Aims at loading Google Adwords data into R. Adwords is an online advertising service that enables advertisers to display advertising copy to web users (see <https://developers.google.com/adwords/> for more information). Therefore the package implements three main features. First, the package provides an authentication process for R with the Google Adwords API (see <https://developers.google.com/adwords/api/> for more information) via OAUTH2. Second, the package offers an interface to apply the Adwords query language in R and query the Adwords API with ad-hoc reports. Third, the received data are transformed into suitable data formats for further data processing and data analysis.

This package provides functions for (1) computing diagnostic test statistics (sensitivity, specificity, etc.) from confusion matrices with adjustment for various base rates or known prevalence based on McCaffrey et al (2003) <doi:10.1007/978-1-4615-0079-7_1>, (2) computing optimal cut-off scores with different criteria including maximizing sensitivity, maximizing specificity, and maximizing the Youden Index from Youden (1950) <doi:10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3>, and (3) displaying and comparing classification statistics and area under the receiver operating characteristic (ROC) curves or area under the curves (AUC) across consecutive categories for ordinal variables.

This package provides methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data.

Utility functions, datasets and extended examples for survival analysis. This extends a range of other packages, some simple wrappers for time-to-event analyses, datasets, and extensive examples in HTML with R scripts. The package also supports the course Biostatistics III entitled "Survival analysis for epidemiologists in R".

An R interface for the Brown Dog which allows researchers to leverage Brown Dog Services that provides modules to identify the conversion options for a file, to convert file to appropriate format, or to extract data from a file. See <http://browndog.ncsa.illinois.edu/> for more information.

This package performs Correlated Meta-Analysis ('corrmeta') across multiple OMIC scans, accounting for hidden non-independencies between elements of the scans due to overlapping samples, related samples, or other information. For more information about the method, refer to the paper Province MA. (2013) <doi:10.1142/9789814447973_0023>.

Helps automate Quarto website creation for small academic groups. Builds a database-like structure of people, projects and publications, linking them together with a string-based ID system. Then, provides functions to automate production of clean markdown for these structures, and in-built CSS formatting using CSS flexbox.

This package provides friendly wrappers for creating duckdb'-backed connections to tabular datasets ('csv', parquet, etc) on local or remote file systems. This mimics the behaviour of "open_dataset" in the arrow package, but in addition to S3 file system also generalizes to any list of http URLs.

Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations. It is also possible to consider random data and ACK probabilities.

Simplifies some complicated and labor intensive processes involved in exploring and explaining data. Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data. Also includes some convenience features designed to remove as much redundant typing as possible.

This package implements stochastic simulations of community assembly (ecological diversification) using customizable ecospace frameworks (functional trait spaces). Provides a wrapper to calculate common ecological disparity and functional ecology statistical dynamics as a function of species richness. Functions are written so they will work in a parallel-computing environment.

This package provides a tool for conducting exact parametric regression-based causal mediation analysis of binary outcomes as described in Samoilenko, Blais and Lefebvre (2018) <doi:10.1353/obs.2018.0013>; Samoilenko, Lefebvre (2021) <doi:10.1093/aje/kwab055>; and Samoilenko, Lefebvre (2023) <doi:10.1002/sim.9621>.

An RStudio addin for editing a data.frame or a tibble'. You can delete, add or update a data.frame without coding. You can get resultant data as a data.frame'. In the package, modularized shiny app codes are provided. These modules are intended for reuse across applications.

Optimal experimental designs for functional linear and functional generalised linear models, for scalar responses and profile/dynamic factors. The designs are optimised using the coordinate exchange algorithm. The methods are discussed by Michaelides (2023) <https://eprints.soton.ac.uk/474982/1/Thesis_DamianosMichaelides_Final_pdfa_1_.pdf>.

This package provides a set of helper functions for constructing file paths relative to the root of various types of projects, such as R packages, Git repositories, and more. File paths are specified with function arguments, or `$` to navigate into folders to specific files supported by auto-completion.

This package provides features for searching, converting, analyzing, plotting, and exporting data effortlessly by inputting feature IDs. Enables easy retrieval of feature information, conversion of ID types, gene enrichment analysis, publication-level figures, group interaction plotting, and result export in one Excel file for seamless sharing and communication.

An R interface to the GPTZero API (<https://gptzero.me/docs>). Allows users to classify text into human and computer written with probabilities. Formats the data into data frames where each sentence is an observation. Paragraph-level and document-level predictions are organized to align with the sentences.

Facilitates fitting measurement error and missing data imputation models using integrated nested Laplace approximations, according to the method described in Skarstein, Martino and Muff (2023) <doi:10.1002/bimj.202300078>. See Skarstein and Muff (2024) <doi:10.48550/arXiv.2406.08172> for details on using the package.

Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2019) <doi:10.1080/01621459.2018.1520115>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.

Insieme di funzioni di supporto al volume "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. This package contains sets of functions defined in "Laboratorio di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006. Function names and docs are in italian as well.

Evaluates whether the relationship between two vectors is linear or nonlinear. Performs a test to determine how well a linear model fits the data compared to higher order polynomial models. Jhang et al. (2004) <doi:10.1043/1543-2165(2004)128%3C44:EOLITC%3E2.0.CO;2>.

The lava estimation is used to recover signals that is the sum of a sparse signal and a dense signal. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) <doi:10.1214/16-AOS1434>.

Collection of functions to compute within-study covariances for different effect sizes, data visualization, and single and multiple imputations for missing data. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD).

Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using Stan'.