Robust mixture discriminant analysis (RMDA), proposed in Bouveyron & Girard, 2009 <doi:10.1016/j.patcog.2009.03.027>, allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.

This package provides functions and methods for manipulating SNOMED CT concepts. The package contains functions for loading the SNOMED CT release into a convenient R environment, selecting SNOMED CT concepts using regular expressions, and navigating the SNOMED CT ontology. It provides the SNOMEDconcept S3 class for a vector of SNOMED CT concepts (stored as 64-bit integers) and the SNOMEDcodelist S3 class for a table of concepts IDs with descriptions. The package can be used to construct sets of SNOMED CT concepts for research (<doi:10.1093/jamia/ocac158>). For more information about SNOMED CT visit <https://www.snomed.org/>.

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the RMarkdown vignette: "'refitME R-package tutorial".

The output gap indicates the percentage difference between the actual output of an economy and its potential. Since potential output is a latent process, the estimation of the output gap poses a challenge and numerous filtering techniques have been proposed. RGAP facilitates the estimation of a Cobb-Douglas production function type output gap, as suggested by the European Commission (Havik et al. 2014) <https://ideas.repec.org/p/euf/ecopap/0535.html>. To that end, the non-accelerating wage rate of unemployment (NAWRU) and the trend of total factor productivity (TFP) can be estimated in two bivariate unobserved component models by means of Kalman filtering and smoothing. RGAP features a flexible modeling framework for the appropriate state-space models and offers frequentist as well as Bayesian estimation techniques. Additional functionalities include direct access to the AMECO <https://economy-finance.ec.europa.eu/economic-research-and-databases/economic-databases/ameco-database_en> database and automated model selection procedures. See the paper by Streicher (2022) <http://hdl.handle.net/20.500.11850/552089> for details.

Loading calls data from Ringostat API'. See <https://help.ringostat.com/knowledge-base/article/integration-with-ringostat-via-api>.

Jalali calendar, or solar Hijri, is calendar of Iran and Afghanistan (<https://en.wikipedia.org/wiki/Solar_Hijri_calendar>). This package is designed to working with Jalali date. For this purpose, It defines JalaliDate class that is similar to Date class.

Compute the repeated measures correlation, a statistical technique for determining the overall within-individual relationship among paired measures assessed on two or more occasions, first introduced by Bland and Altman (1995). Includes functions for diagnostics, p-value, effect size with confidence interval including optional bootstrapping, as well as graphing. Also includes several example datasets. For more details, see the web documentation <https://lmarusich.github.io/rmcorr/index.html> and the original paper: Bakdash and Marusich (2017) <doi:10.3389/fpsyg.2017.00456>.

Datasets from the 2021 Ghana Population and Housing Census Results. Users can access results as tidyverse and sf'-Ready Data Frames. The data in this package is scraped from pdf reports released by the Ghana Statistical Service website <https://census2021.statsghana.gov.gh/> . The package currently only contains datasets from the literacy and education reports. Namely, school attendance data for respondents aged 3 years and above.

Random walk functions to extract new variables based on clients transactional behaviour. For more details, see Eddin et al. (2021) <arXiv:2112.07508v3> and Oliveira et al. (2021) <arXiv:2102.05373v2>.

This package provides a tool to exchange data between R and Raven sound analysis software (Cornell Lab of Ornithology). Functions work on data formats compatible with the R package warbleR'.

Geostatistical analysis of continuous and count data. Implements stationary Gaussian processes with Matérn correlation for spatial prediction, as described in Diggle and Giorgi (2019, ISBN: 978-1-138-06102-7).

Estimation of reproduction numbers for disease outbreak, based on incidence data. The R0 package implements several documented methods. It is therefore possible to compare estimations according to the methods used. Depending on the methods requested by user, basic reproduction number (commonly denoted as R0) or real-time reproduction number (referred to as R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs will give different graphs depending on the methods requested : basic reproductive number estimations will only show the epidemic curve (collected data) and an adjusted model, whereas real-time methods will also show the R(t) variations throughout the outbreak time period. Sensitivity analysis tools are also provided, and allow for investigating effects of varying Generation Time distribution or time window on estimates.

Model fitting, model selection and parameter tuning procedures for a class of random network models. Many useful network modeling, estimation, and processing methods are included. The work to build and improve this package is partially supported by the NSF grants DMS-2015298 and DMS-2015134.

Download, prepare and analyze data from large-scale assessments and surveys with complex sampling and assessment design (see Rutkowski', 2010 <doi:10.3102/0013189X10363170>). Such studies are, for example, international assessments like TIMSS', PIRLS and PISA'. A graphical interface is available for the non-technical user.The package includes functions to covert the original data from SPSS into R data sets keeping the user-defined missing values, merge data from different respondents and/or countries, generate variable dictionaries, modify data, produce descriptive statistics (percentages, means, percentiles, benchmarks) and multivariate statistics (correlations, linear regression, binary logistic regression). The number of supported studies and analysis types will increase in future. For a general presentation of the package, see Mirazchiyski', 2021a (<doi:10.1186/s40536-021-00114-4>). For detailed technical aspects of the package, see Mirazchiyski', 2021b (<doi:10.3390/psych3020018>).

Generate utils::globalVariables() from roxygen2 @global and @autoglobal tags.

Collection of models and analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and convergence, regional specialization as well as accessibility and spatial interaction models.

Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See <https://github.com/yupenghe/REPTILE> for more information.

R access to the FOAAS (F... Off As A Service) web service is provided.

Robust estimators for the beta regression, useful for modeling bounded continuous data. Currently, four types of robust estimators are supported. They depend on a tuning constant which may be fixed or selected by a data-driven algorithm also implemented in the package. Diagnostic tools associated with the fitted model, such as the residuals and goodness-of-fit statistics, are implemented. Robust Wald-type tests are available. More details about robust beta regression are described in Maluf et al. (2025) <doi:10.1007/s00184-024-00949-1>.

This package provides a fast implementation of the greedy algorithm for the set cover problem using Rcpp'.

Functionality to download stock prices, cryptocurrency data, and more from the Tiingo API <https://api.tiingo.com/>.

R package for creating, manipulating and reading RO-Crates. Latest supported version of the specification: <https://w3id.org/ro/crate/1.2/>.

This package provides tools for grading the coding style and documentation of R scripts. This is the R component of Roger the Omni Grader, an automated grading system for computer programming projects based on Unix shell scripts; see <https://gitlab.com/roger-project>. The package also provides an R interface to the shell scripts. Inspired by the lintr package.

Easily interact with the Arduino Iot Cloud API <https://www.arduino.cc/reference/en/iot/api/>, managing devices, things, properties and data.