This package provides a toolbox for writing knitr', Sweave or other LaTeX'- or markdown'-based reports and to prettify the output of various estimated models.

Enable users to measure and record the execution time of pipe operations (using |>) with optional logging to dataframes and output to the console.

This package provides a simple interface for extracting various elements from the publicly available PubMed XML files, incorporating PubMed's regular updates, and combining the data with the NIH Open Citation Collection. See Schoenbachler and Hughey (2021) <doi:10.7717/peerj.11071>.

This package provides a toolbox for making R functions and capabilities more accessible to students and professionals from Epidemiology and Public Health related disciplines. Includes a function to report coefficients and confidence intervals from models using robust standard errors (when available), functions that expand ggplot2 plots and functions relevant for introductory papers in Epidemiology or Public Health. Please note that use of the provided data sets is for educational purposes only.

The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to estimate the phylogenetic heritability of continuous traits, to test hypotheses of neutral evolution versus stabilizing selection, to quantify the strength of stabilizing selection, to estimate measurement error and to make predictions about the evolution of a phenotype and phenotypic variation in a population. The package implements combined maximum likelihood and Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed Model, fast parallel likelihood calculation, maximum likelihood inference of the genotypic values at the tips, functions for summarizing and plotting traces and posterior samples, functions for simulation of a univariate continuous trait evolution model along a phylogenetic tree. So far, the package has been used for estimating the heritability of quantitative traits in macroevolutionary and epidemiological studies, see e.g. Bertels et al. (2017) <doi:10.1093/molbev/msx246> and Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for parallel POUMM likelihood calculation has been published in Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.

This package provides functions to read and write APE-compatible phylogenetic trees in NEXUS and Newick formats, while preserving annotations.

Convert Chinese characters into Pinyin (the official romanization system for Standard Chinese in mainland China, Malaysia, Singapore, and Taiwan. See <https://en.wikipedia.org/wiki/Pinyin> for details), Sijiao (four or five numerical digits per character. See <https://en.wikipedia.org/wiki/Four-Corner_Method>.), Wubi (an input method with five strokes. See <https://en.wikipedia.org/wiki/Wubi_method>) or user-defined codes.

Construct a principal surface that are two-dimensional surfaces that pass through the middle of a p-dimensional data set. They minimise the distance from the data points, and provide a nonlinear summary of data. The surfaces are nonparametric and their shape is suggested by the data. The formation of a surface is found using an iterative procedure which starts with a linear summary, typically with a principal component plane. Each successive iteration is a local average of the p-dimensional points, where an average is based on a projection of a point onto the nonlinear surface of the previous iteration. For more information on principal surfaces, see Ganey, R. (2019, "https://open.uct.ac.za/items/4e655d7d-d10c-481b-9ccc-801903aebfc8").

High-quality real-world data can be transformed into scientific real-world evidence for regulatory and healthcare decision-making using proven analytical methods and techniques. For example, propensity score (PS) methodology can be applied to select a subset of real-world data containing patients that are similar to those in the current clinical study in terms of baseline covariates, and to stratify the selected patients together with those in the current study into more homogeneous strata. Then, statistical methods such as the power prior approach or composite likelihood approach can be applied in each stratum to draw inference for the parameters of interest. This package provides functions that implement the PS-integrated real-world evidence analysis methods such as Wang et al. (2019) <doi:10.1080/10543406.2019.1657133>, Wang et al. (2020) <doi:10.1080/10543406.2019.1684309>, and Chen et al. (2020) <doi:10.1080/10543406.2020.1730877>.

Psychometric mixture models based on flexmix infrastructure. At the moment Rasch mixture models with different parameterizations of the score distribution (saturated vs. mean/variance specification), Bradley-Terry mixture models, and MPT mixture models are implemented. These mixture models can be estimated with or without concomitant variables. See Frick et al. (2012) <doi:10.18637/jss.v048.i07> and Frick et al. (2015) <doi:10.1177/0013164414536183> for details on the Rasch mixture models.

Retrieves a pluscode by inputting latitude and longitude. Includes additional functions to retrieve neighbouring pluscodes'.

Defines functions to describe regression models using only pre-computed summary statistics (i.e. means, variances, and covariances) in place of individual participant data. Possible models include linear models for linear combinations, products, and logical combinations of phenotypes. Implements methods presented in Wolf et al. (2021) <doi:10.3389/fgene.2021.745901> Wolf et al. (2020) <doi:10.1142/9789811215636_0063> and Gasdaska et al. (2019) <doi:10.1142/9789813279827_0036>.

The primary goal of phase I clinical trials is to find the maximum tolerated dose (MTD). To reach this objective, we introduce a new design for phase I clinical trials, the posterior predictive (PoP) design. The PoP design is an innovative model-assisted design that is as simply as the conventional algorithmic designs as its decision rules can be pre-tabulated prior to the onset of trial, but is of more flexibility of selecting diverse target toxicity rates and cohort sizes. The PoP design has desirable properties, such as coherence and consistency. Moreover, the PoP design provides better empirical performance than the BOIN and Keyboard design with respect to high average probabilities of choosing the MTD and slightly lower risk of treating patients at subtherapeutic or overly toxic doses.

The purpose of PH1XBAR is to build a Phase I Shewhart control chart for the basic Shewhart, the variance components and the ARMA models in R for subgrouped and individual data. More details can be found: Yao and Chakraborti (2020) <doi: 10.1002/qre.2793>, Yao and Chakraborti (2021) <doi: 10.1080/08982112.2021.1878220>, and Yao et al. (2023) <doi: 10.1080/00224065.2022.2139783>.

This package provides a simple interface in the form of R6 classes for executing tasks in parallel, tracking their progress, and displaying accurate progress bars.

In this record linkage package, data preprocessing has been meticulously executed to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. This approach facilitates seamless data integration and analysis across various datasets. While users have the flexibility to modify variable names, the system intelligently ensures that changes are only permitted when they do not compromise data consistency or essential variable essence.

Package for corpus analysis using the Corpus Workbench ('CWB', <https://cwb.sourceforge.io>) as an efficient back end for indexing and querying large corpora. The package offers functionality to flexibly create subcorpora and to carry out basic statistical operations (count, co-occurrences etc.). The original full text of documents can be reconstructed and inspected at any time. Beyond that, the package is intended to serve as an interface to packages implementing advanced statistical procedures. Respective data structures (document-term matrices, term-co-occurrence matrices etc.) can be created based on the indexed corpora.

An add-on to the party package, with a faster implementation of the partial-conditional permutation importance for random forests. The standard permutation importance is implemented exactly the same as in the party package. The conditional permutation importance can be computed faster, with an option to be backward compatible to the party implementation. The package is compatible with random forests fit using the party and the randomForest package. The methods are described in Strobl et al. (2007) <doi:10.1186/1471-2105-8-25> and Debeer and Strobl (2020) <doi:10.1186/s12859-020-03622-2>.

Interfaces and methods for variable selection in Partial Least Squares. The methods include filter methods, wrapper methods and embedded methods. Both regression and classification is supported.

Search for R packages on CRAN directly from the R console, based on the packages titles, short and long descriptions, or other fields. Combine multiple keywords with logical operators ('and', or'), view detailed information on any package and keep track of the latest package contributions to CRAN. If you don't want to search from the R console, use the comfortable R Studio add-in.

This package provides functions to automatically build a directory structure for a new R project. Using this structure, ProjectTemplate automates data loading, preprocessing, library importing and unit testing.

Achieve internal conversions of mass units, molar units, and volume units commonly used in pharmacokinetics, as well as conversions between mass units and molar units.

Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019) <https://hal.archives-ouvertes.fr/hal-02002275>. It can be used with univariate and multivariate data.

Create, transform, and summarize custom random variables with distribution functions (analogues of p*()', d*()', q*()', and r*() functions from base R). Two types of distributions are supported: "discrete" (random variable has finite number of output values) and "continuous" (infinite number of values in the form of continuous random variable). Functions for distribution transformations and summaries are available. Implemented approaches often emphasize approximate and numerical solutions: all distributions assume finite support and finite values of density function; some methods implemented with simulation techniques.