Makes the time series prediction easier by automatizing this process using four main functions: prep(), modl(), pred() and postp(). Features different preprocessing methods to homogenize variance and to remove trend and seasonality. Also has the potential to bring together different predictive models to make comparatives. Features ARIMA and Data Mining Regression models (using caret).

Replace the standard print method for functions with one that performs syntax highlighting, using ANSI colors, if the terminal supports them.

Consider a possibly nonlinear nonparametric regression with p regressors. We provide evaluations by 13 methods to rank regressors by their practical significance or importance using various methods, including machine learning tools. Comprehensive methods are as follows. m6=Generalized partial correlation coefficient or GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>. m7= a generalization of psychologists effect size incorporating nonlinearity and many variables. m8= local linear partial (dy/dxi) using the np package for kernel regressions. m9= partial (dy/dxi) using the NNS package. m10= importance measure using the NNS boost function. m11= Shapley Value measure of importance (cooperative game theory). m12 and m13= two versions of the random forest algorithm. Taraldsen's exact density for sampling distribution of correlations added.

Set the R prompt dynamically, from a function. The package contains some examples to include various useful dynamic information in the prompt: the status of the last command (success or failure); the amount of memory allocated by the current R process; the name of the R package(s) loaded by pkgload and/or devtools'; various git information: the name of the active branch, whether it is dirty, if it needs pushes pulls. You can also create your own prompt if you don't like the predefined examples.

The perturbR() function incrementally perturbs network edges (using the rewireR function)and compares the resulting community detection solutions from the rewired networks with the solution found for the original network. These comparisons aid in understanding the stability of the original solution. The package requires symmetric, weighted (specifically, count) matrices/networks.

This package implements PRIDIT (Principal Component Analysis applied to RIDITs'), an unsupervised, nonparametric method for aggregating ordinal, categorical, and continuous indicators into a single interpretable composite score. Originally proposed by Brockett et al. (2002) <doi:10.1111/1539-6975.00027> for insurance fraud detection and extended to hospital quality measurement by Lieberthal (2008) <doi:10.1111/j.1475-6773.2007.00821.x> and Lieberthal and Comer (2013) <doi:10.1111/rmir.12009>. The package provides: (1) low-level functions ridit(), PRIDITweight(), and PRIDITscore(); (2) a unified pridit() entry point returning a classed object with print, summary, autoplot', and coef methods; (3) pridit_boot() for bootstrap confidence intervals on scores and weights; (4) a step_pridit() recipe step for out-of-sample scoring within the tidymodels framework; and (5) pridit_longitudinal() for panel data, computing cross-period stability of scores and weights.

This package provides permutation methods for testing in high-dimensional linear models. The tests are often robust against heteroscedasticity and non-normality and usually perform well under anti-sparsity. See Hemerik, Thoresen and Finos (2021) <doi:10.1080/00949655.2020.1836183>.

This package implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists of polynomials.

Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)).

Create, transform, and summarize custom random variables with distribution functions (analogues of p*()', d*()', q*()', and r*() functions from base R). Two types of distributions are supported: "discrete" (random variable has finite number of output values) and "continuous" (infinite number of values in the form of continuous random variable). Functions for distribution transformations and summaries are available. Implemented approaches often emphasize approximate and numerical solutions: all distributions assume finite support and finite values of density function; some methods implemented with simulation techniques.

Plot principal component histograms around a bivariate scatter plot.

The probaverse is a suite of packages designed to facilitate creating advanced statistical models through probability distributions. These packages work best when loaded together because they share a common design philosophy and focus on different aspects of developing statistical models. Inspired by the tidyverse package, the probaverse package makes it easy to load the entire suite of probaverse packages together.

Use Prime Factorization for simplifying computations, for instance for ratios of large factorials.

Estimates corrected Procrustean correlation between matrices for removing overfitting effect. Coissac Eric and Gonindard-Melodelima Christelle (2019) <doi:10.1101/842070>.

Chromatin immunoprecipitation DNA sequencing results in genomic tracks that show enriched regions or peaks where proteins are bound. This package implements fast C code that computes the true and false positives with respect to a database of annotated region labels.

This package provides a suite of likelihood ratio test based methods to use in pharmacovigilance. Contains various testing and post-processing functions.

This package provides a probabilistic framework that integrates Data Envelopment Analysis (DEA) (Banker et al., 1984) <doi:10.1287/mnsc.30.9.1078> with machine learning classifiers (Kuhn, 2008) <doi:10.18637/jss.v028.i05> to estimate both the (in)efficiency status and the probability of efficiency for decision-making units. The approach trains predictive models on DEA-derived efficiency labels (Charnes et al., 1985) <doi:10.1016/0304-4076(85)90133-2>, enabling explainable artificial intelligence (XAI) workflows with global and local interpretability tools, including permutation importance (Molnar et al., 2018) <doi:10.21105/joss.00786>, Shapley value explanations (Strumbelj & Kononenko, 2014) <doi:10.1007/s10115-013-0679-x>, and sensitivity analysis (Cortez, 2011) <https://CRAN.R-project.org/package=rminer>. The framework also supports probability-threshold peer selection and counterfactual improvement recommendations for benchmarking and policy evaluation. The probabilistic efficiency framework is detailed in González-Moyano et al. (2025) "Probability-based Technical Efficiency Analysis through Machine Learning", in review for publication.

This package provides a set of tools to extract bibliographic content from PubMed database using NCBI REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>. It includes functions to search, download, and convert PubMed bibliographic records into data frames compatible with the bibliometrix package. Features include programmatic query building, batch downloading by PMID, citation enrichment via NCBI E-Link, and robust error handling with automatic retry logic.

Color palettes generated from paintings.

Perform a supervised data analysis on a database through a shiny graphical interface. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.

This package provides simple methods to extract data portions from various objects. The relative portion size and the way the portion is selected can be chosen.

Generates Plus Code of geometric objects or data frames that contain them, giving the possibility to specify the precision of the area. The main feature of the package comes from the open-source code developed by Google Inc. present in the repository <https://github.com/google/open-location-code/blob/main/java/src/main/java/com/google/openlocationcode/OpenLocationCode.java>. For details about Plus Code', visit <https://maps.google.com/pluscodes/> or <https://github.com/google/open-location-code>.

This package provides propensity score weighting methods to control for confounding in causal inference with dichotomous treatments and continuous/binary outcomes. It includes the following functional modules: (1) visualization of the propensity score distribution in both treatment groups with mirror histogram, (2) covariate balance diagnosis, (3) propensity score model specification test, (4) weighted estimation of treatment effect, and (5) augmented estimation of treatment effect with outcome regression. The weighting methods include the inverse probability weight (IPW) for estimating the average treatment effect (ATE), the IPW for average treatment effect of the treated (ATT), the IPW for the average treatment effect of the controls (ATC), the matching weight (MW), the overlap weight (OVERLAP), and the trapezoidal weight (TRAPEZOIDAL). Sandwich variance estimation is provided to adjust for the sampling variability of the estimated propensity score. These methods are discussed by Hirano et al (2003) <DOI:10.1111/1468-0262.00442>, Lunceford and Davidian (2004) <DOI:10.1002/sim.1903>, Li and Greene (2013) <DOI:10.1515/ijb-2012-0030>, and Li et al (2016) <DOI:10.1080/01621459.2016.1260466>.

Producing the time-dependent receiver operating characteristic (ROC) curve through parametric approaches. Tools for generating random data, fitting, predicting and check goodness of fit are prepared. The methods are developed from the theoretical framework of proportional hazard model and copula functions. Using this package, users can now simulate parametric time-dependent ROC and run experiment to understand the behavior of the curve under different scenario.