Allows users to download and analyze official data on Brazil's federal budget through the SPARQL endpoint provided by the Integrated Budget and Planning System ('SIOP'). This package enables access to detailed information on budget allocations and expenditures of the federal government, making it easier to analyze and visualize these data. Technical information on the Brazilian federal budget is available (Portuguese only) at <https://www1.siop.planejamento.gov.br/mto/>. The SIOP endpoint is available at <https://www1.siop.planejamento.gov.br/sparql/>.

Download data from Brazil's Origin Destination Surveys. The package covers both data from household travel surveys, dictionaries of variables, and the spatial geometries of surveys conducted in different years and across various urban areas in Brazil. For some cities, the package will include enhanced versions of the data sets with variables "harmonized" across different years.

This package creates mock data for testing and package development for the Observational Medical Outcomes Partnership common data model. The package offers functions crafted with pipeline-friendly implementation, enabling users to effortlessly include only the necessary tables for their testing needs.

Users can build a single shiny app for exploring population characterization, population-level causal effect estimation, and patient-level prediction results generated via the R analyses packages in HADES (see <https://ohdsi.github.io/Hades/>). Learn more about OhdsiShinyAppBuilder at <https://ohdsi.github.io/OhdsiShinyAppBuilder/>.

Distance based bipartite matching using minimum cost flow, oriented to matching of treatment and control groups in observational studies ('Hansen and Klopfer 2006 <doi:10.1198/106186006X137047>). Routines are provided to generate distances from generalised linear models (propensity score matching), formulas giving variables on which to limit matched distances, stratified or exact matching directives, or calipers, alone or in combination.

This package provides programmatic access to the Open Experience Sampling Method ('openESM') database (<https://openesmdata.org>), a collection of harmonized experience sampling datasets. The package enables researchers to discover, download, and work with the datasets while ensuring proper citation and license compliance.

This package implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. The methods implemented include InterVA4 by Byass et al (2012) <doi:10.3402/gha.v5i0.19281>, InterVA5 by Byass at al (2019) <doi:10.1186/s12916-019-1333-6>, InSilicoVA by McCormick et al (2016) <doi:10.1080/01621459.2016.1152191>, NBC by Miasnikof et al (2015) <doi:10.1186/s12916-015-0521-2>, and a replication of Tariff method by James et al (2011) <doi:10.1186/1478-7954-9-31> and Serina, et al. (2015) <doi:10.1186/s12916-015-0527-9>. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics. The NBC method is implemented by the nbc4va package that can be installed from <https://github.com/rrwen/nbc4va>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist in the implementation of the Tariff method.

Data processing, visualisation and analysis of Limit Order Book event data.

This package provides a function to detect and trim outliers in Gaussian mixture model-based clustering using methods described in Clark and McNicholas (2024) <doi:10.1007/s00357-024-09473-3>.

Interface with the One Health VBD (vector-borne disease) Hub <https://vbdhub.org/> and related repositories (VectorByte <https://www.vectorbyte.org>, GBIF <https://www.gbif.org> and AREAdata <https://pearselab.github.io/areadata/>) directly to find, download, and subset vector-borne disease data.

This package provides a toolbox for working with public opinion data from Argentina. It facilitates access to microdata and the calculation of indicators of the Trust in Government Index (ICG), prepared by the Torcuato Di Tella University. Although we will try to document everything possible in English, by its very nature Spanish will be the main language. El paquete fue pensado como una caja de herramientas para el trabajo con datos de opinión pública de Argentina. El mismo facilita el acceso a los microdatos y el cálculos de indicadores del à ndice de Confianza en el Gobierno (ICG), elaborado por la Universidad Torcuato Di Tella.

Calculating the stability of random forest with certain numbers of trees. The non-linear relationship between stability and numbers of trees is described using a logistic regression model and used to estimate the optimal number of trees.

Search and extract data from the Organization for Economic Cooperation and Development (OECD).

This package provides an end-to-end workflow for integrative analysis of two omics layers using sparse canonical correlation analysis (sCCA), including sample alignment, feature selection, network edge construction, and visualization of gene-metabolite relationships. The underlying methods are based on penalized matrix decomposition and sparse CCA (Witten, Tibshirani and Hastie (2009) <doi:10.1093/biostatistics/kxp008>), with design principles inspired by multivariate integrative frameworks such as mixOmics (Rohart et al. (2017) <doi:10.1371/journal.pcbi.1005752>).

This package performs robust cluster analysis allowing for outliers and noise that cannot be fitted by any cluster. The data are modelled by a mixture of Gaussian distributions and a noise component, which is an improper uniform distribution covering the whole Euclidean space. Parameters are estimated by (pseudo) maximum likelihood. This is fitted by a EM-type algorithm. See Coretto and Hennig (2016) <doi:10.1080/01621459.2015.1100996>, and Coretto and Hennig (2017) <https://jmlr.org/papers/v18/16-382.html>.

This package provides an Interface to Open Collaboration Services OCS (<https://www.open-collaboration-services.org/>) REST API.

Empirical or simulated disease outbreak data, provided either as RData or as text files.

Density-based clustering methods are well adapted to the clustering of high-dimensional data and enable the discovery of core groups of various shapes despite large amounts of noise. This package provides a novel density-based cluster extraction method, OPTICS k-Xi, and a framework to compare k-Xi models using distance-based metrics to investigate datasets with unknown number of clusters. The vignette first introduces density-based algorithms with simulated datasets, then presents and evaluates the k-Xi cluster extraction method. Finally, the models comparison framework is described and experimented on 2 genetic datasets to identify groups and their discriminating features. The k-Xi algorithm is a novel OPTICS cluster extraction method that specifies directly the number of clusters and does not require fine-tuning of the steepness parameter as the OPTICS Xi method. Combined with a framework that compares models with varying parameters, the OPTICS k-Xi method can identify groups in noisy datasets with unknown number of clusters. Results on summarized genetic data of 1,200 patients are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>. A short video tutorial can be found at <https://www.youtube.com/watch?v=P2XAjqI5Lc4/>.

Designed to enhance data validation and management processes by employing a set of functions that read a set of rules from a CSV or Excel file and apply them to a dataset. Funded by the National Renewable Energy Laboratory and Possibility Lab, maintained by the Moore Institute for Plastic Pollution Research.

Access and Analyze Official Development Assistance (ODA) data using the OECD API <https://gitlab.algobank.oecd.org/public-documentation/dotstat-migration/-/raw/main/OECD_Data_API_documentation.pdf>. ODA data includes sovereign-level aid data such as key aggregates (DAC1), geographical distributions (DAC2A), project-level data (CRS), and multilateral contributions (Multisystem).

Obtaining Bayes Expected A Posteriori (EAP) individual score estimates based on linear and non-linear extended Exploratoy Factor Analysis solutions that include a correlated-residual structure.

This package provides functions to handle ordinal relations reflected within the feature space. Those function allow to search for ordinal relations in multi-class datasets. One can check whether proposed relations are reflected in a specific feature representation. Furthermore, it provides functions to filter, organize and further analyze those ordinal relations.

Estimates win ratio or Mann-Whitney parameter for two group comparisons using ordered composite endpoints with right censoring as described in Follmann, Fay, Hamasaki, and Evans (2020)<doi:10.1002/sim.7890>.

Primarily devoted to implementing the Univariate Bootstrap (as well as the Traditional Bootstrap). In addition there are multiple functions for DeFries-Fulker behavioral genetics models. The univariate bootstrapping functions, DeFries-Fulker functions, regression and traditional bootstrapping functions form the original core. Additional features may come online later, however this software is a work in progress. For more information about univariate bootstrapping see: Lee and Rodgers (1998) and Beasley et al (2007) <doi:10.1037/1082-989X.12.4.414>.