Used to construct the URLs and parameters of Socrata Open Data API <https://dev.socrata.com> calls, using the API's SoQL parameter format. Has method-chained and sensical syntax. Plays well with pipes.

Takea Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA) for polytomous items. Package includes functions for generating a sequential relation table and a treegram to visualize the sequential relations between pairs of items.

Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the networkDynamic format. This work was supported by grant R01HD68395 from the National Institute of Health.

Fitting tree-structured varying coefficient models (Berger et al. (2019), <doi:10.1007/s11222-018-9804-8>). Simultaneous detection of covariates with varying coefficients and effect modifiers that induce varying coefficients if they are present.

The main purpose of this package is to propose a rigorous framework to fairly compare trip distribution laws and models as described in Lenormand et al. (2016) <doi:10.1016/j.jtrangeo.2015.12.008>.

Fit a two-step kernel ridge regression model for predicting edges in networks, and carry out cross-validation using shortcuts for swift and accurate performance assessment (Stock et al, 2018 <doi:10.1093/bib/bby095> ).

This package provides functions for the calibration of radiocarbon dates, as well as options to calculate different radiocarbon-related timescales (cal BP, cal BC/AD, C14 age, F14C, pMC, D14C) and estimating the effects of contamination or local reservoir offsets (Reimer and Reimer 2001 <doi:10.1017/S0033822200038339>). The methods follow long-established recommendations such as Stuiver and Polach (1977) <doi:10.1017/S0033822200003672> and Reimer et al. (2004) <doi:10.1017/S0033822200033154>. This package uses the calibration curves from the data package rintcal'.

An implementation of EDM algorithms based on research software developed for internal use at the Sugihara Lab ('UCSD/SIO'). The package is implemented with Rcpp wrappers around the cppEDM library. It implements the simplex projection method from Sugihara & May (1990) <doi:10.1038/344734a0>, the S-map algorithm from Sugihara (1994) <doi:10.1098/rsta.1994.0106>, convergent cross mapping described in Sugihara et al. (2012) <doi:10.1126/science.1227079>, and, multiview embedding described in Ye & Sugihara (2016) <doi:10.1126/science.aag0863>.

An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the rstan package.

This package provides implementations of a classifier based on the "Classification Based on Associations" (CBA). It can be used for building classification models from association rules. Rules are pruned in the order of precedence given by the sort criteria and a default rule is added. The final classifier labels provided instances. CBA was originally proposed by Liu, B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7).

This package helps with the analysis of array CGH data by detecting of the breakpoints in the genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified.

This package contains:

• facilities for working with grouped data: do something to data stratified by some variables.

• implementations of least-squares means, general linear contrasts, and

• miscellaneous other utilities.

This package contains linear and nonlinear regression methods based on partial least squares and penalization techniques. Model parameters are selected via cross-validation, and confidence intervals ans tests for the regression coefficients can be conducted via jackknifing.

The package provides different distances measurements to calculate the difference between genesets. Based on these scores the genesets are clustered and visualized as graph. This is all presented in an interactive Shiny application for easy usage.

Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination.

Retrieve and import data from the INKAR database (Indikatoren und Karten zur Raum- und Stadtentwicklung Datenbank, <https://www.inkar.de>) of the Federal Office for Building and Regional Planning (BBSR) in Bonn using their JSON API.

Routines for solving convex optimization problems with cone constraints by means of interior-point methods. The implemented algorithms are partially ported from CVXOPT, a Python module for convex optimization (see <https://cvxopt.org> for more information).

Implementing Function-on-Scalar Regression model in which the response function is dichotomized and observed sparsely. This package provides smooth estimations of functional regression coefficients and principal components for the dichotomized functional response regression (dfrr) model.

Data and miscellanea to support the book "Introduction to Data analysis with R for Forensic Scientists." This book was written by James Curran and published by CRC Press in 2010 (ISBN: 978-1-4200-8826-7).

Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time the picture was taken on, from JPEG images. Incorporates the easyexif <https://github.com/mayanklahiri/easyexif> library.

This package performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression. The algorithm estimates the regression parameters with lower biases and higher variances but mean-square errors (MSEs) are reduced.

Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.

This package provides a case conversion between common cases like CamelCase and snake_case. Using the rust crate heck <https://github.com/withoutboats/heck> as the backend for a highly performant case conversion for R'.

Utilities to work with data from the Internal Displacement Monitoring Centre (IDMC) (<https://www.internal-displacement.org/>), with convenient functions for loading events data from the IDMC API and transforming events data to daily displacement estimates.