Computes Chernoff's distribution based on the method in Piet Groeneboom & Jon A Wellner (2001) Computing Chernoff's Distribution, Journal of Computational and Graphical Statistics, 10:2, 388-400, <doi:10.1198/10618600152627997>. Chernoff's distribution is defined as the distribution of the maximizer of the two-sided Brownian motion minus quadratic drift. That is, Z = argmax (B(t)-t^2).

This package provides a series of wrapper functions to implement the 10 maximum likelihood models of animal orientation described by Schnute and Groot (1992) <DOI:10.1016/S0003-3472(05)80068-5>. The functions also include the ability to use different optimizer methods and calculate various model selection metrics (i.e., AIC, AICc, BIC). The ability to perform variants of the Hermans-Rasson test and Pycke test is also included as described in Landler et al. (2019) <DOI:10.1186/s12898-019-0246-8>. The latest version also includes a new method to calculate circular-circular and circular-linear distance correlations.

Biclustering, row clustering and column clustering using the proportional odds model (POM), ordered stereotype model (OSM) or binary model for ordinal categorical data. Fernández, D., Arnold, R., Pledger, S., Liu, I., & Costilla, R. (2019) <doi:10.1007/s11634-018-0324-3>.

This package provides functions for computing the density and the log-likelihood function of closed-skew normal variates, and for generating random vectors sampled from this distribution. See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004). The closed skew normal distribution, Skew-elliptical distributions and their applications: a journey beyond normality, Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.

Spatial and non-spatial regression models with compositional responses (and compositional predictors) using the alpha--transformation. Relevant papers include: Tsagris M. and Pantazis Y. (2026), <doi:10.48550/arXiv.2510.12663>, Tsagris M. (2015), <https://soche.cl/chjs/volumes/06/02/Tsagris(2015).pdf>, Tsagris M.T., Preston S. and Wood A.T.A. (2011), <doi:10.48550/arXiv.1106.1451>.

After using this, a publication-ready correlation table with p-values indicated will be created. The input can be a full data frame; any string and Boolean terms will be dropped as part of functionality. Correlations and p-values are calculated using the Hmisc framework. Output of the correlation_matrix() function is a table of strings; this gets saved out to a .csv2 with the save_correlation_matrix() function for easy insertion into a paper. For more details about the process, consult <https://paulvanderlaken.com/2020/07/28/publication-ready-correlation-matrix-significance-r/>.

Parameters of a user-specified probability distribution are modelled by a multi-layer perceptron artificial neural network. This framework can be used to implement probabilistic nonlinear models including mixture density networks, heteroscedastic regression models, zero-inflated models, etc. following Cannon (2012) <doi:10.1016/j.cageo.2011.08.023>.

Calculate the colocalization index, NSInC, in two different ways as described in the paper (Liu et al., 2019. Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearsonâ s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions.

Fit a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.

Determining the value of Stirling numbers of 1st kind and 2nd kind,references: Bóna,Miklós(2017,ISBN 9789813148840).

Enhances the ini package by adding the ability to interpolate variables. The INI configuration file is read into an R6 ConfigParser object (loosely inspired by Pythons ConfigParser module) and the keys can be read, where %(....)s instances are interpolated by other included options or outside variables.

This package provides a daily summary of COVID-19 cases, deaths, recovered, tests, vaccinations, and hospitalizations for 230+ countries, 760+ regions, and 12000+ administrative divisions of lower level. Includes policy measures, mobility data, and geospatial identifiers. Data source: COVID-19 Data Hub <https://covid19datahub.io>.

Population ratio estimator (calibrated) under two-phase random sampling design has gained enormous popularity in the recent time. This package provides functions for estimation population ratio (calibrated) under two phase sampling design, including the approximate variance of the ratio estimator. The improved ratio estimator can be applicable for both the case, when auxiliary data is available at unit level or aggregate level (eg., mean or total) for first phase sampled. Calibration weight of each unit of the second phase sample was calculated. Single and combined inclusion probabilities were also estimated for both phases under two phase random [simple random sampling without replacement (SRSWOR)] sampling. The improved ratio estimator's percentage coefficient of variation was also determined as a measure of accuracy. This package has been developed based on the theoretical development of Islam et al. (2021) and Ozgul (2020) <doi:10.1080/00949655.2020.1844702>.

Evaluates stimuli using Large Language Models. Supports multiple LLM providers: OpenAI', Anthropic', Ollama', LM Studio', DeepSeek', Groq', Mistral', and OpenAI-compatible endpoints. Stimuli: plain text, local image/audio files, or image URLs. Audio is transcribed via OpenAI Whisper before rating. Supports numeric, text, and raw return types.

Customized training is a simple technique for transductive learning, when the test covariates are known at the time of training. The method identifies a subset of the training set to serve as the training set for each of a few identified subsets in the training set. This package implements customized training for the glmnet() and cv.glmnet() functions.

Climate stability measures are not formalized in the literature and tools for generating stability metrics from existing data are nascent. This package provides tools for calculating climate stability from raster data encapsulating climate change as a series of time slices. The methods follow Owens and Guralnick <doi:10.17161/bi.v14i0.9786> Biodiversity Informatics.

Emulation of an application originally created by Paul Pukite. Computer Aided Rate Modeling and Simulation. Jan Pukite and Paul Pukite, (1998, ISBN 978-0-7803-3482), William J. Stewart, (1994, ISBN: 0-691-03699-3).

This package provides a convenient set of wrapper functions to install pharmacometric packages and Shiny applications developed by Certara PMX and Integrated Drug Development (iDD). The functions ensure the successful installation of packages from non-standard repositories.

Datasets for the book entitled "Modelling Survival Data in Medical Research" by Collett (2023) <doi:10.1201/9781003282525>. The datasets provide extensive examples of time-to-event data.

Integration of Earth system data from various sources is a challenging task. Except for their qualitative heterogeneity, different data records exist for describing similar Earth system process at different spatio-temporal scales. Data inter-comparison and validation are usually performed at a single spatial or temporal scale, which could hamper the identification of potential discrepancies in other scales. csa package offers a simple, yet efficient, graphical method for synthesizing and comparing observed and modelled data across a range of spatio-temporal scales. Instead of focusing at specific scales, such as annual means or original grid resolution, we examine how their statistical properties change across spatio-temporal continuum.

This package contains some commonly used categorical variable encoders, such as LabelEncoder and OneHotEncoder'. Inspired by the encoders implemented in Python sklearn.preprocessing package (see <http://scikit-learn.org/stable/modules/preprocessing.html>).

This package implements lasso and ridge regression for dichotomised outcomes (<doi:10.1080/02664763.2023.2233057>), i.e., numerical outcomes that were transformed to binary outcomes. Such artificial binary outcomes indicate whether an underlying measurement is greater than a threshold.

Implementation of two-dimensional (2D) correlation analysis based on the Fourier-transformation approach described by Isao Noda (I. Noda (1993) <DOI:10.1366/0003702934067694>). Additionally there are two plot functions for the resulting correlation matrix: The first one creates colored 2D plots, while the second one generates 3D plots.

CODATA internationally recommended values of the fundamental physical constants, provided as symbols for direct use within the R language. Optionally, the values with uncertainties and/or units are also provided if the errors', units and/or quantities packages are installed. The Committee on Data for Science and Technology (CODATA) is an interdisciplinary committee of the International Council for Science which periodically provides the internationally accepted set of values of the fundamental physical constants. This package contains the "2022 CODATA" version, published on May 2024: Eite Tiesinga, Peter J. Mohr, David B. Newell, and Barry N. Taylor (2024) <https://physics.nist.gov/cuu/Constants/>.