Estimate model parameters to determine whether two compounds have synergy, antagonism, or Loewe's Additivity.

Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.

This package provides a simple progress bar showing estimated remaining time. Multiple forecast methods and user defined forecast method for the remaining time are supported.

Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.

Automated analysis and modeling of longitudinal omics data (e.g. breath metabolomics') using generalized spline mixed effect models. Including automated filtering of noise parameters and determination of breakpoints.

Time series analysis based on lambda transformer and variational seq2seq, built on Torch'.

Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc. REFERENCES i. Hoerl and Kennard (1970) <doi:10.1080/00401706.1970.10488634>, ii. Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006>, iii. Imdadullah, Aslam, and Saima (2017), iv. Marquardt (1970) <doi:10.2307/1267205>.

Lag-sequential analysis is a method of assessing of patterns (what tends to follow what?) in sequences of codes. The codes are typically for discrete behaviors or states. The functions in this package read a stream of codes, or a frequency transition matrix, and produce a variety of lag sequential statistics, including transitional frequencies, expected transitional frequencies, transitional probabilities, z values, adjusted residuals, Yule's Q values, likelihood ratio tests of stationarity across time and homogeneity across groups or segments, transformed kappas for unidirectional dependence, bidirectional dependence, parallel and nonparallel dominance, and significance levels based on both parametric and randomization tests. The methods are described in Bakeman & Quera (2011) <doi:10.1017/CBO9781139017343>, O'Connor (1999) <doi:10.3758/BF03200753>, Wampold & Margolin (1982) <doi:10.1037/0033-2909.92.3.755>, and Wampold (1995, ISBN:0-89391-919-5).

This package provides function for the l1-ball prior on high-dimensional regression. The main function, l1ball(), yields posterior samples for linear regression, as introduced by Xu and Duan (2020) <arXiv:2006.01340>.

This package contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>.

Temporary and permanent message queues for R. Built on top of SQLite databases. SQLite provides locking, and makes it possible to detect crashed consumers. Crashed jobs can be automatically marked as "failed", or put in the queue again, potentially a limited number of times.

Whole-buffer DEFLATE-based compression and decompression of raw vectors using the libdeflate library (see <https://github.com/ebiggers/libdeflate>). Provides the user with additional control over the speed and the quality of DEFLATE compression compared to the fixed level of compression offered in R's memCompress() function. Also provides the libdeflate static library and C headers along with a CMake target and packageâ config file that ease linking of libdeflate in packages that compile and statically link bundled libraries using CMake'.

This package creates HTML strings to embed tables, images or graphs in pop-ups of interactive maps created with packages like leaflet or mapview'. Handles local images located on the file system or via remote URL. Handles graphs created with lattice or ggplot2 as well as interactive plots created with htmlwidgets'.

The Programme for International Student Assessment (PISA) is a global study conducted by the Organization for Economic Cooperation and Development (OECD) in member and non-member countries to assess educational systems by assessing 15-year-old school students academic performance in mathematics, science, and reading. This datasets contains information on their scores and other socioeconomic characteristics, information about their school and its infrastructure, as well as the countries that are taking part in the program.

This package contains some functions to learn and teach basic plane Geometry at undergraduate level with the aim of being helpful to young students with little programming skills.

Dimensionality reduction techniques for binary data including logistic PCA.

This package implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution. Described further in Mantz (2016) <DOI:10.1093/mnras/stv3008>.

This package provides functions for estimating the gliding box lacunarity (GBL), covariance, and pair-correlation of a random closed set (RACS) in 2D from a binary coverage map (e.g. presence-absence land cover maps). Contains a number of newly-developed covariance-based estimators of GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and balanced estimators, proposed by Picka (2000) <http://www.jstor.org/stable/1428408>, for covariance, centred covariance, and pair-correlation. Also contains methods for estimating contagion-like properties of RACS and simulating 2D Boolean models. Binary coverage maps are usually represented as raster images with pixel values of TRUE, FALSE or NA, with NA representing unobserved pixels. A demo for extracting such a binary map from a geospatial data format is provided. Binary maps may also be represented using polygonal sets as the foreground, however for most computations such maps are converted into raster images. The package is based on research conducted during the author's PhD studies.

This package provides a set of all-cause and cause-specific life expectancy sensitivity and decomposition methods, including Arriaga (1984) <doi:10.2307/2061029>, others documented by Ponnapalli (2005) <doi:10.4054/DemRes.2005.12.7>, lifetable, numerical, and other algorithmic approaches such as Horiuchi et al (2008) <doi:10.1353/dem.0.0033>, or Andreev et al (2002) <doi:10.4054/DemRes.2002.7.14>.

This package provides extensions to the leaflet package to customize legends with images, text styling, orientation, sizing, and symbology and functions to create symbols to plot on maps.

This package implements a logistic box-cox model. This model is fully described in Xing, L. et al. (2021) <doi:10.1002/cjs.11587>.

Gives users seeking federal lobbying disclosures an easier way to query the API maintained by the Senate federal lobbying disclosures database <https://lda.senate.gov/api/redoc/v1/> to find out how much companies and other entities are spending to lobby Congress and the federal government. It allows for search terms such as keywords, time periods and entity names. It then attempts to clean, or at least flag, filings that could provide incorrect results when seeking to answer the question: How much is being spent on lobbying our Congress and the administration and what issues do they care about?

This package provides a collection of helper functions and illustrative datasets to support learning and teaching of data science with R. The package is designed as a companion to the book <https://book-data-science-r.netlify.app>, making key data science techniques accessible to individuals with minimal coding experience. Functions include tools for data partitioning, performance evaluation, and data transformations (e.g., z-score and min-max scaling). The included datasets are curated to highlight practical applications in data exploration, modeling, and multivariate analysis. An early inspiration for the package came from an ancient Persian idiom about "eating the liver", symbolizing deep and immersive engagement with knowledge.

This package provides a shiny application to construct age-specific life tables and fertility schedules from individual female daily egg records. The application computes age-specific survival and fertility functions and estimates key demographic parameters including the net reproductive rate, mean generation time, intrinsic rate of increase, finite rate of increase and doubling time. Optional confidence intervals can be obtained using percentile bootstrap or delete-1 jackknife resampling at the female level. Methods and definitions follow Stevens (2009) <doi:10.1007/978-0-387-89882-7> and Rossini et al. (2024) <doi:10.1371/journal.pone.0299598>.