Tests of goodness-of-fit based on a kernel smoothing of the data. References: Pavà a (2015) <doi:10.18637/jss.v066.c01>.

Interact with the Google Tag Manager API <https://developers.google.com/tag-platform/tag-manager/api/v2>, enabling scripted deployments and updates across multiple tags, triggers, variables and containers.

This package provides a function to retrieve the system timezone on Unix systems which has been found to find an answer when Sys.timezone() has failed. It is based on an answer by Duane McCully posted on StackOverflow', and adapted to be callable from R. The package also builds on Windows, but just returns NULL.

We provides functions that employ splines to estimate generalized partially linear single index models (GPLSIM), which extend the generalized linear models to include nonlinear effect for some predictors. Please see Y. (2017) at <doi:10.1007/s11222-016-9639-0> and Y., and R. (2002) at <doi:10.1198/016214502388618861> for more details.

This package provides functions for survey data including svydesign objects from the survey package that call ggplot2 to make bar charts, histograms, boxplots, and hexplots of survey data.

The getDTeval() function facilitates the translation of the original coding statement to an optimized form for improved runtime efficiency without compromising on the programmatic coding design. The function can either provide a translation of the coding statement, directly evaluate the translation to return a coding result, or provide both of these outputs.

Using simple input, this package creates plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.

Enhance a mice imputation workflow with visualizations for incomplete and/or imputed data. The plotting functions produce ggplot objects which may be easily manipulated or extended. Use ggmice to inspect missing data, develop imputation models, evaluate algorithmic convergence, or compare observed versus imputed data.

This package provides tools to download data from geoBoundaries <https://www.geoboundaries.org/>. Several administration levels available. See Runfola, D. et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLOS ONE 15(4): 1-9. <doi:10.1371/journal.pone.0231866>.

Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] <DOI: 10.1007/978-3-658-20540-9>. This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9> and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in <DOI: 10.1016/j.mex.2020.101093>.

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>.

Statistical testing procedures for detecting GxE (gene-environment) interactions. The main focus lies on GRSxE interaction tests that aim at detecting GxE interactions through GRS (genetic risk scores). Moreover, a novel testing procedure based on bagging and OOB (out-of-bag) predictions is implemented for incorporating all available observations at both GRS construction and GxE testing (Lau et al., 2023, <doi:10.1038/s41598-023-28172-4>).

An engine to facilitate the orchestration and execution of metadata-driven data management workflows, in compliance with FAIR (Findable, Accessible, Interoperable and Reusable) data management principles. By means of a pivot metadata model, relying on the DublinCore standard (<https://dublincore.org/>), a unique source of metadata can be used to operate multiple and inter-connected data management actions. Users can also customise their own workflows by creating specific actions but the library comes with a set of native actions targeting common geographic information and data management, in particular actions oriented to the publication on the web of metadata and data resources to provide standard discovery and access services. At first, default actions of the library were meant to focus on providing turn-key actions for geospatial (meta)data: 1) by creating manage geospatial (meta)data complying with ISO/TC211 (<https://committee.iso.org/home/tc211>) and OGC (<https://www.ogc.org/standards/>) geographic information standards (eg 19115/19119/19110/19139) and related best practices (eg. INSPIRE'); and 2) by facilitating extraction, reading and publishing of standard geospatial (meta)data within widely used software that compound a Spatial Data Infrastructure ('SDI'), including spatial databases (eg. PostGIS'), metadata catalogues (eg. GeoNetwork', CSW servers), data servers (eg. GeoServer'). The library was then extended to actions for other domains: 1) biodiversity (meta)data standard management including handling of EML metadata, and their management with DataOne servers, 2) in situ sensors, remote sensing and model outputs (meta)data standard management by handling part of CF conventions, NetCDF data format and OPeNDAP access protocol, and their management with Thredds servers, 3) generic / domain agnostic (meta)data standard managers ('DublinCore', DataCite'), to facilitate the publication of data within (meta)data repositories such as Zenodo (<https://zenodo.org>) or DataVerse (<https://dataverse.org/>). The execution of several actions will then allow to cross-reference (meta)data resources in each action performed, offering a way to bind resources between each other (eg. reference Zenodo DOI in GeoNetwork'/'GeoServer metadata, or vice versa reference GeoNetwork'/'GeoServer links in Zenodo or EML metadata). The use of standardized configuration files ('JSON or YAML formats) allow fully reproducible workflows to facilitate the work of data and information managers.

Retrieve datasets from the Global Data Lab website <https://globaldatalab.org> directly into R data frames. Functions are provided to reference available options (indicators, levels, countries, regions) as well.

This package provides a fully automated workflow for calibrating and analyzing light-level geolocation ('GLS') data from seabirds and other wildlife. The glscalibrator package auto-discovers birds from directory structures, automatically detects calibration periods from the first days of deployment, processes multiple individuals in batch mode, and generates standardized outputs including position estimates, diagnostic plots, and quality control metrics. Implements the established threshold workflow internally, following the methods described in SGAT (Wotherspoon et al. (2016) <https://github.com/SWotherspoon/SGAT>), GeoLight (Lisovski et al. (2012) <doi:10.1111/j.2041-210X.2012.00185.x>), and TwGeos (Lisovski et al. (2019) <https://github.com/slisovski/TwGeos>).

Calculates Agresti's generalized odds ratios. For a randomly selected pair of observations from two groups, calculates the odds that the second group will have a higher scoring outcome than that of the first group. Package provides hypothesis testing for if this odds ratio is significantly different to 1 (equal chance).

Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets, Schmid et al. (2016) <doi:10.1093/bioinformatics/btw030>.

Downloads and organizes datasets using BCB's API <https://www.bcb.gov.br/>. Offers options for caching with the memoise package and , multicore/multisession with furrr and format of output data (long/wide).

This package provides a collection of GIS (Geographic Information System) functions in R, created for use in Statistics Norway. The functions are primarily related to network analysis on the Norwegian road network.

Graph clustering using an agglomerative algorithm to maximize the integrated classification likelihood criterion and a mixture of stochastic block models. The method is described in the article "Model-based clustering of multiple networks with a hierarchical algorithm" by T. Rebafka (2022) <arXiv:2211.02314>.

This package performs Gamma regression, where both mean and shape parameters follows lineal regression structures.

Statistical functions to fit, validate and describe a Generalized Waring Regression Model (GWRM).

Tool for import and process data from Lattes curriculum platform (<http://lattes.cnpq.br/>). The Brazilian government keeps an extensive base of curricula for academics from all over the country, with over 5 million registrations. The academic life of the Brazilian researcher, or related to Brazilian universities, is documented in Lattes'. Some information that can be obtained: professional formation, research area, publications, academics advisories, projects, etc. getLattes package allows work with Lattes data exported to XML format.

Dynamically retrieve data from the web to render HTML tables on inspection in R Markdown HTML documents.