Enables the calibration and analysis of radiocarbon dates, often but not exclusively for the purposes of archaeological research. It includes functions not only for basic calibration, uncalibration, and plotting of one or more dates, but also a statistical framework for building demographic and related longitudinal inferences from aggregate radiocarbon date lists, including: Monte-Carlo simulation test (Timpson et al 2014 <doi:10.1016/j.jas.2014.08.011>), random mark permutation test (Crema et al 2016 <doi:10.1371/journal.pone.0154809>) and spatial permutation tests (Crema, Bevan, and Shennan 2017 <doi:10.1016/j.jas.2017.09.007>).

The Nearest Neighbor Descent method for finding approximate nearest neighbors by Dong and co-workers (2010) <doi:10.1145/1963405.1963487>. Based on the Python package PyNNDescent <https://github.com/lmcinnes/pynndescent>.

Implementation of the methods described in the paper with the above title: Langsrud, Ã . (2019) <doi:10.1007/s11222-018-9848-9>. The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included.

Search, composite, and download Google Earth Engine imagery with reticulate bindings for the Python module geedim by Dugal Harris. Read the geedim documentation here: <https://geedim.readthedocs.io/>. Wrapper functions are provided to make it more convenient to use geedim to download images larger than the Google Earth Engine size limit <https://developers.google.com/earth-engine/apidocs/ee-image-getdownloadurl>. By default the "High Volume" API endpoint <https://developers.google.com/earth-engine/cloud/highvolume> is used to download data and this URL can be customized during initialization of the package.

For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species.

This package provides a quantile regression method for multivariate data to find linear combinations of explanatory and response variables generalizing canonical correlation. The package consists of functions, rqcan() for fitting the coefficients, and summary.rqcan(), which calls a bootstrap function. For details, see the help files for rqcan() and summary.rqcan(), and the reference: Portnoy (2022) <doi:10.1016/j.jmva.2022.105071>.

Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.

Includes algorithms to assess research productivity and patterns, such as the h-index and i-index. Cardoso et al. (2022) Cardoso, P., Fukushima, C.S. & Mammola, S. (2022) Quantifying the internationalization and representativeness in research. Trends in Ecology and Evolution, 37: 725-728.

An R interface to the typeform <https://www.typeform.com/> application program interface. Also provides functions for downloading your results.

This package implements classical cultural consensus analysis with formal, informal, and covariance agreement models, UCINET'-aligned minimum-residual factor extraction, competence estimation, and answer-key estimation. Based on the classical framework of Romney, Weller, and Batchelder (1986) <doi:10.1525/aa.1986.88.2.02a00020>, Romney, Batchelder, and Weller (1987) <doi:10.1177/000276487031002003>, and Weller (2007) <doi:10.1177/1525822X07303502>.

This package provides spatial data analysis functionalities including Exploratory Spatial Data Analysis, Spatial Cluster Detection and Clustering Analysis, Regionalization, etc. based on the C++ source code of GeoDa', which is an open-source software tool that serves as an introduction to spatial data analysis. The GeoDa software and its documentation are available at <https://geodacenter.github.io>.

This package provides portable access from R to biomedical image processing toolbox ANTs by Avants et al. (2009) <doi:10.54294/uvnhin> via seamless integration with the Python implementation ANTsPy'. Allows biomedical images to be processed in Python and analyzed in R', and vice versa via shared memory. See citation("rpyANTs") for more reference information.

This package implements simple Hamiltonian Monte Carlo routines in R for sampling from any desired target distribution which is continuous and smooth. See Neal (2017) <arXiv:1701.02434> for further details on Hamiltonian Monte Carlo. Automatic parameter selection is not supported.

This package provides methods for fast computation of running sample statistics for time series. These include: (1) mean, (2) standard deviation, and (3) variance over a fixed-length window of time-series, (4) correlation, (5) covariance, and (6) Euclidean distance (L2 norm) between short-time pattern and time-series. Implemented methods utilize Convolution Theorem to compute convolutions via Fast Fourier Transform (FFT).

Various tools for handling fuzzy measures, calculating Shapley value and interaction index, Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using lpsolve package, whose source in C adapted to the R environment is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package. Please refer to the following: [1] <https://personal-sites.deakin.edu.au/~gleb/fmtools.html> [2] G. Beliakov, H. Bustince, T. Calvo, A Practical Guide to Averaging', Springer, (2016, ISBN: 978-3-319-24753-3). [3] G. Beliakov, S. James, J-Z. Wu, Discrete Fuzzy Measures', Springer, (2020, ISBN: 978-3-030-15305-2).

Flexible rounding functions for use in error detection. They were outsourced from the scrutiny package.

Calculate the probability density functions (PDFs) for two threshold evidence accumulation models (EAMs). These are defined using the following Stochastic Differential Equation (SDE), dx(t) = v(x(t),t)*dt+D(x(t),t)*dW, where x(t) is the accumulated evidence at time t, v(x(t),t) is the drift rate, D(x(t),t) is the noise scale, and W is the standard Wiener process. The boundary conditions of this process are the upper and lower decision thresholds, represented by b_u(t) and b_l(t), respectively. Upper threshold b_u(t) > 0, while lower threshold b_l(t) < 0. The initial condition of this process x(0) = z where b_l(t) < z < b_u(t). We represent this as the relative start point w = z/(b_u(0)-b_l(0)), defined as a ratio of the initial threshold location. This package generates the PDF using the same approach as the python package it is based upon, PyBEAM by Murrow and Holmes (2023) <doi:10.3758/s13428-023-02162-w>. First, it converts the SDE model into the forwards Fokker-Planck equation dp(x,t)/dt = d(v(x,t)*p(x,t))/dt-0.5*d^2(D(x,t)^2*p(x,t))/dx^2, then solves this equation using the Crank-Nicolson method to determine p(x,t). Finally, it calculates the flux at the decision thresholds, f_i(t) = 0.5*d(D(x,t)^2*p(x,t))/dx evaluated at x = b_i(t), where i is the relevant decision threshold, either upper (i = u) or lower (i = l). The flux at each thresholds f_i(t) is the PDF for each threshold, specifically its PDF. We discuss further details of this approach in this package and PyBEAM publications. Additionally, one can calculate the cumulative distribution functions of and sampling from the EAMs.

Authors working with LaTeX articles use the built-in bibliography options and BibTeX files. While this might work with LaTeX', it does not function well with Web articles. As a way out, rebib offers tools to convert and combine bibliographies from both sources.

The handling of an API key (misnomer for password) for protected data can be difficult. This package provides secure convenience functions for entering / handling API keys and pulling data directly into memory. By default it will load from REDCap instances, but other sources are injectable via inversion of control.

An implementation of calls designed to collect Tumblr data via its Application Program Interfaces (API), which can be found at the following URL: <https://www.tumblr.com/docs/en/api/v2>.

Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.

This package provides 3D plotting routines that facilitate the use of the rgl package and extend its functionality. For example, the routines allow the user to directly control the camera position & orientation, as well as to generate 3D movies with a moving observer.

Fits standard and random effects latent class models. The single level random effects model is described in Qu et al <doi:10.2307/2533043> and the two level random effects model in Beath and Heller <doi:10.1177/1471082X0800900302>. Examples are given for their use in diagnostic testing.

Generates both total- and level-specific R-squared measures from Rights and Sterbaâ s (2019) <doi:10.1037/met0000184> framework of R-squared measures for multilevel models with random intercepts and/or slopes, which is based on a complete decomposition of variance. Additionally generates graphical representations of these R-squared measures to allow visualizing and interpreting all measures in the framework together as an integrated set. This framework subsumes 10 previously-developed R-squared measures for multilevel models as special cases of 5 measures from the framework, and it also includes several newly-developed measures. Measures in the framework can be used to compute R-squared differences when comparing multilevel models (following procedures in Rights & Sterba (2020) <doi:10.1080/00273171.2019.1660605>). Bootstrapped confidence intervals can also be calculated. To use the confidence interval functionality, download bootmlm from <https://github.com/marklhc/bootmlm>.