Named after the Irish name for weather, this package contains tidied data from the Irish Meteorological Service's hourly observations for 2017. In all, the data sets include observations from 25 weather stations, and also latitude and longitude coordinates for each weather station. Now includes energy generation data for Ireland and Northern Ireland (2017), including Wind Generation data.

Computation and visualization of Bayesian Regions of Evidence to systematically evaluate the sensitivity of a superiority or non-inferiority claim against any prior assumption of its assessors. Methodological details are elaborated by Hoefler and Miller (<https://osf.io/jxnsv>). Besides generic functions, the package also provides an intuitive Shiny application, that can be run in local R environments.

Doubly robust methods for evaluating surrogate markers as outlined in: Agniel D, Hejblum BP, Thiebaut R & Parast L (2022). "Doubly robust evaluation of high-dimensional surrogate markers", Biostatistics <doi:10.1093/biostatistics/kxac020>. You can use these methods to determine how much of the overall treatment effect is explained by a (possibly high-dimensional) set of surrogate markers.

This package provides function to create, read, write, and work with iCalendar files (which typically have .ics or .ical extensions), and the scheduling data, calendars and timelines of people, organisations and other entities that they represent. iCalendar is an open standard for exchanging calendar and scheduling information between users and computers, described at <https://icalendar.org/>.

Calculate a set of corrected test statistics for cases when samples are not independent, such as when classification accuracy values are obtained over resamples or through k-fold cross-validation, as proposed by Nadeau and Bengio (2003) <doi:10.1023/A:1024068626366> and presented in Bouckaert and Frank (2004) <doi:10.1007/978-3-540-24775-3_3>.

This package provides a set of fast tools for converting a textual corpus into a set of normalized tables. Users may make use of the udpipe back end with no external dependencies, or a Python back ends with spaCy <https://spacy.io>. Exposed annotation tasks include tokenization, part of speech tagging, named entity recognition, and dependency parsing.

This package provides tools for exploratory analysis of tabular data using colour highlighting. Highlighting is displayed in any console supporting ANSI colours, and can be converted to HTML', typst', latex and SVG'. quarto and rmarkdown rendering are directly supported. It is also possible to add colour to regular expression matches and highlight differences between two arbitrary R objects.

Experiences studies are an integral component of the actuarial control cycle. Regardless of the decrement or policyholder behavior of interest, the analyses conducted is often the same. Ultimately, this package aims to reduce time spent writing the same code used for different experience studies, therefore increasing the time for to uncover new insights inherit within the relevant experience.

This package provides tools for simulating from continuous-time individual level models of disease transmission, and carrying out infectious disease data analyses with the same models. The epidemic models considered are distance-based and/or contact network-based models within Susceptible-Infectious-Removed (SIR) or Susceptible-Infectious-Notified-Removed (SINR) compartmental frameworks. <doi:10.18637/jss.v098.i10>.

The forensIT package is a comprehensive statistical toolkit tailored for handling missing person cases. By leveraging information theory metrics, it enables accurate assessment of kinship, particularly when limited genetic evidence is available. With a focus on optimizing statistical power, forensIT empowers investigators to effectively prioritize family members, enhancing the reliability and efficiency of missing person investigations.

Statistical tool set for population genetics. The package provides following functions: 1) estimators of genetic differentiation (FST), 2) regression analysis of environmental effects on genetic differentiation using generalized least squares (GLS) method, 3) interfaces to read and manipulate GENEPOP format data files). For more information, see Kitada, Nakamichi and Kishino (2020) <doi:10.1101/2020.01.30.927186>.

An easy way to conduct flexible scan. Monte-Carlo method is used to test the spatial clusters given the cases, population, and shapefile. A table with formal style and a map with clusters are included in the result report. The method can be referenced at: Toshiro Tango and Kunihiko Takahashi (2005) <doi:10.1186/1476-072X-4-11>.

This package provides basic distribution functions for a generalized logistic distribution proposed by Rathie and Swamee (2006) <https://www.rroij.com/open-access/on-new-generalized-logistic-distributions-and-applicationsbarreto-fhs-mota-jma-and-rathie-pn-.pdf>. It also has an interactive RStudio plot for better guessing dynamically of initial values for ease of included optimization and simulating.

Penalized methods are useful for fitting over-parameterized models. This package includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets where the number of covariates exceeds the sample size or when there is collinearity among the covariates. The glmnet fitting algorithm is used to fit the continuation ratio model after data restructuring.

Inference, goodness-of-fit tests, and predictions for continuous and discrete univariate Hidden Markov Models (HMM), including zero-inflated distributions. The goodness-of-fit test is based on a Cramer-von Mises statistic and uses parametric bootstrap to estimate the p-value. The description of the methodology is taken from Nasri et al (2020) <doi:10.1029/2019WR025122>.

This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

This package provides an R interface to the JBrowse 2 genome browser. Enables embedding a JB2 genome browser in a Shiny app or R Markdown document. The browser can also be launched from an interactive R console. The browser can be loaded with a variety of common genomics data types, and can be used with a custom theme.

This package provides an interface to MetaPost (Hobby, 1998) <http://www.tug.org/docs/metapost/mpman.pdf>. There are functions to generate an R description of a MetaPost curve, functions to generate MetaPost code from an R description, functions to process MetaPost code, and functions to read solved MetaPost paths back into R.

Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when the coefficient matrix is low-rank and sparse. mixedLSR allows subgroup identification by alternating optimization with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically performing parameter selection to identify low-rank substructures in the coefficient matrix.

Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression (SUR) systems on unbalanced panel in the one/two-way cases also taking into account the possibility of cross equation restrictions. Methodological details can be found in Biørn (2004) <doi:10.1016/j.jeconom.2003.10.023> and Platoni, Sckokai, Moro (2012) <doi:10.1080/07474938.2011.607098>.

Develop spatial interaction models (SIMs). SIMs predict the amount of interaction, for example number of trips per day, between geographic entities representing trip origins and destinations. Contains functions for creating origin-destination datasets from geographic input datasets and calculating movement between origin-destination pairs with constrained, production-constrained, and attraction-constrained models (Wilson 1979) <doi:10.1068/a030001>.

Takes one or more fitted Cox proportional hazards models and writes a shiny application to a directory specified by the user. The shiny application displays predicted survival curves based on user input, and contains none of the original data used to create the Cox model or models. The goal is towards visualization and presentation of predicted survival curves.

This package provides a port of the Scarabee toolkit originally written as a Matlab-based application. scaRabee provides a framework for simulation and optimization of pharmacokinetic-pharmacodynamic models at the individual and population level. It is built on top of the neldermead package, which provides the direct search algorithm proposed by Nelder and Mead for model optimization.

Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi: 10.1002/sim.8549>.