Create a details HTML tag around R objects to place in a Markdown, Rmarkdown and roxygen2 documentation.

For working with the DataRobot predictive modeling platform's API <https://www.datarobot.com/>.

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012>; iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225>; or the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses the Armadillo C++ library and the collapse package for fast estimation. A comprehensive set of methods supports interpretation and visualization, forecasting, and decomposition of the news content of macroeconomic data releases following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

This package provides a zero dependency package containing functions to declare labels and missing values, coupled with associated functions to create (weighted) tables of frequencies and various other summary measures. Some of the base functions have been rewritten to make use of the specific information about the missing values, most importantly to distinguish between empty NA and declared NA values. Some functions have similar functionality with the corresponding ones from packages "haven" and "labelled". The aim is to ensure as much compatibility as possible with these packages, while offering an alternative in the objects of class "declared".

This package provides functions for analyzing dichotomous choice contingent valuation (CV) data. It provides functions for estimating parametric and nonparametric models for single-, one-and-one-half-, and double-bounded CV data. For details, see Aizaki et al. (2022) <doi:10.1007/s42081-022-00171-1>.

This package creates a data frame containing the metadata associated with the documentation of a collection of R packages. It allows for linking topic names to their corresponding documentation online. If you maintain a universe meta-package, it helps create a comprehensive reference for its website.

Data and miscellanea to support the book "Introduction to Data analysis with R for Forensic Scientists." This book was written by James Curran and published by CRC Press in 2010 (ISBN: 978-1-4200-8826-7).

The state-of-the-art algorithms for distance metric learning, including global and local methods such as Relevant Component Analysis, Discriminative Component Analysis, Local Fisher Discriminant Analysis, etc. These distance metric learning methods are widely applied in feature extraction, dimensionality reduction, clustering, classification, information retrieval, and computer vision problems.

This package implements the dynamic panel models described by Allison, Williams, and Moral-Benito (2017 <doi:10.1177/2378023117710578>) in R. This class of models uses structural equation modeling to specify dynamic (lagged dependent variable) models with fixed effects for panel data. Additionally, models may have predictors that are only weakly exogenous, i.e., are affected by prior values of the dependent variable. Options also allow for random effects, dropping the lagged dependent variable, and a number of other specification choices.

Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package BayesTree'.

This package provides functions to download and treat data regarding the Brazilian Amazon region from a variety of official sources.

Simple feature stores and tools for creating personalised feature stores. diseasystore powers feature stores which can automatically link and aggregate features to a given stratification level. These feature stores are automatically time-versioned (powered by the SCDB package) and allows you to easily and dynamically compute features as part of your continuous integration.

Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). DiSTATIS and CovSTATIS are used to analyze multiple distance/covariance matrices collected on the same set of observations. These methods are based on Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012) <doi:10.1002/wics.198>.

This package provides functions for deep learning estimation of Conditional Average Treatment Effects (CATEs) from meta-learner models and Population Average Treatment Effects on the Treated (PATT) in settings with treatment noncompliance using reticulate, TensorFlow and Keras3. Functions in the package also implements the conformal prediction framework that enables computation and illustration of conformal prediction (CP) intervals for estimated individual treatment effects (ITEs) from meta-learner models. Additional functions in the package permit users to estimate the meta-learner CATEs and the PATT in settings with treatment noncompliance using weighted ensemble learning via the super learner approach and R neural networks.

This package implements Design-Robust Meta-Analysis (DR-Meta), a variance-function random-effects framework in which between-study heterogeneity is modelled as a function of a study-level design robustness index, allowing heterogeneity to depend systematically on study quality or design strength rather than being treated as a single nuisance parameter. The package provides profiled restricted maximum likelihood (REML) estimation of the overall effect and variance-function parameters, study-specific weights, heterogeneity diagnostics (tau-squared, I-squared), influence and leave-one-out analysis, and graphical tools including forest plots and influence plots. The DR-Meta framework nests classical fixed-effects and standard random-effects meta-analysis as special cases, making it a strict generalisation of existing approaches.

Time-varying coefficient models for interval censored and right censored survival data including 1) Bayesian Cox model with time-independent, time-varying or dynamic coefficients for right censored and interval censored data studied by Sinha et al. (1999) <doi:10.1111/j.0006-341X.1999.00585.x> and Wang et al. (2013) <doi:10.1007/s10985-013-9246-8>, 2) Spline based time-varying coefficient Cox model for right censored data proposed by Perperoglou et al. (2006) <doi:10.1016/j.cmpb.2005.11.006>, and 3) Transformation model with time-varying coefficients for right censored data using estimating equations proposed by Peng and Huang (2007) <doi:10.1093/biomet/asm058>.

This package provides a key-value dictionary data structure based on R6 class which is designed to be similar usages with other languages dictionary (e.g. Python') with reference semantics and extendabilities by R6.

Several tools for handling block-matrix diagonals and similar constructs are implemented. Block-diagonal matrices can be extracted or removed using two small functions implemented here. In addition, non-square matrices are supported. Block diagonal matrices occur when two dimensions of a data set are combined along one edge of a matrix. For example, trade-flow data in the decompr and gvc packages have each country-industry combination occur along both edges of the matrix.

Shiny modules to import data into an application or addin from various sources, and to manipulate them after that.

Summarizes data frames by calculating various statistics including central tendency, dispersion, shape, and normality diagnostics. Handles numeric, character, and factor columns with NA-aware computations.

This package implements the daily based Morgan-Morgan-Finney (DMMF) soil erosion model (Choi et al., 2017 <doi:10.3390/w9040278>) for estimating surface runoff and sediment budgets from a field or a catchment on a daily basis.

This package provides a toolbox for descriptive statistics, based on the computation of frequency and contingency tables. Several statistical functions and plot methods are provided to describe univariate or bivariate distributions of factors, integer series and numerical series either provided as individual values or as bins.

Easily perform a Monte Carlo simulation to evaluate the cost and carbon, ecological, and water footprints of a set of ideal diets. Pre-processing tools are also available to quickly treat the data, along with basic statistical features to analyze the simulation results â including the ability to establish confidence intervals for selected parameters, such as nutrients and price/emissions. A standard version of the datasets employed is included as well, allowing users easy access to customization. This package brings to R the Python software initially developed by Vandevijvere, Young, Mackay, Swinburn and Gahegan (2018) <doi:10.1186/s12966-018-0648-6>.

This package provides database-backed spatial geometry classes and methods for working with vector spatial data in DuckDB'. The package supports loading, converting, querying, joining, and measuring spatial geometries through familiar sf'-style interfaces while keeping geometry columns lazy inside the database. It integrates with dbProject to preserve database paths, live connections, and spatial table metadata across interactive sessions. The package follows the Simple Features framework described by Pebesma (2018) <doi:10.32614/RJ-2018-009> and uses DuckDB's spatial extension <https://duckdb.org/docs/stable/core_extensions/spatial/overview.html>.