Single Nucleotide Polymorphisms (SNPs) as given by Mazumder AK (2024) <doi:10.1038/s41598-024-66903-3>. It supports candidate gene analysis for wheat and rice. Just import your GWAS result as explained in the sample_data file and the function does all the manual search and retrieve candidate genes for you, while exporting the results into ready-to-use output.

This package provides tools for systematically exploring large quantities of temporal data across cyclic temporal granularities (deconstructions of time) by visualizing probability distributions. Cyclic time granularities can be circular, quasi-circular or aperiodic. gravitas computes cyclic single-order-up or multiple-order-up granularities, check the feasibility of creating plots for any two cyclic granularities and recommend probability distributions plots for exploring periodicity in the data.

Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models as described in Bilodeau, Lafaye De Micheaux and Mahdi (2015) <doi:10.18637/jss.v065.i01>.

This package provides a comprehensive suite of helper functions designed to facilitate the analysis of genomic annotations from the GENCODE database <https://www.gencodegenes.org/>, supporting both human and mouse genomes. This toolkit enables users to extract, filter, and analyze a wide range of annotation features including genes, transcripts, exons, and introns across different GENCODE releases. It provides functionality for cross-version comparisons, allowing researchers to systematically track annotation updates, structural changes, and feature-level differences between releases. In addition, the package can generate high-quality FASTA files containing donor and acceptor splice site motifs, which are formatted for direct input into the MaxEntScan tool (Yeo and Burge, 2004 <doi:10.1089/1066527041410418>), enabling accurate calculation of splice site strength scores.

The goal of this package is to translate between different languages without any Google API authentication which is pain and you must pay for the key, This package is free and lightweight.

Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically. V. Picheny, M. Binois, A. Habbal (2018) <doi:10.1007/s10898-018-0688-0>. M. Binois, V. Picheny, P. Taillandier, A. Habbal (2020) <doi:10.48550/arXiv.1902.06565>.

This package provides a ggplot2 based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance, together with variable vectors showing how the data variables relate to this space. It provides a replacement for stats::biplot(), but with many enhancements to control the analysis and graphical display. It implements biplot and scree plot methods which can be used with the results of prcomp(), princomp(), FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using ggplot2 techniques.

This package provides functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.

Designed to customize ggplot graphics according to the institutional identity of the University of Ljubljana.

This package provides diagnostic graphic tools for GLMs, beta-binomial regression model (estimated by VGAM package), beta regression model (estimated by betareg package) and negative binomial regression model (estimated by MASS package). Since most of functions implemented in glmxdiag already exist in other packages, the aim is to provide the user unique functions that work on almost all regression models previously specified. Details about some of the implemented functions can be found in Brown (1992) <doi:10.2307/2347617>, Dunn and Smyth (1996) <doi:10.2307/1390802>, O'Hara Hines and Carter (1993) <doi:10.2307/2347405>, Wang (1985) <doi:10.2307/1269708>.

Access Google Cloud machine learning APIs for text and speech tasks. Use the Cloud Translation API for text detection and translation, the Natural Language API to analyze sentiment, entities, and syntax, the Cloud Speech API to transcribe audio to text, and the Cloud Text-to-Speech API to synthesize text into audio files.

Fits weighted quantile sum (WQS) regressions for one or more chemical groups with continuous or binary outcomes. Wheeler D, Czarnota J.(2016) <doi:10.1289/isee.2016.4698>.

This package provides a ggplot2 extension that adds specialised arrow geometry layers. It offers more arrow options than the standard grid arrows that are built-in many line-based geom layers.

This package provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.

Identifies biomarkers that exhibit differential response dynamics by time across groups and estimates kinetic properties of biomarkers.

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.

This package provides tools implementing an automated version of the graphic double integration technique (GDI) for volume implementation, and some other related utilities for paleontological image-analysis. GDI was first employed by Jerison (1973) <ISBN:9780323141086> and Hurlburt (1999) <doi:10.1080/02724634.1999.10011145> and is primarily used for volume or mass estimation of (extinct) animals. The package gdi aims to make this technique as convenient and versatile as possible. The core functions of gdi provide utilities for automatically measuring diameters from digital silhouettes provided as image files and calculating volume via graphic double integration with simple elliptical, superelliptical (following Motani 2001 <doi:10.1666/0094-8373(2001)027%3C0735:EBMFST%3E2.0.CO;2>) or complex cross-sectional geometries (see also Zhao 2024 <doi:10.7717/peerj.17479>). Additionally, the package provides functions for estimating the center of mass position (COM), the moment of inertia (I) for 3D shapes and the second moment of area (Ix, Iy, Iz) of 2D cross-sections, as well as for the visualization of results.

This package performs variable selection with data from Genome-wide association studies (GWAS), or other high-dimensional data with continuous, binary or survival outcomes, combining in an iterative framework the computational efficiency of the structured screen-and-select variable selection strategy based on some association learning and the parsimonious uncertainty quantification provided by the use of non-local priors (see Sanyal et al., 2019 <DOI:10.1093/bioinformatics/bty472>).

Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values. Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.

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.