This package provides a comprehensive suite of utilities for univariate continuous probability distributions and reliability models. Includes functions to compute the probability density, cumulative distribution, quantile, reliability, and hazard functions, along with random variate generation. Also offers diagnostic and model assessment tools such as Quantile-Quantile (Q-Q) and Probability-Probability (P-P) plots, the Kolmogorov-Smirnov goodness-of-fit test, and model selection criteria including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Currently implements the following distributions: Burr X, Chen, Exponential Extension, Exponentiated Logistic, Exponentiated Weibull, Exponential Power, Flexible Weibull, Generalized Exponential, Gompertz, Generalized Power Weibull, Gumbel, Inverse Generalized Exponential, Linear Failure Rate, Log-Gamma, Logistic-Exponential, Logistic-Rayleigh, Log-log, Marshall-Olkin Extended Exponential, Marshall-Olkin Extended Weibull, and Weibull Extension distributions. Serves as a valuable resource for teaching and research in probability theory, reliability analysis, and applied statistical modeling.

This package provides functions to fit Gaussian linear model by maximising the residual log likelihood where the covariance structure can be written as a linear combination of known matrices. Can be used for multivariate models and random effects models. Easy straight forward manner to specify random effects models, including random interactions. Code now optimised to use Sherman Morrison Woodbury identities for matrix inversion in random effects models. We've added the ability to fit models using any kernel as well as a function to return the mean and covariance of random effects conditional on the data (best linear unbiased predictors, BLUPs). Clifford and McCullagh (2006) <https://www.r-project.org/doc/Rnews/Rnews_2006-2.pdf>.

This package provides a piped query generator based on Edgar F. Codd's relational algebra, and on production experience using SQL and dplyr at big data scale. The design represents an attempt to make SQL more teachable by denoting composition by a sequential pipeline notation instead of nested queries or functions. The implementation delivers reliable high performance data processing on large data systems such as Spark', databases, and data.table'. Package features include: data processing trees or pipelines as observable objects (able to report both columns produced and columns used), optimized SQL generation as an explicit user visible table modeling step, plus explicit query reasoning and checking.

This package provides tools for robust regression model fitting using the RANSAC (Random Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters of a model from a dataset that contains outliers. This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) <doi:10.1145/358669.358692>.

This is a wrapper function for image(), which makes reasonable raster plots with nice axis and other useful features.

This package performs goodness-of-fit tests for capture-recapture models as described by Gimenez et al. (2018) <doi:10.1111/2041-210X.13014>. Also contains several functions to process capture-recapture data.

This package provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon-related timescales (cal BP, cal BC/AD, C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 <doi:10.1017/S0033822200038339>). The methods follow long-established recommendations such as Stuiver and Polach (1977) <doi:10.1017/S0033822200003672> and Reimer et al. (2004) <doi:10.1017/S0033822200033154>. This package uses the calibration curves from the data package rintcal'.

Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by ZdzisÅ aw Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.

Biologically relevant, yet mathematically sound constraints are used to compute the propensity and thence infer the dominant direction of reactions of a generic biochemical network. The reactions must be unique and their number must exceed that of the reactants,i.e., reactions >= reactants + 2. ReDirection', computes the null space of a user-defined stoichiometry matrix. The spanning non-zero and unique reaction vectors (RVs) are combinatorially summed to generate one or more subspaces recursively. Every reaction is represented as a sequence of identical components across all RVs of a particular subspace. The terms are evaluated with (biologically relevant bounds, linear maps, tests of convergence, descriptive statistics, vector norms) and the terms are classified into forward-, reverse- and equivalent-subsets. Since, these are mutually exclusive the probability of occurrence is binary (all, 1; none, 0). The combined propensity of a reaction is the p1-norm of the sub-propensities, i.e., sum of the products of the probability and maximum numeric value of a subset (least upper bound, greatest lower bound). This, if strictly positive is the probable rate constant, is used to infer dominant direction and annotate a reaction as "Forward (f)", "Reverse (b)" or "Equivalent (e)". The inherent computational complexity (NP-hard) per iteration suggests that a suitable value for the number of reactions is around 20. Three functions comprise ReDirection. These are check_matrix() and reaction_vector() which are internal, and calculate_reaction_vector() which is external.

This package provides functions to manipulate rational functions, including basic arithmetic operators, derivatives, and integrals with EXPLICIT forms.

Download, prepare and analyze data from large-scale assessments and surveys with complex sampling and assessment design (see Rutkowski', 2010 <doi:10.3102/0013189X10363170>). Such studies are, for example, international assessments like TIMSS', PIRLS and PISA'. A graphical interface is available for the non-technical user.The package includes functions to covert the original data from SPSS into R data sets keeping the user-defined missing values, merge data from different respondents and/or countries, generate variable dictionaries, modify data, produce descriptive statistics (percentages, means, percentiles, benchmarks) and multivariate statistics (correlations, linear regression, binary logistic regression). The number of supported studies and analysis types will increase in future. For a general presentation of the package, see Mirazchiyski', 2021a (<doi:10.1186/s40536-021-00114-4>). For detailed technical aspects of the package, see Mirazchiyski', 2021b (<doi:10.3390/psych3020018>).

This package provides datasets related to the Star Trek fictional universe and functions for working with the data. The package also provides access to real world datasets based on the televised series and other related licensed media productions. It interfaces with the Star Trek API (STAPI) (<http://stapi.co/>), Memory Alpha (<https://memory-alpha.fandom.com/wiki/Portal:Main>), and Memory Beta (<https://memory-beta.fandom.com/wiki/Main_Page>) to retrieve data, metadata and other information relating to Star Trek. It also contains several local datasets covering a variety of topics. The package also provides functions for working with data from other Star Trek-related R data packages containing larger datasets not stored in rtrek'.

This package provides a list of functions for the statistical analysis and the post-processing of the Markov Chains simulated by ChronoModel (see <http://www.chronomodel.fr> for more information). ChronoModel is a friendly software to construct a chronological model in a Bayesian framework. Its output is a sampled Markov chain from the posterior distribution of dates component the chronology. The functions can also be applied to the analyse of mcmc output generated by Oxcal software.

Distance-sampling (<doi:10.1007/978-3-319-19219-2>) is a field survey and analytical method that estimates density and abundance of survey targets (e.g., animals) when detection probability declines with observation distance. Distance-sampling is popular in ecology, especially when survey targets are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Analysis involves fitting smooth (parametric) curves to histograms of observation distances and using those functions to adjust density estimates for missed targets. Routines included here fit curves to observation distance histograms, estimate effective sampling area, density of targets in surveyed areas, and the abundance of targets in a surrounding study area. Confidence interval estimation uses built-in bootstrap resampling. Help files are extensive and have been vetted by multiple authors. Many tutorials are available on the package's website (URL below).

Random walk functions to extract new variables based on clients transactional behaviour. For more details, see Eddin et al. (2021) <arXiv:2112.07508v3> and Oliveira et al. (2021) <arXiv:2102.05373v2>.

This package provides a proof of concept implementation of regularized non-negative matrix factorization optimization. A non-negative matrix factorization factors non-negative matrix Y approximately as L R, for non-negative matrices L and R of reduced rank. This package supports such factorizations with weighted objective and regularization penalties. Allowable regularization penalties include L1 and L2 penalties on L and R, as well as non-orthogonality penalties. This package provides multiplicative update algorithms, which are a modification of the algorithm of Lee and Seung (2001) <http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.pdf>, as well as an additive update derived from that multiplicative update. See also Pav (2004) <doi:10.48550/arXiv.2410.22698>.

This package provides a tool for multiply imputing missing data using MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; <doi:10.1017/pan.2020.49>). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with Python to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>.

Implementation of the Robust Exponential Decreasing Index (REDI), proposed in the article by Issa Moussa, Arthur Leroy et al. (2019) <https://bmjopensem.bmj.com/content/bmjosem/5/1/e000573.full.pdf>. The REDI represents a measure of cumulated workload, robust to missing data, providing control of the decreasing influence of workload over time. Various functions are provided to format data, compute REDI, and visualise results in a simple and convenient way.

This package provides an I/O interface between R data.frames and Raven DataFrames. Defines functions to both read and write DataFrame files, as well as serialize/deserialize data.frames/DataFrames.

Perform sigmoidal Emax model fit using Stan in a formula notation, without writing Stan model code.

This package provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) <doi:10.32614/RJ-2010-017>.

Robust tests (RW and RF) are provided for testing the equality of two long-tailed symmetric (LTS) means when the variances are unknown and arbitrary. RW test is a robust version of Welch's two sample t test and the RF is a robust fiducial based test. The RW and RF tests are proposed using the adaptive modified maximum likelihood (AMML) estimators derived by Tiku and Surucu (2009) <doi:10.1016/j.spl.2008.12.001> and Donmez (2010) <https://open.metu.edu.tr/bitstream/handle/11511/19440/index.pdf>.

This package provides simplified methods for managing classic Rubik's cubes and many other modifications of it (such as NxNxN size cubes, void cubes and 8-coloured cubes - so called octa cubes). Includes functions of handling special syntax for managing such cubes; and different approach to plotting 3D cubes without using external libraries (for example OpenGL').

Helps to prepare a release. Before releasing an R package it is important to update the DESCRIPTION file and the changelog. This package prepares these files and also updates the versions according to the branches. It relies heavily on the desc packages.