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>.

Conduct unit root tests based on EViews (<https://eviews.com>) routines and report them in tables. EViews (Econometric Views) is a commercial software for econometrics.

This package implements Minimum Torsion for portfolio diversification as described in Meucci, Attilio (2013) <doi:10.2139/ssrn.2276632>.

Programmatic interface to access data from the UK Health Security Agency (UKHSA) Data Dashboard API. The package was originally based on the ukcovid19 package by Pouria Hadjibagheri and has been substantially rewritten and extended. For more information on the API, see <https://ukhsa-dashboard.data.gov.uk/access-our-data>.

Bindings to system utilities found in most Unix systems such as POSIX functions which are not part of the Standard C Library.

The most used functions on IPEA (Instituto de Pesquisa Economica Aplicada). Most of functions deal with brazilian names. It can guess the women single's name, extract prepositions or extract the first name.

This package provides a set of general functions that I have used in various projects and other R packages. Miscellaneous operations on data frames, matrices and vectors, ROC and PR statistics.

We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403.<DOI:10.1111/biom.13024> for more information.

This package provides tools for converting data from complex or irregular layouts to a columnar structure. For example, tables with multilevel column or row headers, or spreadsheets. Header and data cells are selected by their contents and position, as well as formatting and comments where available, and are associated with one other by their proximity in given directions. Functions for data frames and HTML tables are provided.

This package provides a generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. The including functions simulate and summarize posterior samples for location-scale mixture models using a weakly informative prior. There is no need to define priors for scale-location parameters except two hyperparameters in which are associated with a Dirichlet prior for weights and a simplex.

This is a framework that aims to provide methods and tools for assessing the impact of different sources of uncertainties (e.g.positional uncertainty) on performance of species distribution models (SDMs).).

Enables the user to calculate Value at Risk (VaR) and Expected Shortfall (ES) by means of various parametric and semiparametric GARCH-type models. For the latter the estimation of the nonparametric scale function is carried out by means of a data-driven smoothing approach. Model quality, in terms of forecasting VaR and ES, can be assessed by means of various backtesting methods such as the traffic light test for VaR and a newly developed traffic light test for ES. The approaches implemented in this package are described in e.g. Feng Y., Beran J., Letmathe S. and Ghosh S. (2020) <https://ideas.repec.org/p/pdn/ciepap/137.html> as well as Letmathe S., Feng Y. and Uhde A. (2021) <https://ideas.repec.org/p/pdn/ciepap/141.html>.

Historical voting data of the United Nations General Assembly. This includes votes for each country in each roll call, as well as descriptions and topic classifications for each vote.

Universally unique identifiers ('UUIDs') can be sub-optimal for many uses-cases because they are not the most character efficient way of encoding 128 bits of randomness; v1/v2 versions are impractical in many environments, as they require access to a unique, stable MAC address; v3/v5 versions require a unique seed and produce randomly distributed IDs, which can cause fragmentation in many data structures; v4 provides no other information than randomness which can cause fragmentation in many data structures. Providing an alternative, ULIDs (<https://github.com/ulid/spec>) have 128-bit compatibility with UUID', 1.21e+24 unique ULIDs per millisecond, support standard (text) sorting, canonically encoded as a 26 character string, as opposed to the 36 character UUID', use base32 encoding for better efficiency and readability (5 bits per character), are case insensitive, have no special characters (i.e. are URL safe) and have a monotonic sort order (correctly detects and handles the same millisecond).

This package implements various independence tests for discrete, continuous, and infinite-dimensional data. The tests are based on a U-statistic permutation test, the USP of Berrett, Kontoyiannis and Samworth (2020) <arXiv:2001.05513>, and shown to be minimax rate optimal in a wide range of settings. As the permutation principle is used, all tests have exact, non-asymptotic Type I error control at the nominal level.

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/>.

This package provides a ggplot2 theme and color palettes following the United Nations High Commissioner for Refugees (UNHCR) Data Visualization Guidelines recommendations.

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>.

This package provides functions for uniform sampling of the environmental space, designed to assist species distribution modellers in gathering ecologically relevant pseudo-absence data. The method ensures balanced representation of environmental conditions and helps reduce sampling bias in model calibration. Based on the framework described by Da Re et al. (2023) <doi:10.1111/2041-210X.14209>.

Clustering and classification inference for high dimension low sample size (HDLSS) data with U-statistics. The package contains implementations of nonparametric statistical tests for sample homogeneity, group separation, clustering, and classification of multivariate data. The methods have high statistical power and are tailored for data in which the dimension L is much larger than sample size n. See Gabriela B. Cybis, Marcio Valk and SÃ lvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387>, Marcio Valk and Gabriela B. Cybis (2020) <doi:10.1080/10618600.2020.1796398>, Debora Z. Bello, Marcio Valk and Gabriela B. Cybis (2021) <arXiv:2106.09115>.

Full listing of UK baby names occurring more than three times per year between 1974 and 2020, and rankings of baby name popularity by decade from 1904 to 1994.

This package provides a container for data used by the usmap package. The data used by usmap has been extracted into this package so that the file size of the usmap package can be reduced greatly. The data in this package will be updated roughly once per year as new map data files are provided by the US Census Bureau.

Top-down and bottom-up algorithms for nonparametric function estimation in Gaussian noise using Unbalanced Haar wavelets.

The boundaries for geographical units in the United States of America contained in this package include state, county, congressional district, and zip code tabulation area. Contemporary boundaries are provided by the U.S. Census Bureau (public domain). Historical boundaries for the years from 1629 to 2000 are provided form the Newberry Library's Atlas of Historical County Boundaries (licensed CC BY-NC-SA). Additional data is provided in the USAboundariesData package; this package provides an interface to access that data.