This package creates an HTML vertical timeline from a data frame as an input for rmarkdown documents and shiny applications.

Cross-validation methods of regression models that exploit features of various modeling functions to improve speed. Some of the methods implemented in the package are novel, as described in the package vignettes; for general introductions to cross-validation, see, for example, Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (2021, ISBN 978-1-0716-1417-4, Secs. 5.1, 5.3), "An Introduction to Statistical Learning with Applications in R, Second Edition", and Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009, ISBN 978-0-387-84857-0, Sec. 7.10), "The Elements of Statistical Learning, Second Edition".

This package provides function to create, read, write, and work with iCalendar files (which typically have .ics or .ical extensions), and the scheduling data, calendars and timelines of people, organisations and other entities that they represent. iCalendar is an open standard for exchanging calendar and scheduling information between users and computers, described at <https://icalendar.org/>.

Uses optimal transport distances to find probabilistic matching estimators for causal inference. These methods are described in Dunipace, Eric (2021) <arXiv:2109.01991>. The package will build the weights, estimate treatment effects, and calculate confidence intervals via the methods described in the paper. The package also supports several other methods as described in the help files.

Check for namespace collisions between a string input (your function or package name) and half a million packages and functions on CRAN.

Accelerate Bayesian analytics workflows in R through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the numpyro python package.

An exact and a variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations.

Multiple comparison techniques are typically applied following an F test from an ANOVA to decide which means are significantly different from one another. As an alternative to traditional methods, cluster analysis can be performed to group the means of different treatments into non-overlapping clusters. Treatments in different groups are considered statistically different. Several approaches have been proposed, with varying clustering methods and cut-off criteria. This package implements cluster-based multiple comparisons tests and also provides a visual representation in the form of a dendrogram. Di Rienzo, J. A., Guzman, A. W., & Casanoves, F. (2002) <jstor.org/stable/1400690>. Bautista, M. G., Smith, D. W., & Steiner, R. L. (1997) <doi:10.2307/1400402>.

This package provides functions for loading large (10M+ lines) CSV and other delimited files, similar to read.csv, but typically faster and using less memory than the standard R loader. While not entirely general, it covers many common use cases when the types of columns in the CSV file are known in advance. In addition, the package provides a class int64', which represents 64-bit integers exactly when reading from a file. The latter is useful when working with 64-bit integer identifiers exported from databases. The CSV file loader supports common column types including integer', double', string', and int64', leaving further type transformations to the user.

This package provides the source and examples for James P. Howard, II, "Computational Methods for Numerical Analysis with R," <https://jameshoward.us/cmna/>, a book on numerical methods in R.

Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan, 2025; <doi:10.48550/arXiv.2405.17290>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).

Data cleaning functions for classes logical, factor, numeric, character, currency and Date to make data cleaning fast and easy. Relying on very few dependencies, it provides smart guessing, but with user options to override anything if needed.

This package implements the combined cluster and discriminant analysis method for finding homogeneous groups of data with known origin as described in Kovacs et. al (2014): Classification into homogeneous groups using combined cluster and discriminant analysis (CCDA). Environmental Modelling & Software. <doi:10.1016/j.envsoft.2014.01.010>.

This package provides functions supporting the common needs of packages ChemoSpec and ChemoSpec2D'.

Recalibrate risk scores (predicting binary outcomes) to improve clinical utility of risk score using weighted logistic or constrained logistic recalibration methods. Additionally, produces plots to assess the potential for recalibration to improve the clinical utility of a risk model. Methods are described in detail in Mishra, A. (2019) "Methods for Risk Markers that Incorporate Clinical Utility" <http://hdl.handle.net/1773/44068>.

Estimates sugar beet canopy closure with remotely sensed leaf area index and estimates when action might be needed to protect the crop from a Leaf Spot epidemic with a negative prognosis model based on published models.

Implementation of the Cluster Estimated Standard Errors (CESE) proposed in Jackson (2020) <DOI:10.1017/pan.2019.38> to compute clustered standard errors of linear coefficients in regression models with grouped data.

Cellular cooperation compromises the plating efficiency-based analysis of clonogenic survival data. This tool provides functions that enable a robust analysis of colony formation assay (CFA) data in presence or absence of cellular cooperation. The implemented method has been described in Brix et al. (2020). (Brix, N., Samaga, D., Hennel, R. et al. "The clonogenic assay: robustness of plating efficiency-based analysis is strongly compromised by cellular cooperation." Radiat Oncol 15, 248 (2020). <doi:10.1186/s13014-020-01697-y>) Power regression for parameter estimation, calculation of survival fractions, uncertainty analysis and plotting functions are provided.

Reads chromatograms from binary formats into R objects. Currently supports conversion of Agilent ChemStation', Agilent MassHunter', Shimadzu LabSolutions', ThermoRaw', and Varian Workstation files as well as various text-based formats. In addition to its internal parsers, chromConverter contains bindings to parsers in external libraries, such as Aston <https://github.com/bovee/aston>, Entab <https://github.com/bovee/entab>, rainbow <https://rainbow-api.readthedocs.io/>, and ThermoRawFileParser <https://github.com/compomics/ThermoRawFileParser>.

This package provides a general test for conditional independence in supervised learning algorithms as proposed by Watson & Wright (2021) <doi:10.1007/s10994-021-06030-6>. Implements a conditional variable importance measure which can be applied to any supervised learning algorithm and loss function. Provides statistical inference procedures without parametric assumptions and applies equally well to continuous and categorical predictors and outcomes.

This package performs biomedical named entity recognition, Unified Medical Language System (UMLS) concept mapping, and negation detection using the Python spaCy', scispaCy', and medspaCy packages, and transforms extracted data into a wide format for inclusion in machine learning models. The development of the scispaCy package is described by Neumann (2019) <doi:10.18653/v1/W19-5034>. The medspacy package uses ConText', an algorithm for determining the context of clinical statements described by Harkema (2009) <doi:10.1016/j.jbi.2009.05.002>. Clinspacy also supports entity embeddings from scispaCy and UMLS cui2vec concept embeddings developed by Beam (2018) <arXiv:1804.01486>.

This package provides tools for estimating censored Almost Ideal (AI) and Quadratic Almost Ideal (QUAI) demand systems using Maximum Likelihood Estimation (MLE). It includes functions for calculating demand share equations and the truncated log-likelihood function for a system of equations, incorporating demographic variables. The package is designed to handle censored data, where some observations may be zero due to non-purchase of certain goods. Package also contains a procedure to approximate demand elasticities numerically and estimate standard errors via Delta Method. It is particularly useful for applied researchers analyzing household consumption data.

This package provides a one-stop shop for intuitive and dependency-free color and palette creation and modification. Includes palettes and functionality from popular packages such as viridis', RColorBrewer', and base R grDevices', as well as ggplot2 plot bindings. Users can generate perceptually uniform and colorblind-friendly palettes, adjust palettes in HSL and RGB color spaces, map color gradients to value ranges, and create color-generating functions.

This is a pedagogical package, designed to help students understanding convergence of random variables. It provides a way to investigate interactively various modes of convergence (in probability, almost surely, in law and in mean) of a sequence of i.i.d. random variables. Visualisation of simulated sample paths is possible through interactive plots. The approach is illustrated by examples and exercises through the function investigate', as described in Lafaye de Micheaux and Liquet (2009) <doi:10.1198/tas.2009.0032>. The user can study his/her own sequences of random variables.