This package provides a RUT (Rol Unico Tributario) is an unique and personal identification number implemented in Chile to identify citizens and taxpayers. Rutifier allows to validate if a RUT exist or not and change between the different formats a RUT can have.

This package provides a supportive collection of functions for gathering and plotting treatment ranking metrics after network meta-analysis.

This package provides a set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.

This package provides a random-effects stochastic model that allows quick detection of clonal dominance events from clonal tracking data collected in gene therapy studies. Starting from the Ito-type equation describing the dynamics of cells duplication, death and differentiation at clonal level, we first considered its local linear approximation as the base model. The parameters of the base model, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones. Although this assumption makes inference easier, in some cases it can be too restrictive and does not take into account possible scenarios of clonal dominance. Therefore we extended the base model by introducing random effects for the clones. In this extended formulation the dynamic parameters are estimated using a tailor-made expectation maximization algorithm. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.05.31.494100>.

Shiny-based interactive gadgets of radial visualization methods and extensions thereof.

This package provides functions for reading, analysing and plotting river networks. For this package, river networks consist of sections and nodes with associated attributes, e.g. to characterise their morphological, chemical and biological state. The package provides functions to read this data from text files, to analyse the network structure and network paths and regions consisting of sections and nodes that fulfill prescribed criteria, and to plot the river network and associated properties.

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 an R interface to the JuliaBUGS.jl package (<https://github.com/TuringLang/JuliaBUGS.jl>) for Bayesian inference using the BUGS modeling language. Allows R users to run models in Julia and return results as familiar R objects. Visualization and posterior analysis are supported via the bayesplot and posterior packages.

This package provides a lightweight wrapper around the RSQLite package for streamlined loading of data from tabular files (i,e. text delimited files like Comma Separated Values and Tab Separated Values, Microsoft Excel, and Arrow Inter-process Communication files) in SQLite databases. Includes helper functions for inspecting the structure of the input files, and some functions to simplify activities on the SQLite tables.

We implement a test of the rational expectations hypothesis based on the marginal distributions of realizations and subjective beliefs from D'Haultfoeuille, Gaillac, and Maurel (2018) <doi:10.3386/w25274>. This test can be used in cases where realizations and subjective beliefs are observed in two different datasets that cannot be matched, or when they are observed in the same dataset. The package also computes the estimator of the minimal deviations from rational expectations than can be rationalized by the data.

Interface to the yacas computer algebra system (<http://www.yacas.org/>).

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).

Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <doi:10.1088/0952-4746/35/3/557>.

Generates both total- and level-specific R-squared measures from Rights and Sterbaâ s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel models with random intercepts and/or slopes, which is based on a complete decomposition of variance. Additionally generates graphical representations of these R-squared measures to allow visualizing and interpreting all measures in the framework together as an integrated set. This framework subsumes 10 previously-developed R-squared measures for multilevel models as special cases of 5 measures from the framework, and it also includes several newly-developed measures. Measures in the framework can be used to compute R-squared differences when comparing multilevel models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>). Bootstrapped confidence intervals can also be calculated. To use the confidence interval functionality, download bootmlm from <https://github.com/marklhc/bootmlm>.

Fits the robust Bayesian Copas (RBC) selection model of Bai et al. (2020) <arXiv:2005.02930> for correcting and quantifying publication bias in univariate meta-analysis. Also fits standard random effects meta-analysis and the Copas-like selection model of Ning et al. (2017) <doi:10.1093/biostatistics/kxx004>.

This package provides four boolean matrix factorization (BMF) methods. BMF has many applications like data mining and categorical data analysis. BMF is also known as boolean matrix decomposition (BMD) and was found to be an NP-hard (non-deterministic polynomial-time) problem. Currently implemented methods are Asso Miettinen, Pauli and others (2008) <doi:10.1109/TKDE.2008.53>, GreConD R. Belohlavek, V. Vychodil (2010) <doi:10.1016/j.jcss.2009.05.002> , GreConDPlus R. Belohlavek, V. Vychodil (2010) <doi:10.1016/j.jcss.2009.05.002> , topFiberM A. Desouki, M. Roeder, A. Ngonga (2019) <arXiv:1903.10326>.

Adds subtotal rows / sections (a la the SAS Proc Tabulate All option) to a Group By output by running a series of Group By functions with partial sets of the same variables and combining the results with the original. Can be used to add comprehensive information to a data report or to quickly aggregate Group By outputs used to gain a greater understanding of data.

Access Synthesize Bio models from their API <https://app.synthesize.bio/> using this wrapper that provides a convenient interface to the Synthesize Bio API, allowing users to generate realistic gene expression data based on specified biological conditions. This package enables researchers to easily access AI-generated transcriptomic data for various modalities including bulk RNA-seq, single-cell RNA-seq, microarray data, and more.

Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) <doi:10.1002/sim.9076>. Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the bayesmeta package.

External jars required for package RKEA.

This package provides a collection of non-linear optimization problems with box bounds transformed into ROI optimization problems. This package provides a wrapper around the globalOptTests which provides a collection of global optimization problems. More information can be found in the README file.

Foundational package in the R4SUB (R for Regulatory Submission) ecosystem. Defines the core evidence table schema, parsers, indicator abstractions, and scoring primitives needed to quantify clinical submission readiness. Provides a standardized contract for ingesting heterogeneous sources (validation outputs, metadata, traceability) into a single evidence framework.

The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net).

Allows developers to work with many R folders inside a package. It offers functionalities to transfer R scripts (saved outside the R folder) into the R folder while making additional checks.