Downloads and processes health microdata from Brazilian Unified Health System ('DataSUS') information systems. It handles the compressed DBC format internally and provides functions to organize variables and add labels to categorical fields from mortality, live births, hospital admissions, outpatient care, health facilities, and notifiable diseases data. For details, see Saldanha et al. (2019) <doi:10.1590/0102-311x00032419>.

This package provides ensemble samplers for affine-invariant Monte Carlo Markov Chain, which allow a faster convergence for badly scaled estimation problems. Two samplers are proposed: the differential.evolution sampler from ter Braak and Vrugt (2008) <doi:10.1007/s11222-008-9104-9> and the stretch sampler from Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65>.

Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction, where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) <doi:10.1287/mnsc.2016.2546>. It also incorporates models that use a power law distribution on the degree distribution.

This package provides an SQL-based mass spectrometry (MS) data backend supporting also storage and handling of very large data sets. Objects from this package are supposed to be used with the Spectra Bioconductor package. Through the MsBackendSql with its minimal memory footprint, this package thus provides an alternative MS data representation for very large or remote MS data sets.

The r-abhgenotyper package provides simple imputation, error-correction and plotting capacities for genotype data. The package is supposed to serve as an intermediate but independent analysis tool between the TASSEL GBS pipeline and the r-qtl package. It provides functionalities not found in either TASSEL or r-qtl in addition to visualization of genotypes as "graphical genotypes".

SpaceTrooper performs Quality Control analysis using data driven GLM models of Image-Based spatial data, providing exploration plots, QC metrics computation, outlier detection. It implements a GLM strategy for the detection of low quality cells in imaging-based spatial data (Transcriptomics and Proteomics). It additionally implements several plots for the visualization of imaging based polygons through the ggplot2 package.

Uses inverse probability weighting methods to estimate treatment effect under marginal structure model for the cause-specific hazard of competing risk events. Estimates also the cumulative incidence function (i.e. risk) of the potential outcomes, and provides inference on risk difference and risk ratio. Reference: Kalbfleisch & Prentice (2002)<doi:10.1002/9781118032985>; Hernan et al (2001)<doi:10.1198/016214501753168154>.

Generate project files and directories following a pre-made template. You can specify variables to customize file names and content, and flexibly adapt the template to your needs. cookiecutter for R implements a subset of the excellent cookiecutter package for the Python programming language (<https://github.com/cookiecutter/>), and aims to be largely compatible with the original cookiecutter template format.

Description: Application of empirical mode decomposition based support vector regression model for nonlinear and non stationary univariate time series forecasting. For method details see (i) Choudhury (2019) <http://krishi.icar.gov.in/jspui/handle/123456789/44873>; (ii) Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/43174>; (iii) Das (2023) <http://krishi.icar.gov.in/jspui/handle/123456789/77772>.

Reads water network simulation data in Epanet text-based .inp and .rpt formats into R. Also reads results from Epanet-msx'. Provides basic summary information and plots. The README file has a quick introduction. See <https://www.epa.gov/water-research/epanet> for more information on the Epanet software for modeling hydraulic and water quality behavior of water piping systems.

Create forecasts from multiple predictions using ensemble Bayesian model averaging (EBMA). EBMA models can be estimated using an expectation maximization (EM) algorithm or as fully Bayesian models via Gibbs sampling. The methods in this package are Montgomery, Hollenbach, and Ward (2015) <doi:10.1016/j.ijforecast.2014.08.001> and Montgomery, Hollenbach, and Ward (2012) <doi:10.1093/pan/mps002>.

Comparing two independent or paired groups across a range of descriptive statistics, enabling the evaluation of potential differences in central tendency (mean, median), dispersion (variance, interquartile range), shape (skewness, kurtosis), and distributional characteristics (various quantiles). The analytical framework incorporates parametric t-tests, non-parametric Wilcoxon tests, permutation tests, and bootstrap resampling techniques to assess the statistical significance of observed differences.

Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractability of the Ising model the distribution can only be computed up to roughly 10 nodes. The Blume-Capel model, an Ising model with an additional on-site quadratic (crystal-field) term, is also supported.

The free and open a statistical spreadsheet jamovi (<https://www.jamovi.org>) aims to make statistical analyses easy and intuitive. jamovi produces syntax that can directly be used in R (in connection with the R-package jmv'). Having import / export routines for the data files jamovi produces ('.omv') permits an easy transfer of data and analyses between jamovi and R.

Quickly and easily generate plots of acoustic data aligned with transcriptions similar to those made in Praat using either derived signals generated directly in R with wrassp or imported derived signals from Praat'. Provides easy and fast out-of-the-box solutions but also a high extent of flexibility. Also provides options for embedding audio in figures and animating figures.

Empirical likelihood methods for asymptotically efficient estimation of models based on conditional or unconditional moment restrictions; see Kitamura, Tripathi & Ahn (2004) <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013) <doi:10.1002/cjs.11183>. Kernel-based non-parametric methods for density/regression estimation and numerical routines for empirical likelihood maximisation are implemented in Rcpp for speed.

Takes objects of class edsurvey.data.frame and converts them to a data.frame within the calling environment of dplyr and ggplot2 functions. Additionally, for plotting with ggplot2', users can map aesthetics to subject scales and all plausible values will be used. This package supports student level data; to work with school or teacher level data, see ?EdSurvey::getData'.

We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.

Computes Chernoff's distribution based on the method in Piet Groeneboom & Jon A Wellner (2001) Computing Chernoff's Distribution, Journal of Computational and Graphical Statistics, 10:2, 388-400, <doi:10.1198/10618600152627997>. Chernoff's distribution is defined as the distribution of the maximizer of the two-sided Brownian motion minus quadratic drift. That is, Z = argmax (B(t)-t^2).

This package contains functions to validate administrative register as CPF (Cadastro de Pessoa Fisica), CNPJ (Cadastro de Pessoa Juridica), PIS (Programa de Integracao Social), CNES (Cadastro Nacional de Saude). Builds from and improves on previous package from IPEA validaRA <https://github.com/ipea/validaRA>. It can check individual registers or help creating a table summarizing validity of a set.

Computes six functional diversity indices. These are namely, Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008 <doi:10.1890/07-1206.1>). Provides efficient, modular, and parallel functions to compute functional diversity indices (preprint: <doi:10.32942/osf.io/dg7hw>).

Perform forensic handwriting analysis of two scanned handwritten documents. This package implements the statistical method described by Madeline Johnson and Danica Ommen (2021) <doi:10.1002/sam.11566>. Similarity measures and a random forest produce a score-based likelihood ratio that quantifies the strength of the evidence in favor of the documents being written by the same writer or different writers.

Offers a rich collection of data focused on cancer research, covering survival rates, genetic studies, biomarkers, and epidemiological insights. Designed for researchers, analysts, and bioinformatics practitioners, the package includes datasets on various cancer types such as melanoma, leukemia, breast, ovarian, and lung cancer, among others. It aims to facilitate advanced research, analysis, and understanding of cancer epidemiology, genetics, and treatment outcomes.

Extracts growth, survival, and local neighborhood density information from repeated, fine-scale maps of organism occurrence. Further information about this package can be found in our journal article, "plantTracker: An R package to translate maps of plant occurrence into demographic data" published in 2022 in Methods in Ecology and Evolution (Stears, et al., 2022) <doi:10.1111/2041-210X.13950>.