Makes accessing and loading the Canadian Election Study (<http://www.ces-eec.ca/>, <https://ces-eec.arts.ubc.ca/>, <https://search1.odesi.ca/#/>) surveys into the R workspace more efficient by downloading a requested survey and loading it as a data object. This removes the need to locate, download, load, and change working directories when working with the Canadian Election Study surveys.

Data frame, tibble, or tbl objects are converted to data package objects using specific metadata labels (name, version, title, homepage, description). A data package object ('dpkg') can be written to disk as a parquet file or released to a GitHub repository. Data package objects can be read into R from online repositories and downloaded files are cached locally across R sessions.

This package provides implementations of computationally efficient maximum likelihood parameter estimation algorithms for models that represent linear dynamical systems. Currently, one such algorithm is implemented for the one-dimensional cumulative structural equation model with shock-error output measurement equation and assumptions of normality and independence. The corresponding scientific paper is yet to be published, therefore the relevant reference will be provided later.

This package provides tools for importing, analyzing and visualizing ego-centered network data. Supports several data formats, including the export formats of EgoNet', EgoWeb 2.0 and openeddi'. An interactive (shiny) app for the intuitive visualization of ego-centered networks is provided. Also included are procedures for creating and visualizing Clustered Graphs (Lerner 2008 <DOI:10.1109/PACIFICVIS.2008.4475458>).

This package provides a highly configurable jQuery plugin offering a simple interface to create complex queries/filters in Shiny'. The outputted rules can easily be parsed into a set of R and/or SQL queries and used to filter data. Custom parsing of the rules is also supported. For more information about jQuery QueryBuilder see <https://querybuilder.js.org/>.

Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).

This package provides functions for vectorised conditional recoding of variables. case_when() enables you to vectorise multiple if and else statements (like CASE WHEN in SQL'). if_else() is a stricter and more predictable version of ifelse() in base that preserves attributes. These functions are forked from dplyr with all package dependencies removed and behave identically to the originals.

Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods. Exact procedures are performed when computationally possible. Monte Carlo and Asymptotic procedures are performed otherwise. For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.

User friendly functions for power and sample size analysis at one-way and two-way ANOVA settings take either effect size or delta and sigma as arguments. They are designed for both one-way and two-way ANOVA settings. In addition, a function for plotting power curves is available for power comparison, which can be easily visualized by statisticians and clinical researchers.

This package provides a secure and user-friendly interface to interact with the Plug <https://plugbytpf.com.br> API'. It enables developers to store and manage tokens securely using the keyring package, retrieve data from API endpoints with the httr2 package, and handle large datasets with chunked data fetching. Designed for simplicity and security, the package facilitates seamless integration with Plug ecosystem.

The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two variables. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). PPS can be useful for data exploration purposes, in the same way correlation analysis is. For more information on PPS, see <https://github.com/paulvanderlaken/ppsr>.

Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".

This package provides a comprehensive set of string manipulation functions based on those found in Python without relying on reticulate'. It provides functions that intend to (1) make it easier for users familiar with Python to work with strings, (2) reduce the complexity often associated with string operations, (3) and enable users to write more readable and maintainable code that manipulates strings.

This queue is a data structure that lets parallel processes send and receive messages, and it can help coordinate the work of complicated parallel tasks. Processes can push new messages to the queue, pop old messages, and obtain a log of all the messages ever pushed. File locking preserves the integrity of the data even when multiple processes access the queue simultaneously.

An engine for univariate time series forecasting using different regression models in an autoregressive way. The engine provides an uniform interface for applying the different models. Furthermore, it is extensible so that users can easily apply their own regression models to univariate time series forecasting and benefit from all the features of the engine, such as preprocessings or estimation of forecast accuracy.

Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) <doi:10.1016/j.biopsych.2005.09.014>.

The Recode library converts files between character sets and usages. It recognises or produces over 200 different character sets (or about 300 if combined with an iconv library) and transliterates files between almost any pair. When exact transliteration are not possible, it gets rid of offending characters or falls back on approximations. The recode program is a handy front-end to the library.

This package provides functions for calculating the acute chronic workload ratio using three different methods: exponentially weighted moving average (EWMA), rolling average coupled (RAC) and rolling averaged uncoupled (RAU). Examples of this methods can be found in Williams et al. (2017) <doi:10.1136/bjsports-2016-096589> for EWMA and Windt & Gabbet (2018) for RAC and RAU <doi: 10.1136/bjsports-2017-098925>.

This package produces an economic evaluation of a sample of suitable variables of cost and effectiveness / utility for two or more interventions, e.g. from a Bayesian model in the form of MCMC simulations. This package computes the most cost-effective alternative and produces graphical summaries and probabilistic sensitivity analysis, see Baio et al (2017) <doi:10.1007/978-3-319-55718-2>.

Bayesian Age-Period-Cohort Modeling and Prediction using efficient Markov Chain Monte Carlo Methods. This is the R version of the previous BAMP software as described in Volker Schmid and Leonhard Held (2007) <DOI:10.18637/jss.v021.i08> Bayesian Age-Period-Cohort Modeling and Prediction - BAMP, Journal of Statistical Software 21:8. This package includes checks of convergence using Gelman's R.

Implementing the computational phase of the Causes of Outcome Learning approach as described in Rieckmann, Dworzynski, Arras, Lapuschkin, Samek, Arah, Rod, Ekstrom. 2022. Causes of outcome learning: A causal inference-inspired machine learning approach to disentangling common combinations of potential causes of a health outcome. International Journal of Epidemiology <doi:10.1093/ije/dyac078>. The optional ggtree package can be obtained through Bioconductor.

The framework provides functions to generate ODEs of reaction networks, parameter transformations, observation functions, residual functions, etc. The framework follows the paradigm that derivative information should be used for optimization whenever possible. Therefore, all major functions produce and can handle expressions for symbolic derivatives. The methods used in dMod were published in Kaschek et al, 2019, <doi:10.18637/jss.v088.i10>.

Conduct detection and attribution of climate change using methods including optimal fingerprinting via generalized total least squares or estimating equation approach from Ma et al. (2023) <doi:10.1175/JCLI-D-22-0681.1>. Provide shrinkage estimators for covariance matrix from Ledoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4>, and Ledoit and Wolf (2017) <doi:10.2139/ssrn.2383361>.

Collection of functions for fitting and interpreting distributed lag interaction models (DLIM). A DLIM regresses a scalar outcome on repeated measures of exposure and allows for modification by a continuous variable. Includes a dlim() function for fitting, predict() function for inference, and plotting functions for visualization. Details on methodology are described in Demateis et al. (2024) <doi:10.1002/env.2843>.