Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model. The curvatures are global measures of assessing whether a model/data set combination is close-to-linear or not. See Bates and Watts (1980) <doi:10.1002/9780470316757> and Ratkowsky and Reddy (2017) <doi:10.1093/aesa/saw098> for details.

Fitting multivariate data patterns with local principal curves, including tools for data compression (projection) and measuring goodness-of-fit; with some additional functions for mean shift clustering. See Einbeck, Tutz and Evers (2005) <doi:10.1007/s11222-005-4073-8> and Ameijeiras-Alonso and Einbeck (2023) <doi:10.1007/s11634-023-00575-1>.

Distance between multivariate generalised Gaussian distributions, as presented by N. Bouhlel and A. Dziri (2019) <doi:10.1109/LSP.2019.2915000>. Manipulation of multivariate generalised Gaussian distributions (methods presented by Gomez, Gomez-Villegas and Marin (1998) <doi:10.1080/03610929808832115> and Pascal, Bombrun, Tourneret and Berthoumieu (2013) <doi:10.1109/TSP.2013.2282909>).

It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.

Model mixed integer linear programs in an algebraic way directly in R. The model is solver-independent and thus offers the possibility to solve a model with different solvers. It currently only supports linear constraints and objective functions. See the ompr website <https://dirkschumacher.github.io/ompr/> for more information, documentation and examples.

This package performs partial principal component analysis of a large sparse matrix. The matrix may be stored as a list of matrices to be concatenated (implicitly) horizontally. Useful application includes cases where the number of total nonzero entries exceed the capacity of 32 bit integers (e.g., with large Single Nucleotide Polymorphism data).

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().

This package provides functions to implement the stability controlled quasi-experiment (SCQE) approach to study the effects of newly adopted treatments that were not assigned at random. This package contains tools to help users avoid making statistical assumptions that rely on infeasible assumptions. Methods developed in Hazlett (2019) <doi:10.1002/sim.8717>.

Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) (<doi:10.1080/10618600.2015.1049700>).

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 shiny application that helps learners connect regression tables to fitted generalised linear models. Users construct models via drag-and-drop controls, obtain fitted equations and plain-language explanations generated by a large language model, and can view plots of the fitted model in settings with a single continuous covariate.

Integrates concepts of stochastic demography into species distribution modelling. The main approach maximizes a likelihood function based on environmental information and presence/absence records. This is used to reconstruct species fundamental ecological niches and to project their potential geographic range. Data requirements include species presence/absence records and a timeseries of environmental data.

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.

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.

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.

Toolkit to interact with the SOAP web services of the SEI (Sistema Eletronico de Informacoes), the electronic system for document and process management widely used by Brazilian public administration bodies. Provides functions to build the SOAP envelopes, perform the requests, handle SOAP faults, and parse the XML responses into data frames. Covers process and document queries, listing services, write operations (creating processes and documents, sending and signing off processes, blocks, deadlines and markers) and the permission services of the companion SIP system. Note that access to the web services is restricted by the server to previously authorized network addresses. For more information about the SEI system and its web services see <https://www.gov.br/gestao/pt-br/assuntos/processo-eletronico-nacional>.

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.

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>.

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.

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>.