Implement dynamic linear models outlined in Shumway and Stoffer (2025) <doi:10.1007/978-3-031-70584-7>. Two model structures for data smoothing and forecasting are considered. The specific models proposed will be added once the manuscript is published.

Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the direction of satisfying a linear constraint that would not be satisfied without penalization. Yu and Rosenbaum (2019) <doi:10.1111/biom.13098>.

Diagnostics for linear L1 regression (also known as LAD - Least Absolute Deviations), including: estimation, confidence intervals, tests of hypotheses, measures of leverage, methods of diagnostics for L1 regression, special diagnostics graphs and measures of leverage. The algorithms are based in Dielman (2005) <doi:10.1080/0094965042000223680>, Elian et al. (2000) <doi:10.1080/03610920008832518> and Dodge (1997) <doi:10.1006/jmva.1997.1666>. This package builds on the quantreg package, which is a well-established package for tuning quantile regression models. There are also tests to verify if the errors have a Laplace distribution based on the work of Puig and Stephens (2000) <doi:10.2307/1270952>.

Set of tools aimed at processing meteorological data, converting hourly recorded data to daily, monthly and annual data.

This package provides a grammar of data manipulation with data.table', providing a consistent a series of utility functions that help you solve the most common data manipulation challenges.

In-line functions for multivariate optimization via desirability functions (Derringer and Suich, 1980, <doi:10.1080/00224065.1980.11980968>) with easy use within dplyr pipelines.

Double constrained correspondence analysis (dc-CA) analyzes (multi-)trait (multi-)environment ecological data by using the vegan package and native R code. Throughout the two step algorithm of ter Braak et al. (2018) is used. This algorithm combines and extends community- (sample-) and species-level analyses, i.e. the usual community weighted means (CWM)-based regression analysis and the species-level analysis of species-niche centroids (SNC)-based regression analysis. The two steps use canonical correspondence analysis to regress the abundance data on to the traits and (weighted) redundancy analysis to regress the CWM of the orthonormalized traits on to the environmental predictors. The function dc_CA() has an option to divide the abundance data of a site by the site total, giving equal site weights. This division has the advantage that the multivariate analysis corresponds with an unweighted (multi-trait) community-level analysis, instead of being weighted. The first step of the algorithm uses vegan::cca(). The second step uses wrda() but vegan::rda() if the site weights are equal. This version has a predict() function. For details see ter Braak et al. 2018 <doi:10.1007/s10651-017-0395-x>. and ter Braak & van Rossum 2025 <doi:10.1016/j.ecoinf.2025.103143>.

Offers functionality which provides methods for data analyses and cleaning that can be flexibly applied across multiple variables and in groups. These include cleaning accidental text, contingent calculations, counting missing data, and building summarizations of the data.

The Data Driven I-V Feature Extraction is used to extract Current-Voltage (I-V) features from I-V curves. I-V curves indicate the relationship between current and voltage for a solar cell or Photovoltaic (PV) modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) contain important information of the performance for PV modules. The traditional method uses the single diode model to model I-V curves and extract I-V features. This package does not use the diode model, but uses data-driven a method which select different linear parts of the I-V curves to extract I-V features. This method also uses a sampling method to calculate uncertainties when extracting I-V features. Also, because of the partially shaded array, "steps" occurs in I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves. This material is based upon work supported by the U.S. Department of Energyâ s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be found in the following paper. [1] Ma, X. et al, 2019. <doi:10.1109/JPHOTOV.2019.2928477>.

Find, visualize and explore patterns of differential taxa in vegetation data (namely in a phytosociological table), using the Differential Value (DiffVal). Patterns are searched through mathematical optimization algorithms. Ultimately, Total Differential Value (TDV) optimization aims at obtaining classifications of vegetation data based on differential taxa, as in the traditional geobotanical approach (Monteiro-Henriques 2025, <doi:10.3897/VCS.140466>). The Gurobi optimizer, as well as the R package gurobi', can be installed from <https://www.gurobi.com/products/gurobi-optimizer/>. The useful vignette Gurobi Installation Guide, from package prioritizr', can be found here: <https://prioritizr.net/articles/gurobi_installation_guide.html>.

Statistical deadband algorithms are based on the Send-On-Delta concept as in Miskowicz(2006,<doi:10.3390/s6010049>). A collection of functions compare effectiveness and fidelity of sampled signals using statistical deadband algorithms.

The implemented methods are: Standard Bass model, Generalized Bass model (with rectangular shock, exponential shock, and mixed shock. You can choose to add from 1 to 3 shocks), Guseo-Guidolin model and Variable Potential Market model, and UCRCD model. The Bass model consists of a simple differential equation that describes the process of how new products get adopted in a population, the Generalized Bass model is a generalization of the Bass model in which there is a "carrier" function x(t) that allows to change the speed of time sliding. In some real processes the reachable potential of the resource available in a temporal instant may appear to be not constant over time, because of this we use Variable Potential Market model, in which the Guseo-Guidolin has a particular specification for the market function. The UCRCD model (Unbalanced Competition and Regime Change Diachronic) is a diffusion model used to capture the dynamics of the competitive or collaborative transition.

This package provides a flexible container to transport and manipulate complex sets of data. These data may consist of multiple data files and associated meta data and ancillary files. Individual data objects have associated system level meta data, and data files are linked together using the OAI-ORE standard resource map which describes the relationships between the files. The OAI- ORE standard is described at <https://www.openarchives.org/ore/>. Data packages can be serialized and transported as structured files that have been created following the BagIt specification. The BagIt specification is described at <https://datatracker.ietf.org/doc/html/draft-kunze-bagit-08>.

Bayesian factor models are effective tools for dimension reduction. This is especially applicable to multivariate large-scale datasets. It allows researchers to understand the latent factors of the data which are the linear or non-linear combination of the variables. Dynamic Intrinsic Conditional Autocorrelative Priors (ICAR) Spatiotemporal Factor Models DIFM package provides function to run Markov Chain Monte Carlo (MCMC), evaluation methods and visual plots from Shin and Ferreira (2023)<doi:10.1016/j.spasta.2023.100763>. Our method is a class of Bayesian factor model which can account for spatial and temporal correlations. By incorporating these correlations, the model can capture specific behaviors and provide predictions.

This package implements the locally efficient doubly robust difference-in-differences (DiD) estimators for the average treatment effect proposed by Sant'Anna and Zhao (2020) <doi:10.1016/j.jeconom.2020.06.003>. The estimator combines inverse probability weighting and outcome regression estimators (also implemented in the package) to form estimators with more attractive statistical properties. Two different estimation methods can be used to estimate the nuisance functions.

This package provides functionality to infer trajectories from single-cell data, represent them into a common format, and adapt them. Other biological information can also be added, such as cellular grouping, RNA velocity and annotation. Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.

This package provides a system designed for detecting concept drift in streaming datasets. It offers a comprehensive suite of statistical methods to detect concept drift, including methods for monitoring changes in data distributions over time. The package supports several tests, such as Drift Detection Method (DDM), Early Drift Detection Method (EDDM), Hoeffding Drift Detection Methods (HDDM_A, HDDM_W), Kolmogorov-Smirnov test-based Windowing (KSWIN) and Page Hinkley (PH) tests. The methods implemented in this package are based on established research and have been demonstrated to be effective in real-time data analysis. For more details on the methods, please check to the following sources. KobyliÅ ska et al. (2023) <doi:10.48550/arXiv.2308.11446>, S. Kullback & R.A. Leibler (1951) <doi:10.1214/aoms/1177729694>, Gama et al. (2004) <doi:10.1007/978-3-540-28645-5_29>, Baena-Garcia et al. (2006) <https://www.researchgate.net/publication/245999704_Early_Drift_Detection_Method>, Frà as-Blanco et al. (2014) <https://ieeexplore.ieee.org/document/6871418>, Raab et al. (2020) <doi:10.1016/j.neucom.2019.11.111>, Page (1954) <doi:10.1093/biomet/41.1-2.100>, Montiel et al. (2018) <https://jmlr.org/papers/volume19/18-251/18-251.pdf>.

Statistical methods for retrospectively detecting changes in location and/or dispersion of univariate and multivariate variables. Data values are assumed to be independent, can be individual (one observation at each instant of time) or subgrouped (more than one observation at each instant of time). Control limits are computed, often using a permutation approach, so that a prescribed false alarm probability is guaranteed without making any parametric assumptions on the stable (in-control) distribution. See G. Capizzi and G. Masarotto (2018) <doi:10.1007/978-3-319-75295-2_1> for an introduction to the package.

Constructs confidence regions without the need to know the sampling distribution of bivariate data. The method was proposed by Zhiqiu Hu & Rong-cai Yang (2013) <doi:10.1371/journal.pone.0081179.g001>.

Based on random forest principle, DynForest is able to include multiple longitudinal predictors to provide individual predictions. Longitudinal predictors are modeled through the random forest. The methodology is fully described for a survival outcome in: Devaux, Helmer, Genuer & Proust-Lima (2023) <doi: 10.1177/09622802231206477>.

Scientific and technical article format for the web. Distill articles feature attractive, reader-friendly typography, flexible layout options for visualizations, and full support for footnotes and citations.

This package provides a library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) <https://hdl.handle.net/1854/LU-8644700> for a description and application of methods available in this package.

This package provides R bindings to the dockview JavaScript library <https://dockview.dev/>. Create fully customizable grid layouts (docks) in seconds to include in interactive R reports with R Markdown or Quarto or in shiny apps <https://shiny.posit.co/>. In shiny mode, modify docks by dynamically adding, removing or moving panels or groups of panels from the server function. Choose among 8 stunning themes (dark and light), serialise the state of a dock to restore it later.

Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) <doi:10.1080/01621459.2012.695674>; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) <doi:10.1111/biom.12676>. Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample.