Measures morphological diversity from discrete character data and estimates evolutionary tempo on phylogenetic trees. Imports morphological data from #NEXUS (Maddison et al. (1997) <doi:10.1093/sysbio/46.4.590>) format with read_nexus_matrix(), and writes to both #NEXUS and TNT format (Goloboff et al. (2008) <doi:10.1111/j.1096-0031.2008.00217.x>). Main functions are test_rates(), which implements AIC and likelihood ratio tests for discrete character rates introduced across Lloyd et al. (2012) <doi:10.1111/j.1558-5646.2011.01460.x>, Brusatte et al. (2014) <doi:10.1016/j.cub.2014.08.034>, Close et al. (2015) <doi:10.1016/j.cub.2015.06.047>, and Lloyd (2016) <doi:10.1111/bij.12746>, and calculate_morphological_distances(), which implements multiple discrete character distance metrics from Gower (1971) <doi:10.2307/2528823>, Wills (1998) <doi:10.1006/bijl.1998.0255>, Lloyd (2016) <doi:10.1111/bij.12746>, and Hopkins and St John (2018) <doi:10.1098/rspb.2018.1784>. This also includes the GED correction from Lehmann et al. (2019) <doi:10.1111/pala.12430>. Multiple functions implement morphospace plots: plot_chronophylomorphospace() implements Sakamoto and Ruta (2012) <doi:10.1371/journal.pone.0039752>, plot_morphospace() implements Wills et al. (1994) <doi:10.1017/S009483730001263X>, plot_changes_on_tree() implements Wang and Lloyd (2016) <doi:10.1098/rspb.2016.0214>, and plot_morphospace_stack() implements Foote (1993) <doi:10.1017/S0094837300015864>. Other functions include safe_taxonomic_reduction(), which implements Wilkinson (1995) <doi:10.1093/sysbio/44.4.501>, map_dollo_changes() implements the Dollo stochastic character mapping of Tarver et al. (2018) <doi:10.1093/gbe/evy096>, and estimate_ancestral_states() implements the ancestral state options of Lloyd (2018) <doi:10.1111/pala.12380>. calculate_tree_length() and reconstruct_ancestral_states() implements the generalised algorithms from Swofford and Maddison (1992; no doi).

Frequentist confidence analysis answers the question: How confident are we in a particular treatment effect? This package calculates the frequentist confidence in a treatment effect of interest given observed data, and returns the family of confidence curves associated with that data.

This package provides functionality for performing cohort incidence analyses against data stored in the OMOP Common Data Model (CDM). The package generates database-specific SQL, executes analyses across supported database platforms, and returns standardized incidence estimates using configurable time-at-risk, stratification, and cohort definitions. It is intended to support reproducible observational research within the OHDSI analytics framework.

Calculating the fractal dimension of a coastline using the boxes and dividers methods.

The data and meta data from Statistics Netherlands (<https://www.cbs.nl>) can be browsed and downloaded. The client uses the open data API of Statistics Netherlands.

This package contains the Multi-Species Acute Toxicity Database (CAS & SMILES columns only) [United States (US) Department of Health and Human Services (DHHS) National Institutes of Health (NIH) National Cancer Institute (NCI), "Multi-Species Acute Toxicity Database", <https://cactus.nci.nih.gov/download/acute-toxicity-db/>] combined with the Toxic Substances Control Act (TSCA) Inventory [United States Environmental Protection Agency (US EPA), "Toxic Substances Control Act (TSCA) Chemical Substance Inventory", <https://www.epa.gov/tsca-inventory/how-access-tsca-inventory

Defines the classes used for "class comparison" problems in the OOMPA project (<http://oompa.r-forge.r-project.org/>). Class comparison includes tests for differential expression; see Simon's book for details on typical problem types.

This package provides the tools to produce catseye plots, principally by catseyesplot() function which calls R's standard plot() function internally, or alternatively by the catseyes() function to overlay the catseye plot onto an existing R plot window. Catseye plots illustrate the normal distribution of the mean (picture a normal bell curve reflected over its base and rotated 90 degrees), with a shaded confidence interval; they are an intuitive way of illustrating and comparing normally distributed estimates, and are arguably a superior alternative to standard confidence intervals, since they show the full distribution rather than fixed quantile bounds. The catseyesplot and catseyes functions require pre-calculated means and standard errors (or standard deviations), provided as numeric vectors; this allows the flexibility of obtaining this information from a variety of sources, such as direct calculation or prediction from a model. Catseye plots, as illustrations of the normal distribution of the means, are described in Cumming (2013 & 2014). Cumming, G. (2013). The new statistics: Why and how. Psychological Science, 27, 7-29. <doi:10.1177/0956797613504966> pmid:24220629.

Enables DBI compliant packages to integrate with the RStudio connections pane, and the pins package. It automates the display of schemata, tables, views, as well as the preview of the table's top 1000 records.

Model soil gas fluxes with the Flux-Gradient Method. It includes functions for data handling, a forward and an inverse model for flux modeling and methods for calibration and uncertainty estimation. For more details see Gartiser et al. (2025a) <doi:10.21105/joss.08094> and Gartiser et al. (2025b) <doi:10.1111/ejss.70126>.

Computes effect sizes, standard errors, and confidence intervals for total, direct, and indirect effects in continuous-time mediation models as described in Pesigan, Russell, and Chow (2025) <doi:10.1037/met0000779>.

Allows you to conduct robust correlations on your non-normal data set. The robust correlations included in the package are median-absolute-deviation and median-based correlations. Li, J.C.H. (2022) <doi:10.5964/meth.8467>.

This package performs the cross-match test that is an exact, distribution free test of equality of 2 high dimensional multivariate distributions. The input is a distance matrix and the labels of the two groups to be compared, the output is the number of cross-matches and a p-value. See Rosenbaum (2005) <doi:10.1111/j.1467-9868.2005.00513.x>.

This package provides a tool that imports, subsets, and exports the CongressData dataset. CongressData contains approximately 800 variables concerning all US congressional districts with data back to 1789. The dataset tracks district characteristics, members of Congress, and the political behavior of those members. Users with only a basic understanding of R can subset this data across multiple dimensions, export their search results, identify the citations associated with their searches, and more.

Analysis of network community objects with applications to neuroimaging data. There are two main components to this package. The first is the hierarchical multimodal spinglass (HMS) algorithm, which is a novel community detection algorithm specifically tailored to the unique issues within brain connectivity. The other is a suite of semiparametric kernel machine methods that allow for statistical inference to be performed to test for potential associations between these community structures and an outcome of interest (binary or continuous).

The main function calculates confidence intervals (CI) for Mixed Models, utilizing both classical estimators from the lmer() function in the lme4 package and robust estimators from the rlmer() function in the robustlmm package, as well as the varComprob() function in the robustvarComp package. Three methods are available: the classical Wald method, the wild bootstrap, and the parametric bootstrap. Bootstrap methods offer flexibility in obtaining lower and upper bounds through percentile or BCa methods. More details are given in Mason, F., Cantoni, E., & Ghisletta, P. (2021) <doi:10.5964/meth.6607> and Mason, F., Cantoni, E., & Ghisletta, P. (2024) <doi:10.1037/met0000643>.

This package contains the adaptation of bubblebath from MATLAB (Danz, 2020: <https://www.mathworks.com/matlabcentral/fileexchange/70348>) and the tools to transform a dataframe of radii and points to plot-able paths.

This package implements Sequential Doubly Robust (SDR) and infinite-dimensional Targeted Maximum Likelihood (iTMLE) estimators for longitudinal modified treatment policies in settings with transitioning states, such as ICU, ward, or emergency department care episodes. Treatment is permitted in active states and becomes structurally inapplicable after a state transition (e.g. discharge or death). Supports asymmetric g- and Q-model regularisation, k-fold cross-fitting, and pluggable SuperLearner ensembles. Includes specialised SuperLearner wrappers (SL.tgt.* and SL.tmle_* families) for the iTMLE targeting step, which pass the logit offset as a covariate column to preserve correct subsetting during SuperLearner cross-validation. Methods based on Diaz et al. (2021) <doi:10.1080/01621459.2021.1955691> and Luedtke et al. (2017) <doi:10.48550/arXiv.1705.02459>.

Modeling associations between covariates and power spectra of replicated time series using a cepstral-based semiparametric framework. Implements a fast two-stage estimation procedure via Whittle likelihood and multivariate regression.The methodology is based on Li and Dong (2025) <doi:10.1080/10618600.2025.2473936>.

Conducts conditional random sampling on observed values in sparse matrices. Useful for training and test set splitting sparse matrices prior to model fitting in cross-validation procedures and estimating the predictive accuracy of data imputation methods, such as matrix factorization or singular value decomposition (SVD). Although designed for applications with sparse matrices, CRASSMAT can also be applied to complete matrices, as well as to those containing missing values.

This package implements higher order likelihood-based inference for logistic and loglinear models.

Implementations of recent complex-valued wavelet shrinkage procedures for smoothing irregularly sampled signals, see Hamilton et al (2018) <doi:10.1080/00401706.2017.1281846>.

Simple interpolation methods designed to be used from C code. Supports constant, linear and spline interpolation. An R wrapper is included but this package is primarily designed to be used from C code using LinkingTo'. The spline calculations are classical cubic interpolation, e.g., Forsythe, Malcolm and Moler (1977) <ISBN: 9780131653320>.

Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods; see "Contrast trees and distribution boosting", Jerome H. Friedman (2020) <doi:10.1073/pnas.1921562117>. In situations where inaccuracies are detected, boosted contrast trees can often improve performance. Functions are provided to to build such trees in addition to a special case, distribution boosting, an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.