Duct tape the quanteda ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of quanteda.textmodels and provides a function to setup the Python environment to use the pretrained models from Hugging Face <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.

This package provides a Humanitarian Data Exchange (HDX) theme, color palettes, and scales for ggplot2 to allow users to easily follow the HDX visual design guide, including convenience functions for for loading and using the Source Sans 3 font.

Helps find meaningful patterns in complex genetic experiments. First gimap takes data from paired CRISPR (Clustered regularly interspaced short palindromic repeats) screens that has been pre-processed to counts table of paired gRNA (guide Ribonucleic Acid) reads. The input data will have cell counts for how well cells grow (or don't grow) when different genes or pairs of genes are disabled. The output of the gimap package is genetic interaction scores which are the distance between the observed CRISPR score and the expected CRISPR score. The expected CRISPR scores are what we expect for the CRISPR values to be for two unrelated genes. The further away an observed CRISPR score is from its expected score the more we suspect genetic interaction. The work in this package is based off of original research from the Alice Berger lab at Fred Hutchinson Cancer Center (2021) <doi:10.1016/j.celrep.2021.109597>.

To calculate the relative risk (RR) for the generalized additive model.

Individual-based simulations forward in time, simulating how patterns in ancestry along the genome change after admixture. Full description can be found in Janzen (2021) <doi:10.1111/2041-210X.13612>.

Create hexagonal heatmaps with ggplot2', using the size aesthetic to variably size each hexagon.

This package provides the standard operations for signal processing on graphs: graph Fourier transform, spectral graph wavelet transform, visualization tools. It also implements a data driven method for graph signal denoising/regression, for details see De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>. The package also provides an interface to the SuiteSparse Matrix Collection, <https://sparse.tamu.edu/>, a large and widely used set of sparse matrix benchmarks collected from a wide range of applications.

Grey zones locally occur in an agreement table due to the subjective evaluation of raters based on various factors such as not having uniform guidelines, the differences between the raters level of expertise or low variability among the level of the categorical variable. It is important to detect grey zones since they cause a negative bias in the estimate of the agreement level. This package provides a function for detecting the existence of grey zones in two-way inter-rater agreement tables (Demirhan and Yilmaz (2023) <doi:10.1186/s12874-022-01759-7>).

This package provides a function that reads in the GEO code of a gene expression dataset, retrieves its data from GEO, (optionally) retrieves the gene symbols of the dataset, and returns a simple dataframe table containing all the data. Platforms available: GPL11532, GPL23126, GPL6244, GPL8300, GPL80, GPL96, GPL570, GPL571, GPL20115, GPL1293, GPL6102, GPL6104, GPL6883, GPL6884, GPL13497, GPL14550, GPL17077, GPL6480. GEO: Gene Expression Omnibus. ID: identifier code. The GEO datasets are downloaded from the URL <https://ftp.ncbi.nlm.nih.gov/geo/series/>. More information can be found in the following manuscript: Davide Chicco, "geneExpressionFromGEO: an R package to facilitate data reading from Gene Expression Omnibus (GEO)". Microarray Data Analysis, Methods in Molecular Biology, volume 2401, chapter 12, pages 187-194, Springer Protocols, 2021, <doi:10.1007/978-1-0716-1839-4_12>.

Create network-style visualizations of pairwise relationships using custom edge glyphs built on top of ggplot2'. The package supports both statistical and non-statistical data and allows users to represent directed relationships. This enables clear, publication-ready graphics for exploring and communicating relational structures in a wide range of domains. The method was first used in Abu-Akel et al. (2021) <doi:10.1371/journal.pone.0245100>. Code is released under the MIT License; included datasets are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0).

Automates the process of adding, committing, and pushing changes to a git repository using commit messages generated by passing the git diff output to the OpenAI GPT-3.5 Turbo model (<https://platform.openai.com/docs/models/gpt-3>).

Implementation of the GTE (Group Technical Effects) model for single-cell data. GTE is a quantitative metric to assess batch effects for individual genes in single-cell data. For a single-cell dataset, the user can calculate the GTE value for individual features (such as genes), and then identify the highly batch-sensitive features. Removing these highly batch-sensitive features results in datasets with low batch effects.

This package provides a user-friendly, highly customizable R package for building horizon plots in the ggplot2 environment.

This package provides tools for working with Gustavo Niemeyer's geohash coordinate system, including API for interacting with other common R GIS libraries.

Likelihood-based boosting approaches for generalized mixed models are provided.

An extension of ggplot2 to provide quiver plots to visualise vector fields. This functionality is implemented using a geom to produce a new graphical layer, which allows aesthetic options. This layer can be overlaid on a map to improve visualisation of mapped data.

Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test if a sample follows Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.

Allows you to retrieve information from the Google Knowledge Graph API <https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and process it in R in various forms. The Knowledge Graph Search API lets you find entities in the Google Knowledge Graph'. The API uses standard schema.org types and is compliant with the JSON-LD specification.

Forest-based statistical estimation and inference. GRF provides non-parametric methods for heterogeneous treatment effects estimation (optionally using right-censored outcomes, multiple treatment arms or outcomes, or instrumental variables), as well as least-squares regression, quantile regression, and survival regression, all with support for missing covariates.

Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format.

Generating, evaluating, and selecting initialization strategies for Gaussian Mixture Models (GMMs), along with functions to run the Expectation-Maximization (EM) algorithm. Initialization methods are compared using log-likelihood, and the best-fitting model can be selected using BIC. Methods build on initialization strategies for finite mixture models described in Michael and Melnykov (2016) <doi:10.1007/s11634-016-0264-8> and Biernacki et al. (2003) <doi:10.1016/S0167-9473(02)00163-9>, and on the EM algorithm of Dempster et al. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>. Background on model-based clustering includes Fraley and Raftery (2002) <doi:10.1198/016214502760047131> and McLachlan and Peel (2000, ISBN:9780471006268).

This package provides a variety of multivariable data summary statistics and constructions have been proposed, either to generalize univariable analogs or to exploit multivariable properties. Notable among these are the bivariate peelings surveyed by Green (1981, ISBN:978-0-471-28039-2), the bag-and-bolster plots proposed by Rousseeuw &al (1999) <doi:10.1080/00031305.1999.10474494>, and the minimum spanning trees used by Jolliffe (2002) <doi:10.1007/b98835> to represent high-dimensional relationships among data in a low-dimensional plot. Additionally, biplots of singular value--decomposed tabular data, such as from principal components analysis, make use of vectors, calibrated axes, and other representations of variable elements to complement point markers for case elements; see Gabriel (1971) <doi:10.1093/biomet/58.3.453> and Gower & Harding (1988) <doi:10.1093/biomet/75.3.445> for original proposals. Because they treat the abscissa and ordinate as commensurate or the data elements themselves as point masses or unit vectors, these multivariable tools can be thought of as belonging to geometric data analysis; see Podani (2000, ISBN:90-5782-067-6) for techniques and applications and Le Roux & Rouanet (2005) <doi:10.1007/1-4020-2236-0> for foundations. gggda extends Wickham's (2010) <doi:10.1198/jcgs.2009.07098> layered grammar of graphics with statistical transformation ("stat") and geometric construction ("geom") layers for many of these tools, as well as convenience coordinate systems to emphasize intrinsic geometry of the data.

This package provides a collection of palettes and themes for ggplot2', offering a light, pastel aesthetic. Syntax follows the viridis package.

Imports time series data from the Quandl database <https://data.nasdaq.com/>. The package uses the json api at <https://data.nasdaq.com/search>, local caching ('memoise package) and the tidy format by default. Also allows queries of databases, allowing the user to see which time series are available for each database id. In short, it is an alternative to package Quandl', with faster data importation in the tidy/long format.