Create and integrate thematic maps in your workflow. This package helps to design various cartographic representations such as proportional symbols, choropleth or typology maps. It also offers several functions to display layout elements that improve the graphic presentation of maps (e.g. scale bar, north arrow, title, labels). mapsf maps sf objects on base graphics.

This package provides tools to handle, manipulate and explore trajectory data, with an emphasis on data from tracked animals. The package is designed to support large studies with several million location records and keep track of units where possible. Data import directly from movebank <https://www.movebank.org/cms/movebank-main> and files is facilitated.

Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components (de Jong S. & Kiers H. A. L. (1992) <doi:10.1016/0169-7439(92)80100-I>). Several rotation and model selection options are provided.

Facilitates extraction of geospatial data from the Office for National Statistics Open Geography and nomis Application Programming Interfaces (APIs). Simplifies process of querying nomis datasets <https://www.nomisweb.co.uk/> and extracting desired datasets in dataframe format. Extracts area shapefiles at chosen resolution from Office for National Statistics Open Geography <https://geoportal.statistics.gov.uk/>.

Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) <doi:10.9790/5728-10121519>.

This package provides an imputation pipeline for single-cell RNA sequencing data. The scISR method uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and estimates the dropout values using a subspace regression model (Tran et.al. (2022) <DOI:10.1038/s41598-022-06500-4>).

This package provides a set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>.

It contains functions to estimate multivariate Student's t dynamic and static regression models for given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form. Poudyal, N., and Spanos, A. (2022) <doi:10.3390/econometrics10020017>. Spanos, A. (1994) <http://www.jstor.org/stable/3532870>.

Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.

Fetch data from the <https://www.justice.gov/developer/api-documentation/api_v1> API such as press releases, blog entries, and speeches. Optional parameters allow users to specify the number of results starting from the earliest or latest entries, and whether these results contain keywords. Data is cleaned for analysis and returned in a dataframe.

This package implements a metabolic network analysis pipeline to identify an active metabolic module based on high throughput data. The pipeline takes as input transcriptional and/or metabolic data and finds a metabolic subnetwork (module) most regulated between the two conditions of interest. The package further provides functions for module post-processing, annotation and visualization.

This package provides a seamless interface to the MEME Suite family of tools for motif analysis. memes provides data aware utilities for using GRanges objects as entrypoints to motif analysis, data structures for examining & editing motif lists, and novel data visualizations. memes functions and data structures are amenable to both base R and tidyverse workflows.

This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package.

The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as well as a specifically tailored test for differential expression in single-cell RNA sequencing data.

The method models RNA-seq reads using a mixture of 3 beta-binomial distributions to generate posterior probabilities for genotyping bi-allelic single nucleotide polymorphisms. Elena Vigorito, Anne Barton, Costantino Pitzalis, Myles J. Lewis and Chris Wallace (2023) <doi:10.1093/bioinformatics/btad393> "BBmix: a Bayesian beta-binomial mixture model for accurate genotyping from RNA-sequencing.".

Este pacote traduz os seguintes conjuntos de dados: airlines', airports', ames_raw', AwardsManagers', babynames', Batting', diamonds', faithful', fueleconomy', Fielding', flights', gapminder', gss_cat', iris', Managers', mpg', mtcars', atmos', penguins', People, Pitching', pixarfilms','planes', presidential', table1', table2', table3', table4a', table4b', table5', vehicles', weather', who'. English: It provides a Portuguese translated version of the datasets listed above.

This package provides functions to compute state-specific and marginal life expectancies. The computation is based on a fitted continuous-time multi-state model that includes an absorbing death state; see Van den Hout (2017, ISBN:9781466568402). The fitted multi-state model model should be estimated using the msm package using age as the time-scale.

Given a set of parameters describing model dynamics and a corresponding cost function, FAMoS performs a dynamic forward-backward model selection on a specified selection criterion. It also applies a non-local swap search method. Works on any cost function. For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.

Wrapper for computing parameters for univariate distributions using MLE. It creates an object that stores d, p, q, r functions as well as parameters and statistics for diagnostics. Currently supports automated fitting from base and actuar packages. A manually fitting distribution fitting function is included to support directly specifying parameters for any distribution from ancillary packages.

Analysis of Bayesian adaptive enrichment clinical trial using Free-Knot Bayesian Model Averaging (FK-BMA) method of Maleyeff et al. (2024) for Gaussian data. Maleyeff, L., Golchi, S., Moodie, E. E. M., & Hudson, M. (2024) "An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables" <doi:10.1093/biomtc/ujae141>.

Utilizing Generative Artificial Intelligence models like GPT-4 and Gemini Pro as coding and writing assistants for R users. Through these models, GenAI offers a variety of functions, encompassing text generation, code optimization, natural language processing, chat, and image interpretation. The goal is to aid R users in streamlining laborious coding and language processing tasks.

Apply an adaptation of the SuperFastHash algorithm to any R object. Hash whole R objects or, for vectors or lists, hash R objects to obtain a set of hash values that is stored in a structure equivalent to the input. See <http://www.azillionmonkeys.com/qed/hash.html> for a description of the hash algorithm.

This package implements an efficient algorithm to fit and tune penalized Support Vector Machine models using the generalized coordinate descent algorithm. Designed to handle high-dimensional datasets effectively, with emphasis on precision and computational efficiency. This package implements the algorithms proposed in Tang, Q., Zhang, Y., & Wang, B. (2022) <https://openreview.net/pdf?id=RvwMTDYTOb>.

These are data and functions to support quantitative peace science research. The data are important state-year information on democracy and wealth, which require periodic updates and regular maintenance. The functions permit some exploratory and diagnostic assessment of the kinds of data in demand by the community, but do not impose many dependencies on the user.