Captures errors or missing examples encountered when iteratively running run_examplez()', and archives them.

Scaffolds packages backed by Zig and generates a safe native boundary for plain Zig functions. The generated integration converts objects at the language boundary, registers native routines, propagates errors, and compiles across platforms without requiring C++.

This package provides an I/O interface between R data.frames and Raven DataFrames. Defines functions to both read and write DataFrame files, as well as serialize/deserialize data.frames/DataFrames.

This package provides methods for multiway data analysis by means of Parafac and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (2018) <doi:10.1080/02664763.2017.1381669>). Several optimization methods alternative to ALS are available (Simonacci and Gallo (2019) <doi:10.1016/j.chemolab.2019.103822>, Simonacci and Gallo (2020) <doi:10.1007/s00500-019-04320-9>).

Features the multiple polynomial quadratic sieve (MPQS) algorithm for factoring large integers and a vectorized factoring function that returns the complete factorization of an integer. The MPQS is based off of the seminal work of Carl Pomerance (1984) <doi:10.1007/3-540-39757-4_17> along with the modification of multiple polynomials introduced by Peter Montgomery and J. Davis as outlined by Robert D. Silverman (1987) <doi:10.1090/S0025-5718-1987-0866119-8>. Utilizes the C library GMP (GNU Multiple Precision Arithmetic). For smaller integers, a simple Elliptic Curve algorithm is attempted followed by a constrained version of Pollard's rho algorithm. The Pollard's rho algorithm is the same algorithm used by the factorize function in the gmp package.

Insert/extract text "reminders" into/from function source code comments or as the "comment" attribute of any object. The former can be handy in development as reminders of e.g. argument requirements, expected objects in the calling environment, required options settings, etc. The latter can be used to provide information of the object and as simple manual "tooltips" for users, among other things.

Download and read the public, non-identified microdata of the RAIS (Relação Anual de Informações Sociais), the annual census of formal employment relationships and establishments published by the Brazilian Ministry of Labour and Employment through the PDET FTP server <ftp://ftp.mtps.gov.br/pdet/microdados/RAIS/>. Lists the years and archives available on the server, resolves which regional or state archive holds a given state, downloads it with an idempotent local cache, and reads the 7z archives as a stream, filtering by state and selecting columns before anything is kept in memory, so that a single state can be extracted from a regional file of tens of millions of records. Handles the two header generations of the files (up to the RAIS 2022 and from the RAIS 2023 onwards) with the same normalized column names, provides the official record layout and a helper to consolidate the employment stock, admissions, separations and December payroll.

Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.

Downloads, imports, and tidies time series data from the Australian Bureau of Statistics <https://www.abs.gov.au/>.

Recursive partitioning methods to build classification trees for ordinal responses within the CART framework. Trees are grown using the Generalized Gini impurity function, where the misclassification costs are given by the absolute or squared differences in scores assigned to the categories of the response. Pruning is based on the total misclassification rate or on the total misclassification cost.

Focused on linear, quadratic and cubic regression models, it has a function for calculating the models, obtaining a list with their parameters, and a function for making the graphs for the respective models.

Within this package the XML-RPC API to NEOS <https://neos-server.org/neos/> is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.

Visualize networks using the javascript library roughjs'. This allows to draw sketchy, hand-drawn-like networks.

The minimum covariance determinant estimator is used to perform robust quadratic discriminant analysis, including cross-validation. References: Friedman J., Hastie T. and Tibshirani R. (2009). "The elements of statistical learning", 2nd edition. Springer, Berlin. <doi:10.1007/978-0-387-84858-7>.

Allows the user to view an image in full screen when clicking on it in RMarkdown documents and shiny applications. The package relies on the JavaScript library intense-images'. See <https://tholman.com/intense-images/> for more information.

Robust estimators for generalized ratio model (Wada, Sakashita and Tsubaki, 2021)<doi:10.17713/ajs.v50i1.994> and linear regression model by the IRLS(iterative reweighted least squares) algorithm are contained.

Load data from vk.com api about your communiti users and views, ads performance, post on user wall and etc. For more information see API Documentation <https://vk.com/dev/first_guide>.

This package creates JavaScript charts with the nvd3 library. So far only the multibar chart, the horizontal multibar chart, the line chart and the line chart with focus are available.

This package provides a custom implementation of the apriori algorithm and binomial tests to identify combinations of features (genes, variants etc) significantly enriched for simultaneous mutations/events from sparse Boolean input, see Vijay Kumar Pounraja, Santhosh Girirajan (2021). Version 1.1 includes a minor adjustment to the number of combinations to be considered for multiple testing correction. This updated version is more conservative in its approach and hence more selective. <doi:10.1101/2021.10.01.462832>.

An interactive web application for reliability analysis using the shiny <https://shiny.posit.co/> framework. The app provides an easy-to-use interface for performing reliability analysis using WeibullR <https://cran.r-project.org/package=WeibullR> and ReliaGrowR <https://cran.r-project.org/package=ReliaGrowR>.

This package provides functions to retrieve data and metadata from providers that disseminate data by means of SDMX web services. SDMX (Statistical Data and Metadata eXchange) is a standard that has been developed with the aim of simplifying the exchange of statistical information. More about the SDMX standard and the SDMX Web Services can be found at: <https://sdmx.org>.

Display spatial data with interactive maps powered by the open- source JavaScript library Leaflet (see <https://leafletjs.com/>). Maps can be rendered in a web browser or displayed in the HTML viewer pane of RStudio'. This package is designed to be easy to use and can create complex maps with vector and raster data, web served map tiles and interface elements.

This package provides a fast calculation of the Blyth-Still-Casella confidence interval. The implementation follows the StatXact 9 manual (Cytel 2010) and "Refining Binomial Confidence Intervals" by George Casella (1986) <doi:10.2307/3314658>.

Utilities to access Integrated Food Security Phase Classification (IPC) and Cadre Harmonisé (CH) food security data. Wrapper functions are available for all of the IPC-CH Public API (<https://docs.api.ipcinfo.org>) simplified and advanced endpoints to easily download the data in a clean and tidy format.