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 a set of functions to aid in the production of visuals in ggplot2.

This package implements empirical Bayes approaches to genotype polyploids from next generation sequencing data while accounting for allele bias, overdispersion, and sequencing error. The main functions are flexdog() and multidog(), which allow the specification of many different genotype distributions. Also provided are functions to simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as functions to calculate oracle genotyping error rates, oracle_mis(), and correlation with the true genotypes, oracle_cor(). These latter two functions are useful for read depth calculations. Run browseVignettes(package = "updog") in R for example usage. See Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details on the implemented methods.

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>.

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.

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 decorators, transformators, and utility functions to extend the teal framework for interactive data analysis applications. Implements methods for data visualization enhancement, statistical data transformations, and workflow integration tools. Designed to support clinical and pharmaceutical research workflows within the teal ecosystem through modular and reusable components.

Code snippets to fit models using the tidymodels framework can be easily created for a given data set.

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>.

User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.

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).

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).

This natural language processing toolkit provides language-agnostic tokenization', parts of speech tagging', lemmatization and dependency parsing of raw text. Next to text parsing, the package also allows you to train annotation models based on data of treebanks in CoNLL-U format as provided at <https://universaldependencies.org/format.html>. The techniques are explained in detail in the paper: Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at <doi:10.18653/v1/K17-3009>. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.

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.

Unit-Gompertz density, cumulative distribution, quantile functions and random deviate generation of the unit-Gompertz distribution. In addition, there are a function for fitting the Generalized Additive Models for Location, Scale and Shape.

Find and import datasets from the University of California Irvine Machine Learning (UCI ML) Repository into R. Supports working with data from UCI ML repository inside of R scripts, notebooks, and Quarto'/'RMarkdown documents. Access the UCI ML repository directly at <https://archive.ics.uci.edu/>.

This package provides a suite of utilities for working with the UK Biobank <https://www.ukbiobank.ac.uk/> Nuclear Magnetic Resonance spectroscopy (NMR) metabolomics data <https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=220>. Includes functions for extracting biomarkers from decoded UK Biobank field data, removing unwanted technical variation from biomarker concentrations, computing an extended set of lipid, fatty acid, and cholesterol fractions, and for re-deriving composite biomarkers and ratios after adjusting data for unwanted biological variation. For further details on methods see Ritchie SC et al. Sci Data (2023) <doi:10.1038/s41597-023-01949-y>.

Predicts a smooth and continuous (individual) utility function from utility points, and computes measures of intensity for risk and higher-order risk measures (or any other measure computed with user-written function) based on this utility function and its derivatives according to the method introduced in Schneider (2017) <http://hdl.handle.net/21.11130/00-1735-0000-002E-E306-0>.

This package provides tools for assigning molecular formulas from exact masses obtained by ultrahigh-resolution mass spectrometry. The methodology follows the workflow described in Leefmann et al. (2019) <doi:10.1002/rcm.8315>. The package supports the inspection, filtering and visualization of molecular formula data and includes utilities for calculating common molecular parameters (e.g., double bond equivalents, DBE). A graphical user interface is available via the shiny'-based ume application.

Fetch data from the <https://www.justice.gov/developer/api-documentation/api_v1> API such as press releases, blog entries, and speeches. Optional parameters allow users to specify the number of results starting from the earliest or latest entries, and whether these results contain keywords. Data is cleaned for analysis and returned in a dataframe.

Fit Bayesian hierarchical models of animal abundance and occurrence via the rstan package, the R interface to the Stan C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

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.

Wraps the unrtf utility <https://www.gnu.org/software/unrtf/> to extract text from RTF files. Supports document conversion to HTML, LaTeX or plain text. Output in HTML is recommended because unrtf has limited support for converting between character encodings.

This package implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis.