This package provides a method for modeling robust generalized autoregressive conditional heteroskedasticity (Garch) (1,1) processes, providing robustness toward additive outliers instead of innovation outliers. This work is based on the methodology described by Muler and Yohai (2008) <doi:10.1016/j.jspi.2007.11.003>.

Embeds sources and headers from Tina's Random Number Generator ('TRNG') C++ library. Exposes some functionality for easier access, testing and benchmarking into R. Provides examples of how to use parallel RNG with RcppParallel'. The methods and techniques behind TRNG are illustrated in the package vignettes and examples. Full documentation is available in Bauke (2021) <https://github.com/rabauke/trng4/blob/v4.23.1/doc/trng.pdf>.

Allows the user to access functionality in the CDK', a Java framework for cheminformatics. This allows the user to load molecules, evaluate fingerprints, calculate molecular descriptors and so on. In addition, the CDK API allows the user to view structures in 2D.

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses). The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers decoupled and/or censored count data with two main functions. The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table. The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities.

Utilities for reading, writing, and managing RCDF files, including encryption and decryption support. It offers a flexible interface for handling data stored in encrypted Parquet format, along with metadata extraction, key management, and secure operations using AES and RSA encryptions.

S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. These tools make use of Vector AutoRegressive models (VARs). The weather generator model is then saved as an object and is calibrated by daily instrumental "Gaussianized" time series through the vars package tools. Once obtained this model, it can it can be used for weather generations and be adapted to work with several climatic monthly time series.

An R and Repast integration tool for running individual-based (IbM) simulation models developed using Repast Simphony Agent-Based framework directly from R code supporting multicore execution. This package integrates Repast Simphony models within R environment, making easier the tasks of running and analyzing model output data for automated parameter calibration and for carrying out uncertainty and sensitivity analysis using the power of R environment.

We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.

Utilities for processing input and output files associated with the Raven Hydrological Modelling Framework. Includes various plotting functions, model diagnostics, reading output files into extensible time series format, and support for writing Raven input files. The RavenR package is also archived at Chlumsky et al. (2020) <doi:10.5281/zenodo.4248183>. The Raven Hydrologic Modelling Framework method can be referenced with Craig et al. (2020) <doi:10.1016/j.envsoft.2020.104728>.

Enhances the R Optimization Infrastructure ('ROI') package with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.

This package implements the t-walk algorithm, a general-purpose, self-adjusting Markov Chain Monte Carlo (MCMC) sampler for continuous distributions as described by Christen & Fox (2010) <doi:10.1214/10-BA603>. The t-walk requires no tuning and is robust for a wide range of target distributions, including high-dimensional and multimodal problems. This implementation includes an option for running multiple chains in parallel to accelerate sampling and facilitate convergence diagnostics.

Generate causally-simulated data to serve as ground truth for evaluating methods in causal discovery and effect estimation. The package provides tools to assist in defining functions based on specified edges, and conversely, defining edges based on functions. It enables the generation of data according to these predefined functions and causal structures. This is particularly useful for researchers in fields such as artificial intelligence, statistics, biology, medicine, epidemiology, economics, and social sciences, who are developing a general or a domain-specific methods to discover causal structures and estimate causal effects. Data simulation adheres to principles of structural causal modeling. Detailed methodologies and examples are documented in our vignette, available at <https://htmlpreview.github.io/?https://github.com/herdiantrisufriyana/rcausim/blob/master/doc/causal_simulation_exemplar.html>.

This package implements two-sample tests for paired data with missing values (Fong, Huang, Lemos and McElrath 2018, Biostatics, <doi:10.1093/biostatistics/kxx039>) and modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test.

Many packages in the r-dcm family take similar arguments, which are checked for expected structures and values. Rather than duplicating code across several packages, commonly used check functions are included here. This package can then be imported to access the check functions in other packages.

Listings are often part of the submission of clinical trial data in regulatory settings. We provide a framework for the specific formatting features often used when displaying large datasets in that context.

This package provides API to Melbourne pedestrian and weather data <https://data.melbourne.vic.gov.au> in tidy data form.

For the calculation of sample size or power in a two-group repeated measures design, accounting for attrition and accommodating a variety of correlation structures for the repeated measures; details of the method can be found in the scientific paper: Donald Hedeker, Robert D. Gibbons, Christine Waternaux (1999) <doi:10.3102/10769986024001070>.

Non-inferiority test and diagnostic test are very important in clinical trails. This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test.

Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) <doi:10.2307/2286290>), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>, Rosenberger and Lachin (2015) <doi:10.1002/9781118742112>) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) <doi:10.1198/016214506000000906>) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) <doi:10.1198/sbr.2009.0056>, Bello and Sabo (2016) <doi:10.1080/00949655.2015.1114116>), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) <doi:10.1016/j.ejca.2007.01.006>) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) <doi:10.1111/biom.12337>, Williamson and Villar (2019) <doi:10.1111/biom.13119>).

This package provides a machine learning algorithm that merges satellite and ground precipitation data using Random Forest for spatial prediction, residual modeling for bias correction, and quantile mapping for adjustment, ensuring accurate estimates across temporal scales and regions.

Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.

This package provides functions for simulating Markov chains using the Barker proposal to compute Markov chain Monte Carlo (MCMC) estimates of expectations with respect to a target distribution on a real-valued vector space. The Barker proposal, described in Livingstone and Zanella (2022) <doi:10.1111/rssb.12482>, is a gradient-based MCMC algorithm inspired by the Barker accept-reject rule. It combines the robustness of simpler MCMC schemes, such as random-walk Metropolis, with the efficiency of gradient-based methods, such as the Metropolis adjusted Langevin algorithm. The key function provided by the package is sample_chain(), which allows sampling a Markov chain with a specified target distribution as its stationary distribution. The chain is sampled by generating proposals and accepting or rejecting them using a Metropolis-Hasting acceptance rule. During an initial warm-up stage, the parameters of the proposal distribution can be adapted, with adapters available to both: tune the scale of the proposals by coercing the average acceptance rate to a target value; tune the shape of the proposals to match covariance estimates under the target distribution. As well as the default Barker proposal, the package also provides implementations of alternative proposal distributions, such as (Gaussian) random walk and Langevin proposals. Optionally, if BridgeStan's R interface <https://roualdes.us/bridgestan/latest/languages/r.html>, available on GitHub <https://github.com/roualdes/bridgestan>, is installed, then BridgeStan can be used to specify the target distribution to sample from.

R6 class interface for handling relational database connections using DBI package as backend. The class allows handling of connections to e.g. PostgreSQL, MariaDB and SQLite. The purpose is having an intuitive object allowing straightforward handling of SQL databases.

This package implements two methods of estimating runs scored in a softball scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical distribution using multinomial random simulation. Scores are based on player-specific input probabilities (out, single, double, triple, walk, and homerun). Optional inputs include probability of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether a player is "fast" (e.g. the player could stretch home). These probabilities may be calculated from common player statistics that are publicly available on team's webpages. Scores are evaluated based on a nine-player lineup and may be used to compare lineups, evaluate base scenarios, and compare the offensive potential of individual players. Manuscript forthcoming. See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for implementation of discrete Markov chains.