Versatile method for ungrouping histograms (binned count data) assuming that counts are Poisson distributed and that the underlying sequence on a fine grid to be estimated is smooth. The method is based on the composite link model and estimation is achieved by maximizing a penalized likelihood. Smooth detailed sequences of counts and rates are so estimated from the binned counts. Ungrouping binned data can be desirable for many reasons: Bins can be too coarse to allow for accurate analysis; comparisons can be hindered when different grouping approaches are used in different histograms; and the last interval is often wide and open-ended and, thus, covers a lot of information in the tail area. Age-at-death distributions grouped in age classes and abridged life tables are examples of binned data. Because of modest assumptions, the approach is suitable for many demographic and epidemiological applications. For a detailed description of the method and applications see Rizzi et al. (2015) <doi:10.1093/aje/kwv020>.

An R API providing easy access to a relational database with macroeconomic, financial and development related time series data for Uganda. Overall more than 5000 series at varying frequency (daily, monthly, quarterly, annual in fiscal or calendar years) can be accessed through the API. The data is provided by the Bank of Uganda, the Ugandan Ministry of Finance, Planning and Economic Development, the IMF and the World Bank. The database is being updated once a month.

Calculates federal and state income taxes in the United States. It acts as a wrapper to the NBER's TAXSIM 35 (<http://taxsim.nber.org/taxsim35/>) tax simulator. TAXSIM 35 conducts the calculations, while usincometaxes prepares the data for TAXSIM 35, sends the data to TAXSIM 35's server or communicates with the Web Assembly file, retrieves the data, and places it into a data frame. All without the user worrying about this process.

An educational toolkit for learning statistical concepts through interactive exploration. Provides functions for basic statistics (mean, variance, etc.) and probability distributions with step-by-step explanations and interactive learning modes. Each function can be used for simple calculations, detailed learning with explanations, or interactive practice with feedback.

Provide a set of wrappers to call all the endpoints of UptimeRobot API which includes various kind of ping, keep-alive and speed tests. See <https://uptimerobot.com/> for more information.

This package provides a classification (decision) tree is constructed from survival data with high-dimensional covariates. The method is a robust version of the logrank tree, where the variance is stabilized. The main function "uni.tree" returns a classification tree for a given survival dataset. The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests. The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user). This tree construction algorithm is proposed by Emura et al. (2021, in review).

Most universities use specific color combinations to express their unique brand identity. The unicol package provides the colors and color palettes of various universities for easy plotting and printing in R. We collect and provide a diverse range of color palettes for creating scientific visualizations.

This package provides a diverse collection of U.S. datasets encompassing various fields such as crime, economics, education, finance, energy, healthcare, and more. It serves as a valuable resource for researchers and analysts seeking to perform in-depth analyses and derive insights from U.S.-specific data.

Does uniformly most powerful (UMP) and uniformly most powerful unbiased (UMPU) tests. At present only distribution implemented is binomial distribution. Also does fuzzy tests and confidence intervals (following Geyer and Meeden, 2005, <doi:10.1214/088342305000000340>) for the binomial distribution (one-tailed procedures based on UMP test and two-tailed procedures based on UMPU test).

Elasticsearch is an open-source, distributed, document-based datastore (<https://www.elastic.co/products/elasticsearch>). It provides an HTTP API for querying the database and extracting datasets, but that API was not designed for common data science workflows like pulling large batches of records and normalizing those documents into a data frame that can be used as a training dataset for statistical models. uptasticsearch provides an interface for Elasticsearch that is explicitly designed to make these data science workflows easy and fun.

This package provides an overview of the demand for natural gas in the US by state and country level. Data source: US Energy Information Administration <https://www.eia.gov/>.

Variance approximations for the Horvitz-Thompson total estimator in Unequal Probability Sampling using only first-order inclusion probabilities. See Matei and Tillé (2005) and Haziza, Mecatti and Rao (2008) for details.

This package provides functions for the creation and manipulation of scenes and objects within the Unity 3D video game engine (<https://unity.com/>). Specific focuses include the creation and import of terrain data and GameObjects as well as scene management.

This package provides functions for estimating uncertainty in the number of fatalities in the Uppsala Conflict Data Program (UCDP) data. The package implements a parametric reported-value Gumbel mixture distribution that accounts for the uncertainty in the number of fatalities in the UCDP data. The model is based on information from a survey on UCDP coders and how they view the uncertainty of the number of fatalities from UCDP events. The package provides functions for making random draws of fatalities from the mixture distribution, as well as to estimate percentiles, quantiles, means, and other statistics of the distribution. Full details on the survey and estimation procedure can be found in Vesco et al (2024).

Perform L1 or L2 isotonic and unimodal regression on 1D weighted or unweighted input vector and isotonic regression on 2D weighted or unweighted input vector. It also performs L infinity isotonic and unimodal regression on 1D unweighted input vector. Reference: Quentin F. Stout (2008) <doi:10.1016/j.csda.2008.08.005>. Spouge, J., Wan, H. & Wilbur, W.(2003) <doi:10.1023/A:1023901806339>. Q.F. Stout (2013) <doi:10.1007/s00453-012-9628-4>.

Core functions necessary for using The Globe and Mail's R data journalism template, startr', along with utilities for day-to-day data journalism tasks, such as reading and writing files, producing graphics and cleaning up datasets.

This package provides a test to understand the stability of the underlying stochastic data. Helps the userâ s understand whether the random variable under consideration is stationary or non-stationary without any manual interpretation of the results. It further ensures to check all the prerequisites and assumptions which are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all the 4 lags the function diagnoses the input data and returns with an optimised solution on the same.

Interface to easily access data via the United States Department of Agriculture (USDA)'s Livestock Mandatory Reporting ('LMR') Data API at <https://mpr.datamart.ams.usda.gov/>. The downloaded data can be saved for later off-line use. Also provide relevant information and metadata for each of the input variables needed for sending the data inquiry.

This package provides functions for building customized ready-to-export tables for publication.

When a package is loaded, the source repository is checked for new versions and a message is shown in the console indicating whether the package is out of date.

By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in shiny'. Based on the recognized data structures, the method can be used to generate new data.

This package provides easy access to a curated selection of pre-processed data sets relevant to the COVID-19 outbreak in the UK for teaching and demonstration purposes.

This package performs a test for second-order stationarity of time series based on unsystematic sub-samples.

Calculates one-sample unbiased central moment estimates and two-sample pooled estimates up to 6th order, including estimates of powers and products of central moments. Provides the machinery for obtaining unbiased central moment estimators beyond 6th order by generating expressions for expectations of raw sample moments and their powers and products. Gerlovina and Hubbard (2019) <doi:10.1080/25742558.2019.1701917>.