This package provides a wrapper for Google's diff-match-patch library. It provides basic tools for computing diffs, finding fuzzy matches, and constructing / applying patches to strings.

Inference by sequential Monte Carlo for dynamic tree regression and classification models with hooks provided for sequential design and optimization, fully online learning with drift, variable selection, and sensitivity analysis of inputs. Illustrative examples from the original dynamic trees paper (Gramacy, Taddy & Polson (2011); <doi:10.1198/jasa.2011.ap09769>) are facilitated by demos in the package; see demo(package="dynaTree").

This package provides functions for discordant kinship modeling (and other sibling-based quasi-experimental designs). Contains data restructuring functions and functions for generating biometrically informed data for kin pairs. See [Garrison and Rodgers, 2016 <doi:10.1016/j.intell.2016.08.008>], [Sims, Trattner, and Garrison, 2024 <doi:10.3389/fpsyg.2024.1430978>] for empirical examples, and [Garrison and colleagues for theoretical work <doi:10.1101/2025.08.25.25334395>].

Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.

Geologic pattern data from <https://ngmdb.usgs.gov/fgdc_gds/geolsymstd.php>. Access functions are provided in the accompanying package deeptime'.

Calculate multiple biotic indices using diatoms from environmental samples. Diatom species are recognized by their species name using a heuristic search, and their ecological data is retrieved from multiple sources. It includes number/shape of chloroplasts diversity indices, size classes, ecological guilds, and multiple biotic indices. It outputs both a dataframe with all the results and plots of all the obtained data in a defined output folder. - Sample data was taken from Nicolosi Gelis, Cochero & Gómez (2020, <doi:10.1016/j.ecolind.2019.105951>). - The package uses the Diat.Barcode database to calculate morphological and ecological information by Rimet & Couchez (2012, <doi:10.1051/kmae/2012018>),and the combined classification of guilds and size classes established by B-Béres et al. (2017, <doi:10.1016/j.ecolind.2017.07.007>). - Current diatom-based biotic indices include the DES index by Descy (1979) - EPID index by Dell'Uomo (1996, ISBN: 3950009002) - IDAP index by Prygiel & Coste (1993, <doi:10.1007/BF00028033>) - ID-CH index by Hürlimann & Niederhauser (2007) - IDP index by Gómez & Licursi (2001, <doi:10.1023/A:1011415209445>) - ILM index by Leclercq & Maquet (1987) - IPS index by Coste (1982) - LOBO index by Lobo, Callegaro, & Bender (2002, ISBN:9788585869908) - SLA by SládeÄ ek (1986, <doi:10.1002/aheh.19860140519>) - TDI index by Kelly, & Whitton (1995, <doi:10.1007/BF00003802>) - SPEAR(herbicide) index by Wood, Mitrovic, Lim, Warne, Dunlop, & Kefford (2019, <doi:10.1016/j.ecolind.2018.12.035>) - PBIDW index by Castro-Roa & Pinilla-Agudelo (2014) - DISP index by Stenger-Kovács et al. (2018, <doi:10.1016/j.ecolind.2018.07.026>) - EDI index by Chamorro et al. (2024, <doi:10.1021/acsestwater.4c00126>) - DDI index by à lvarez-Blanco et al. (2013, <doi: 10.1007/s10661-012-2607-z>) - PDISE index by Kahlert et al. (2023, <doi:10.1007/s10661-023-11378-4>).

Easy access to species distribution data for 6 regions in the world, for a total of 226 anonymised species. These data are described and made available by Elith et al (2020) <doi:10.17161/bi.v15i2.13384> to compare species distribution modelling methods.

Different sample size calculations with different study designs. These techniques are explained by Chow (2007) <doi:10.1201/9781584889830>.

Nonparametric estimator of the cumulative incidences of competing risks under double truncation. The estimator generalizes the Efron-Petrosian NPMLE (Non-Parametric Maximun Likelihood Estimator) to the competing risks setting. Efron, B. and Petrosian, V. (1999) <doi:10.2307/2669997>.

This package contains functions to help with generating tables with descriptive statistics. In addition, the package can display results of statistical tests for group comparisons. A wide range of test procedures is supported, and user-defined test functions can be incorporated.

This package provides methods for simultaneous clustering and dimensionality reduction such as: Double k-means, Reduced k-means, Factorial k-means, Clustering with Disjoint PCA but also methods for exclusively dimensionality reduction: Disjoint PCA, Disjoint FA. The statistical methods implemented refer to the following articles: de Soete G., Carroll J. (1994) "K-means clustering in a low-dimensional Euclidean space" <doi:10.1007/978-3-642-51175-2_24> ; Vichi M. (2001) "Double k-means Clustering for Simultaneous Classification of Objects and Variables" <doi:10.1007/978-3-642-59471-7_6> ; Vichi M., Kiers H.A.L. (2001) "Factorial k-means analysis for two-way data" <doi:10.1016/S0167-9473(00)00064-5> ; Vichi M., Saporta G. (2009) "Clustering and disjoint principal component analysis" <doi:10.1016/j.csda.2008.05.028> ; Vichi M. (2017) "Disjoint factor analysis with cross-loadings" <doi:10.1007/s11634-016-0263-9>.

This package provides a library of density, distribution function, quantile function, (bounded) raw moments and random generation for a collection of distributions relevant for the firm size literature. Additionally, the package contains tools to fit these distributions using maximum likelihood and evaluate these distributions based on (i) log-likelihood ratio and (ii) deviations between the empirical and parametrically implied moments of the distributions. We add flexibility by allowing the considered distributions to be combined into piecewise composite or finite mixture distributions, as well as to be used when truncated. See Dewitte (2020) <https://hdl.handle.net/1854/LU-8644700> for a description and application of methods available in this package.

This package creates survey designs for distance sampling surveys. These designs can be assessed for various effort and coverage statistics. Once the user is satisfied with the design characteristics they can generate a set of transects to use in their distance sampling survey. Many of the designs implemented in this R package were first made available in our Distance for Windows software and are detailed in Chapter 7 of Advanced Distance Sampling, Buckland et. al. (2008, ISBN-13: 978-0199225873). Find out more about estimating animal/plant abundance with distance sampling at <https://distancesampling.org/>.

Apache licensed alternative to Highcharter which provides functions for both fast and beautiful interactive visualization for Markdown and Shiny'.

This package provides a Natural Language Processing Model trained to detect directness and intensity during conflict. See <https://www.mikeyeomans.info>.

This package provides functions to download and treat data regarding the Brazilian Amazon region from a variety of official sources.

This package provides a tool to sample data with the desired properties.Samples can be drawn by purposive sampling with determining distributional conditions, such as deviation from normality (skewness and kurtosis), and sample size in quantitative research studies. For purposive sampling, a researcher has something in mind and participants that fit the purpose of the study are included (Etikan,Musa, & Alkassim, 2015) <doi:10.11648/j.ajtas.20160501.11>.Purposive sampling can be useful for answering many research questions (Klar & Leeper, 2019) <doi:10.1002/9781119083771.ch21>.

Analysis, visualisation and simulation of digital polymerase chain reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). Supports data formats of commercial systems (Bio-Rad QX100 and QX200; Fluidigm BioMark) and other systems.

Compute estimates and confidence intervals of weighted averages quickly and easily. Weighted averages are computed using data.table for speed. Confidence intervals are approximated using the delta method with either using known formulae or via algorithmic or numerical integration.

This package provides a d-statistic tests the null hypothesis of no treatment effect in a matched, nonrandomized study of the effects caused by treatments. A d-statistic focuses on subsets of matched pairs that demonstrate insensitivity to unmeasured bias in such an observational study, correcting for double-use of the data by conditional inference. This conditional inference can, in favorable circumstances, substantially increase the power of a sensitivity analysis (Rosenbaum (2010) <doi:10.1007/978-1-4419-1213-8_14>). There are two examples, one concerning unemployment from Lalive et al. (2006) <doi:10.1111/j.1467-937X.2006.00406.x>, the other concerning smoking and periodontal disease from Rosenbaum (2017) <doi:10.1214/17-STS621>.

Discriminant Adaptive Nearest Neighbor Classification is a variation of k nearest neighbors where the shape of the neighborhood is data driven. This package implements dann and sub_dann from Hastie (1996) <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.

This package provides a graphical user interface (GUI) to the functions implemented in the R package DQAstats'. Publication: Mang et al. (2021) <doi:10.1186/s12911-022-01961-z>.

Time series analysis of network connectivity. Detects and visualizes change points between networks. Methods included in the package are discussed in depth in Baek, C., Gates, K. M., Leinwand, B., Pipiras, V. (2021) "Two sample tests for high-dimensional auto-covariances" <doi:10.1016/j.csda.2020.107067> and Baek, C., Gampe, M., Leinwand B., Lindquist K., Hopfinger J. and Gates K. (2023) â Detecting functional connectivity changes in fMRI dataâ <doi:10.1007/s11336-023-09908-7>.

Clustered or multilevel data structures are common in the assessment of differential item functioning (DIF), particularly in the context of large-scale assessment programs. This package allows users to implement extensions of the Mantel-Haenszel DIF detection procedures in the presence of multilevel data based on the work of Begg (1999) <doi:10.1111/j.0006-341X.1999.00302.x>, Begg & Paykin (2001) <doi:10.1080/00949650108812115>, and French & Finch (2013) <doi:10.1177/0013164412472341>.