This package creates a data specification that describes the columns of a table (data.frame). Provides methods to read, write, and update the specification. Checks whether a table matches its specification. See specification.data.frame(),read.spec(), write.spec(), as.csv.spec(), respecify.character(), and %matches%.data.frame().

Calculates Zhong's optimal two-/three-stage Phase II designs for single-arm trials, generates target-toxicity decision tables for two-/three-stage Phase I dose-finding, and supports dose-finding simulations using custom decision tables. The Phase II design is based on Zhong (2012) <doi:10.1016/j.cct.2012.07.006>.

An R shiny app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. It is a versatile, general-purpose tool for analyzing textual data. tall features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.

This package provides a convenient R interface to the National Health Service NHS Technology Reference Update Distribution (TRUD) API', allowing users to list available releases for their subscribed items, retrieve metadata, and download release files. For more information on the API, see <https://isd.digital.nhs.uk/trud/users/guest/filters/0/api>.

This package provides a comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz.

This package provides a set of tools to streamline data analysis. Learning both R and introductory statistics at the same time can be challenging, and so we created rigr to facilitate common data analysis tasks and enable learners to focus on statistical concepts. We provide easy-to-use interfaces for descriptive statistics, one- and two-sample inference, and regression analyses. rigr output includes key information while omitting unnecessary details that can be confusing to beginners. Heteroscedasticity-robust ("sandwich") standard errors are returned by default, and multiple partial F-tests and tests for contrasts are easy to specify. A single regression function can fit both linear and generalized linear models, allowing students to more easily make connections between different classes of models.

Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework. The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta. This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers. The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along with their loadings. The number of groups and factors can be estimated if they are unknown.

Rlwrap is a 'readline wrapper', a small utility that uses the GNU readline library to allow the editing of keyboard input for any command. You should consider rlwrap especially when you need user-defined completion (by way of completion word lists) and persistent history, or if you want to program `special effects' using the filter mechanism.

LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points.

This package provides tools and workflow to choose design parameters in Bayesian adaptive single-arm phase II trial designs with binary endpoint (response, success) with possible stopping for efficacy and futility at interim analyses. Also contains routines to determine and visualize operating characteristics. See Kopp-Schneider et al. (2018) <doi:10.1002/bimj.201700209>.

Clean, decompose and aggregate univariate time series following the procedure "Cyclic/trend decomposition using bin interpolation" and the Logbox method for flagging outliers, both detailed in Ritter, F.: Technical note: A procedure to clean, decompose, and aggregate time series, Hydrol. Earth Syst. Sci., 27, 349â 361, <doi:10.5194/hess-27-349-2023>, 2023.

Partitions data points (variables) into communities/clusters, similar to clustering algorithms such as k-means and hierarchical clustering. This package implements a clustering algorithm based on a new metric CORD, defined for high-dimensional parametric or semiparametric distributions. For more details see Bunea et al. (2020), Annals of Statistics <doi:10.1214/18-AOS1794>.

Estimate common causal parameters using double/debiased machine learning as proposed by Chernozhukov et al. (2018) <doi:10.1111/ectj.12097>. ddml simplifies estimation based on (short-)stacking as discussed in Ahrens et al. (2024) <doi:10.1002/jae.3103>, which leverages multiple base learners to increase robustness to the underlying data generating process.

Estimation of fully and partially observed Exponential-Family Random Network Models (ERNM). Exponential-family Random Graph Models (ERGM) and Gibbs Fields are special cases of ERNMs and can also be estimated with the package. Please cite Fellows and Handcock (2012), "Exponential-family Random Network Models" available at <doi:10.48550/arXiv.1208.0121>.

When you want to install R package or download file from GitHub, but you can't access GitHub, this package helps you install R packages or download file from GitHub via the proxy website <https://gh-proxy.com/> or <https://ghfast.top/>, which is in real-time sync with GitHub.

Impute observed values below the limit of detection (LOD) via censored likelihood multiple imputation (CLMI) in single-pollutant models, developed by Boss et al (2019) <doi:10.1097/EDE.0000000000001052>. CLMI handles exposure detection limits that may change throughout the course of exposure assessment. lodi provides functions for imputing and pooling for this method.

This package implements the One Rule (OneR) Machine Learning classification algorithm (Holte, R.C. (1993) <doi:10.1023/A:1022631118932>) with enhancements for sophisticated handling of numeric data and missing values together with extensive diagnostic functions. It is useful as a baseline for machine learning models and the rules are often helpful heuristics.

The portmanteau local feature discriminant approach first identifies the local discriminant features and their differential structures, then constructs the discriminant rule by pooling the identified local features together. This method is applicable to high-dimensional matrix-variate data. See the paper by Xu, Luo and Chen (2023, <doi:10.1007/s13171-021-00255-2>).

Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)).

Quantile-based estimators (Q-estimators) can be used to fit any parametric distribution, using its quantile function. Q-estimators are usually more robust than standard maximum likelihood estimators. The method is described in: Sottile G. and Frumento P. (2022). Robust estimation and regression with parametric quantile functions. <doi:10.1016/j.csda.2022.107471>.

Strength training prescription using percent-based approach requires numerous computations and assumptions. STMr package allow users to estimate individual reps-max relationships, implement various progression tables, and create numerous set and rep schemes. The STMr package is originally created as a tool to help writing JovanoviÄ M. (2020) Strength Training Manual <ISBN:979-8604459898>.

Offers Bayesian semiparametric density estimation and tail-index estimation for heavy tailed data, by using a parametric, tail-respecting transformation of the data to the unit interval and then modeling the transformed data with a purely nonparametric logistic Gaussian process density prior. Based on Tokdar et al. (2022) <doi:10.1080/01621459.2022.2104727>.

Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).

This is a statistical tool interactive that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) <DOI: 10.1080/03610918.2019.1693596>).