Comprehensive analysis and forecasting of univariate time series using automatic time series models of many kinds. Harvey AC (1989) <doi:10.1017/CBO9781107049994>. Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>. Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>. Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>. Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>. Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.

Access data from Land Registry Open Data <http://landregistry.data.gov.uk/> through SPARQL queries. uklr supports the house price index, transaction and price paid data.

UNIfied Cross-Omics deconvolution (Unico) deconvolves standard 2-dimensional bulk matrices of samples by features into a 3-dimensional tensors representing samples by features by cell types. Unico stands out as the first principled model-based deconvolution method that is theoretically justified for any heterogeneous genomic data. For more details see Chen and Rahmani et al. (2024) <doi:10.1101/2024.01.27.577588>.

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).

Estimate ambient vitamin D-effective or erythemal dose using ultraviolet radiation (UV) data from the TEMIS database, based on date and geographical location.

Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or self-defined penalties on the B-spline coefficients.

Supervised classification methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to test them on, which highlight the strengths and weaknesses of each technique.

This package provides tools for fitting and assessing Bayesian multilevel regression models that account for unmeasured confounders.

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.

Obtain United States map data frames of varying region types (e.g. county, state). The map data frames include Alaska and Hawaii conveniently placed to the bottom left, as they appear in most maps of the US. Convenience functions for plotting choropleths, visualizing spatial data, and working with FIPS codes are also provided.

Assess essential unidimensionality using external validity information using the procedure proposed by Ferrando & Lorenzo-Seva (2019) <doi:10.1177/0013164418824755>. Provides two indices for assessing differential and incremental validity, both based on a second-order modelling schema for the general factor.

Run a Gibbs sampler for hurdle models to analyze data showing an excess of zeros, which is common in zero-inflated count and semi-continuous models. The package includes the hurdle model under Gaussian, Gamma, inverse Gaussian, Weibull, Exponential, Beta, Poisson, negative binomial, logarithmic, Bell, generalized Poisson, and binomial distributional assumptions. The models described in Ganjali et al. (2024).

Nonparametric estimation of a unimodal or U-shape covariate effect under additive hazards model.

Detects values imported from spreadsheets that were auto-converted to Excel date serials and reconstructs the originally intended day.month decimals (for example, 30.3 that Excel displayed as 30/03/2025'). The functions work in a vectorized manner, preserve non-serial values, and support both the 1900 and 1904 date systems.

Uniform sampling on various geometric shapes, such as spheres, ellipsoids, simplices.

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.

An engine for univariate time series forecasting using different regression models in an autoregressive way. The engine provides an uniform interface for applying the different models. Furthermore, it is extensible so that users can easily apply their own regression models to univariate time series forecasting and benefit from all the features of the engine, such as preprocessings or estimation of forecast accuracy.

When updating major or minor R versions all packages should be re-installed. The utilities in this package assist in getting a user up-and-running again by installing all previously installed R packages. The package uses renv to install; immediately replenishing your renv package cache.

The uc.check() function checks whether the roots of a given polynomial lie outside the Unit circle. You can also easily draw an unit circle.

Reconstructs all possible raw data that could have led to reported summary statistics. Provides a wrapper for the Rust implementation of the CLOSURE algorithm.

Allows users to access live UK energy market information via various APIs.

Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them. The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage). The details of the indices in this package can be found in: J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) <doi:10.1109/TFUZZ.2016.2540063>, T. Calinski, J. Harabasz (1974) <doi:10.1080/03610927408827101>, C. H. Chou, M. C. Su, E. Lai (2004) <doi:10.1007/s10044-004-0218-1>, D. L. Davies, D. W. Bouldin (1979) <doi:10.1109/TPAMI.1979.4766909>, J. C. Dunn (1973) <doi:10.1080/01969727308546046>, F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015651>, M. Kim, R. S. Ramakrishna (2005) <doi:10.1016/j.patrec.2005.04.007>, S. H. Kwon (1998) <doi:10.1049/EL:19981523>, S. H. Kwon, J. Kim, S. H. Son (2021) <doi:10.1049/ell2.12249>, G. W. Miligan (1980) <doi:10.1007/BF02293907>, M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) <doi:10.1016/j.patcog.2003.06.005>, M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) <doi:10.1109/TSMCB.2012.2205679>, S. Saitta, B. Raphael, I. Smith (2007) <doi:10.1007/978-3-540-73499-4_14>, A. Starczewski (2017) <doi:10.1007/s10044-015-0525-8>, Y. Tang, F. Sun, Z. Sun (2005) <doi:10.1109/ACC.2005.1470111>, N. Wiroonsri (2024) <doi:10.1016/j.patcog.2023.109910>, N. Wiroonsri, O. Preedasawakul (2023) <doi:10.48550/arXiv.2308.14785>, C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) <doi:10.1109/TFUZZ.2014.2322495>, X. Xie, G. Beni (1991) <doi:10.1109/34.85677> and Rousseeuw (1987) and Kaufman and Rousseeuw(2009) <doi:10.1016/0377-0427(87)90125-7> and <doi:10.1002/9780470316801> C. Alok. (2010).

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.