Chaos theory has been hailed as a revolution of thoughts and attracting ever increasing attention of many scientists from diverse disciplines. Chaotic systems are nonlinear deterministic dynamic systems which can behave like an erratic and apparently random motion. A relevant field inside chaos theory and nonlinear time series analysis is the detection of a chaotic behaviour from empirical time series data. One of the main features of chaos is the well known initial value sensitivity property. Methods and techniques related to test the hypothesis of chaos try to quantify the initial value sensitive property estimating the Lyapunov exponents. The DChaos package provides different useful tools and efficient algorithms which test robustly the hypothesis of chaos based on the Lyapunov exponent in order to know if the data generating process behind time series behave chaotically or not.

This package provides a thin wrapper around the Datorama API. Ideal for analyzing marketing data from <https://datorama.com>.

Gives you the ability to use arbitrary Docker images (including custom ones) to process Rmarkdown code chunks.

Use numerical optimization to fit ordinary differential equations (ODEs) to time series data to examine the dynamic relationships between variables or the characteristics of a dynamical system. It can now be used to estimate the parameters of ODEs up to second order, and can also apply to multilevel systems. See <https://github.com/yueqinhu/defit> for details.

Graphical interface for loading datasets in RStudio from all installed (including unloaded) packages, also includes command line interfaces.

This package contains a range of functions covering the present development of the distributional method for the dichotomisation of continuous outcomes. The method provides estimates with standard error of a comparison of proportions (difference, odds ratio and risk ratio) derived, with similar precision, from a comparison of means. See the URL below or <arXiv:1809.03279> for more information.

Given count data from two conditions, it determines which transcripts are differentially expressed across the two conditions using Bayesian inference of the parameters of a bottom-up model for PCR amplification. This model is developed in Ndifon Wilfred, Hilah Gal, Eric Shifrut, Rina Aharoni, Nissan Yissachar, Nir Waysbort, Shlomit Reich Zeliger, Ruth Arnon, and Nir Friedman (2012), <http://www.pnas.org/content/109/39/15865.full>, and results in a distribution for the counts that is a superposition of the binomial and negative binomial distribution.

Implementation of the double/debiased machine learning framework of Chernozhukov et al. (2018) <doi:10.1111/ectj.12097> for partially linear regression models, partially linear instrumental variable regression models, interactive regression models and interactive instrumental variable regression models. DoubleML allows estimation of the nuisance parts in these models by machine learning methods and computation of the Neyman orthogonal score functions. DoubleML is built on top of mlr3 and the mlr3 ecosystem. The object-oriented implementation of DoubleML based on the R6 package is very flexible. More information available in the publication in the Journal of Statistical Software: <doi:10.18637/jss.v108.i03>.

Compares distributions with one another in terms of their fit to each sample in a dataset that contains multiple samples, as described in Joo, Aguinis, and Bradley (in press). Users can examine the fit of seven distributions per sample: pure power law, lognormal, exponential, power law with an exponential cutoff, normal, Poisson, and Weibull. Automation features allow the user to compare all distributions for all samples with a single command line, which creates a separate row containing results for each sample until the entire dataset has been analyzed.

The dataset package extends tidy data frames with machine-readable metadata, semantic definitions, and provenance information. It supports incremental semantic stabilization, interoperable dataset exchange, and FAIR-oriented publication workflows by preserving contextual metadata directly within R objects. The package facilitates the creation, exchange, reuse, and RDF serialization of datasets in line with ISO and W3C standards.

Efficient methods for computing distance covariance and relevant statistics. See Székely et al.(2007) <doi:10.1214/009053607000000505>; Székely and Rizzo (2013) <doi:10.1016/j.jmva.2013.02.012>; Székely and Rizzo (2014) <doi:10.1214/14-AOS1255>; Huo and Székely (2016) <doi:10.1080/00401706.2015.1054435>.

The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) <doi:10.3982/ECTA18260>, that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them.

Reaction rate dynamics can be retrieved from metabolite concentration time courses. User has to provide corresponding stoichiometric matrix but not a regulation model (Michaelis-Menten or similar). Instead of solving an ordinary differential equation (ODE) system describing the evolution of concentrations, we use B-splines to catch the concentration and rate dynamics then solve a least square problem on their coefficients with non-negativity (and optionally monotonicity) constraints. Constraints can be also set on initial values of concentration. The package dynafluxr can be used as a library but also as an application with command line interface dynafluxr::cli("-h") or graphical user interface dynafluxr::gui().

This package contains a function called dmur() which accepts four parameters like possible values, probabilities of the values, selling cost and preparation cost. The dmur() function generates various numeric decision parameters like MEMV (Maximum (optimum) expected monitory value), best choice, EPPI (Expected profit with perfect information), EVPI (Expected value of the perfect information), EOL (Expected opportunity loss), which facilitate effective decision-making.

This package creates interactive genome browser. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. Barrios, D. & Prieto, C. (2017) <doi:10.1089/cmb.2016.0213>.

Area under the curve (AUC; Myerson et al., 2001) <doi:10.1901/jeab.2001.76-235> is a popular measure used in discounting research. Although the calculation of AUC is standardized, there are differences in AUC based on some assumptions. For example, Myerson et al. (2001) <doi:10.1901/jeab.2001.76-235> assumed that (with delay discounting data) a researcher would impute an indifference point at zero delay equal to the value of the larger, later outcome. However, this practice is not clearly followed. This imputed zero-delay indifference point plays an important role in log and ordinal versions of AUC. Ordinal and log versions of AUC are described by Borges et al. (2016)<doi:10.1002/jeab.219>. The package can calculate all three versions of AUC [and includes a new version: IHS(AUC)], impute indifference points when x = 0, calculate ordinal AUC in the case of Halton sampling of x-values, and account for probability discounting AUC.

Abstract of Manuscript. Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the userâ s gene lists, aiding in hypothesis generation and data-driven literature review. Sokolowski, Dustin J., et al. "Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression." Nucleic Acids Research Genomics and Bioinformatics (2023).

Estimates dose-response relations from summarized dose-response data and to combines them according to principles of (multivariate) random-effects models.

This package provides functions to compute coefficients measuring the dependence of two or more than two variables. The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) <doi:10.2478/demo-2014-0004>; Liebscher (2017) <doi:10.1515/demo-2017-0012>; Liebscher (2021): <https://arfjournals.com/image/catalog/Journals%20Papers/AJSS/No%202%20(2021)/4-AJSS_123-150.pdf>; Liebscher (2021): Kendall regression coefficient. Computational Statistics and Data Analysis 157. 107140.

This package provides algorithms for estimating divisive and standard latent class models. The divisive latent class method follows van der Palm, van der Ark and Vermunt (2016) <doi:10.1007/s00357-016-9195-5>. Both algorithms use expectation-maximization and Newton-Raphson optimization and are implemented in C++ for speed through Rcpp'.

An implementation by Chen, Li, and Zhang (2022) <doi: 10.1093/bioadv/vbac041> of the Depth Importance in Precision Medicine (DIPM) method in Chen and Zhang (2022) <doi:10.1093/biostatistics/kxaa021> and Chen and Zhang (2020) <doi:10.1007/978-3-030-46161-4_16>. The DIPM method is a classification tree that searches for subgroups with especially poor or strong performance in a given treatment group.

Fast distributional regression models for univariate and bivariate responses using Template Model Builder. The current implementation focuses on Gaussian, Student-t, and skew-normal location-scale models, known sampling covariance, phylogenetic location effects, random-effect scale models, bivariate residual correlation, positive-continuous, Tweedie semi-continuous, strict-proportion, zero-one bounded, and denominator-aware proportion families, fixed-effect Bernoulli/binomial event-probability models, and fixed-effect Poisson, negative-binomial, zero-inflated, zero-truncated, hurdle count, and ordinal cumulative-logit models. Additional response-family models are staged for later phases. Every fitted family also exposes a distributional-output and adequacy layer: randomized quantile-residual worm and QQ plots that detect fixed-effect shape and atom misspecification, and conditional-quantile, exceedance, and centile outputs with plug-in (uncalibrated) intervals.

CRAN packages DoE.base and Rmosek and non-'CRAN package gurobi are enhanced with functionality for the creation of optimized arrays for experimentation, where optimization is in terms of generalized minimum aberration. It is also possible to optimally extend existing arrays to larger run size. The package writes MPS (Mathematical Programming System) files for use with any mixed integer optimization software that can process such files. If at least one of the commercial products Gurobi or Mosek (free academic licenses available for both) is available, the package also creates arrays by optimization. For installing Gurobi and its R package gurobi', follow instructions at <https://support.gurobi.com/hc/en-us/articles/14462206790033-How-do-I-install-Gurobi-for-R>. For installing Mosek and its R package Rmosek', follow instructions at <https://www.mosek.com/downloads/> and <https://docs.mosek.com/8.1/rmosek/install-interface.html>, or use the functionality in the stump CRAN R package Rmosek'.

Estimation of a density from grouped (tabulated) summary statistics evaluated in each of the big bins (or classes) partitioning the support of the variable. These statistics include class frequencies and central moments of order one up to four. The log-density is modelled using a linear combination of penalised B-splines. The multinomial log-likelihood involving the frequencies adds up to a roughness penalty based on the differences in the coefficients of neighbouring B-splines and the log of a root-n approximation of the sampling density of the observed vector of central moments in each class. The so-obtained penalized log-likelihood is maximized using the EM algorithm to get an estimate of the spline parameters and, consequently, of the variable density and related quantities such as quantiles, see Lambert, P. (2021) <arXiv:2107.03883> for details.