Pedigree-based Relatedness Coefficients (ribd)
Recursive algorithms for computing various relatedness coefficients, including pairwise kinship, kappa and identity coefficients. Both autosomal and X-linked coefficients are computed. Founders are allowed to be inbred. In addition to the standard pairwise coefficients, ribd also computes a range of lesser-known coefficients, including generalised kinship coefficients (Karigl (1981) <doi:10.1111/j.1469-1809.1981.tb00341.x>; Weeks and Lange (1988) <h…/PMC1715269> ), two-locus coefficients (Thompson (1988) <doi:10.1093/imammb/5.4.261>) and multi-person coefficients. This package is part of the ped suite, a collection of packages for pedigree analysis with ‘pedtools’ as the core package for creating and handling pedigree objects.

Project Specific Workspace Organization Templates (cabinets)
Creates project specific directory and file templates that are written to a .Rprofile file. Upon starting a new R session, these templates can be used to streamline the creation of new directories that are standardized to the user’s preferences.

Probability Computations on Pedigrees (pedprobr)
An implementation of the Elston-Stewart algorithm for calculating pedigree likelihoods given genetic marker data (Elston and Stewart (1971) <doi:10.1159/000152448>). The standard algorithm is extended to allow inbred founders. Mutation modelling is supported by the ‘pedmut’ package. ‘pedprobr’ is part of the ped suite, a collection of packages for pedigree analysis in R, based on ‘pedtools’ for handling pedigrees and markers.

Computerized Adaptive Multistage Testing (Rmst)
Assemble the panels of computerized adaptive multistage testing by the bottom-up and the top-down approach, and simulate the administration of the assembled panels. The full documentation and tutorials are at <https://…/Rmst>. Reference: Luo and Kim (2018) <doi:10.1111/jedm.12174>.

Flexible Bayes Factor Testing of Scientific Expectations (BFpack)
Implementation of various default Bayes factors for testing statistical hypotheses. The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., regression coefficients), variances (e.g., group variances), and measures of association (e.g,. bivariate correlations). The statistical underpinnings are described in Mulder, Hoijtink, and Xin (2019) <arXiv:1904.00679>, Mulder and Gelissen (2019) <arXiv:1807.05819>, Mulder (2016) <DOI:10.1016/j.jmp.2014.09.004>, Mulder and Fox (2019) <DOI:10.1214/18-BA1115>, Mulder and Fox (2013) <DOI:10.1007/s11222-011-9295-3>, Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder <DOI:10.1037/met0000116>, Hoijtink, Mulder, van Lissa, and Gu, (2018) <DOI:10.31234/osf.io/v3shc>, Gu, Mulder, and Hoijtink, (2018) <DOI:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder, (2018) <DOI:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel, (2018) <DOI:10.1037/met0000187>.

Integrates with the ‘RStudio’ Connections Pane and ‘pins’ (connections)
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.

Automatic Distribution Graphs Using Formulas (fplot)
Easy way to plot common types of graphics with moderators by using formulas. The core of the package concerns distribution plots which are automatic: the many options are tailored to the data at hand to offer the nicest and most meaningful graphs possible — with no/minimum user input.

Multiple Grubbs-Beck Low-Outlier Test (MGBT)
Compute the multiple Grubbs-Beck low-outlier test on positively distributed data and utilities for noninterpretive U.S. Geological Survey annual peak-streamflow data processing discussed in Cohn et al. (2013) <doi:10.1002/wrcr.20392> and England et al. (2017) <doi:10.3133/tm4B5>.

Cluster Optimized Proximity Scaling (cops)
Cluster optimized proximity scaling (COPS) refers to multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration. They achieve this by transforming proximities/distances with power functions and augment the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly for given parameters (COPS-C) for ratio, interval and non-metric MDS (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal parameters (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different MDS models in a COPS framework like ratio, interval and non-metric MDS for COPS-C and P-COPS with Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459), scaling by majorizing a complex function (SMACOF; de Leeuw, 1977, <https://…/4ps3b5mj> ), Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>), elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>), s-stress (Takane, Young & de Leeuw, 1977, <doi:10.1007/BF02293745>, r-stress (de Leeuw, Groenen & Mair, 2016, <https://…/142619> ), power-stress (Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>) and power elastic scaling, power Sammon mapping and approximated power stress (Rusch, Mair & Hornik, 2015, <https://…/> ). All of these models can also solely be fit as MDS with power transformations. The package further contains a function for pattern search optimization, the ‘Adaptive Luus-Jakola Algorithm’ (Rusch, Mair & Hornik, 2015, <https://…/> ).

Spatial Bayesian Factor Analysis (spBFA)
Implements a spatial Bayesian non-parametric factor analysis model with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). Spatial correlation is introduced in the columns of the factor loadings matrix using a Bayesian non-parametric prior, the probit stick-breaking process. Areal spatial data is modeled using a conditional autoregressive (CAR) prior and point-referenced spatial data is treated using a Gaussian process. The response variable can be modeled as Gaussian, probit, Tobit, or Binomial (using Polya-Gamma augmentation). Temporal correlation is introduced for the latent factors through a hierarchical structure and can be specified as exponential or first-order autoregressive.

Random Forest Prediction Decomposition and Feature Importance Measure (tree.interpreter)
An R re-implementation of the ‘treeinterpreter’ package on PyPI <https://…/>. Each prediction can be decomposed as ‘prediction = bias + feature_1_contribution + … + feature_n_contribution’. This decomposition is then used to calculate the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using out-of-bag samples (MDI-oob) feature importance measures based on the work of Li et al. (2019) <arXiv:1906.10845>.

Scalable Robust Estimators with High Breakdown Point for Incomplete Data (rrcovNA)
Robust Location and Scatter Estimation and Robust Multivariate Analysis with High Breakdown Point for Incomplete Data (missing values) (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).

Calculation for Single Arm Group Sequential Test with Binary Endpoint (BinGSD)
Consider an at-most-K-stage group sequential design with only an upper bound for the last analysis and non-binding lower bounds.With binary endpoint, two kinds of test can be applied, asymptotic test based on normal distribution and exact test based on binomial distribution. This package supports the computation of boundaries and conditional power for single-arm group sequential test with binary endpoint, via either asymptotic or exact test. The package also provides functions to obtain boundary crossing probabilities given the design.

Analysis and Prediction of Tides (TideCurves)
Tidal analysis of evenly spaced observed time series (time step 1 to 60 min) with or without shorter gaps using the harmonic representation of inequalities. The analysis should preferably cover an observation period of at least 19 years. For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion. The main objective of this package is to synthesize or predict a tidal time series.

Table One for ‘Latex’, ‘Word’, and ‘Html’ ‘R Markdown’ Documents (tibbleOne)
Table one is a tabular description of characteristics, e.g., demographics of patients in a clinical trial, presented overall and also stratified by a categorical variable, e.g. treatment group. There are many excellent packages available to create table one. This package focuses on providing table one objects that seamlessly fit into ‘R Markdown’ analyses.

An Accurate kNN Implementation with Multiple Distance Measures (knn.covertree)
Similarly to the ‘FNN’ package, this package allows calculation of the k nearest neighbors (kNN) of a data matrix. The implementation is based on cover trees introduced by Alina Beygelzimer, Sham Kakade, and John Langford (2006) <doi:10.1145/1143844.1143857>.

Implementation of the Empirical Bayes Method (ebreg)
Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) <doi:10.3150/15-BEJ797>. More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) <arXiv:1903.00961>.

Project Environments (renv)
A dependency management toolkit for R. Using ‘renv’, you can create and manage project-local R libraries, save the state of these libraries to a ‘lockfile’, and later restore your library as required. Together, these tools can help make your projects more isolated, portable, and reproducible.

Performance Measures for ‘mlr3’ (mlr3measures)
Implements multiple performance measures for supervised learning. Includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are.

Multiple Summary Statistics for Binned Stats/Geometries (ggplot.multistats)
Provides the ggplot binning layer stat_summaries_hex(), which functions similar to its singular form, but allows the use of multiple statistics per bin. Those statistics can be mapped to multiple bin aesthetics.

Fast and Cached Import of Data from ‘Quandl’ Using the ‘json API’ (GetQuandlData)
Imports time series data from the ‘Quandl’ database <https://www.quandl.com>. The package uses the ‘json api’ at <https://…/api>, local caching (‘memoise’ package) and the tidy format by default. Also allows queries of databases, allowing the user to see which time series are available for each database id. In short, it is an alternative to package ‘Quandl’, with faster data importation in the tidy/long format.

P-Model (rpmodel)
Implements the P-model (Stocker et al., 2019 <doi:10.5194/gmd-2019-200>), predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis, assimilation, and dark respiration rates as a function of the environment (temperature, CO2, vapour pressure deficit, light, atmospheric pressure).

Easily Process a Batch of Cox Models (ezcox)
A tool to operate a batch of univariate or multivariate Cox models and return tidy result.

Common Forms for Complex Hierarchical and Relational Data Structures (silicate)
Generate common data forms for complex data suitable for conversions and transmission by decomposition as paths or primitives. Paths are sequentially-linked records, primitives are basic atomic elements and both can model many forms and be grouped into hierarchical structures. The universal models ‘SC0’ (structural) and ‘SC’ (labelled, relational) are composed of edges and can represent any hierarchical form. Specialist models ‘PATH’, ‘ARC’ and ‘TRI’ provide the most common intermediate forms used for converting from one form to another. The methods are inspired by the simplicial complex <https://…/Simplicial_complex> and provide intermediate forms that relate spatial data structures to this mathematical construct.

Computation of Bayes Factors for Common Biomedical Designs (baymedr)
BAYesian inference for MEDical designs in R. Convenience functions for the computation of Bayes factors for common biomedical research designs. Implemented are functions to test the equivalence (equiv_bf), non-inferiority (infer_bf), and superiority (super_bf) of an experimental group compared to a control group. Bayes factors for these three tests can be computed based on raw data (x, y) or summary statistics (n_x, n_y, mean_x, mean_y, sd_x, sd_y [or ci_margin and ci_level]), making it possible to reanalyse findings (e.g., from publications) without the need to obtain the raw data.

Create Dependency Wheels and Sankey Diagrams (sankeywheel)
By binding R functions and the ‘Highcharts’ <http://…/> charting library, ‘sankeywheel’ package provides a simple way to draw dependency wheels and sankey diagrams.

Matrix Bending (mbend)
Bending non-positive-definite (symmetric) matrices to positive-definite, using weighted and unweighted methods. Jorjani, H., et al. (2003) <doi:10.3168/jds.S0022-0302(03)73646-7>. Schaeffer, L. R. (2010) <http://…/PDforce.pdf>.

Robust Regression with Compositional Covariates (robregcc)
We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.

Generate PDF Conference Posters Using R Markdown (posterdown)
Use ‘rmarkdown’ and ‘pagedown’ to generate HTML and PDF conference posters.

Spherically Constrained Optimization Routine (SCOR)
A non-convex optimization package that optimizes any function under the criterion, combination of variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) <arXiv:1909.04024> .

Zero Inflated Poisson Factor Analysis (ZIPFA)
Estimation methods for zero-inflated Poisson factor analysis (ZIPFA) on sparse data. It provides estimates of coefficients in a new type of zero-inflated regression. It provides a cross-validation method to determine the potential rank of the data in the ZIPFA and conducts zero-inflated Poisson factor analysis based on the determined rank.

A Post Hoc Analysis for Pearson’s Chi-Squared Test for Count Data (chisq.posthoc.test)
Perform post hoc analysis based on residuals of Pearson’s Chi-squared Test for Count Data based on T. Mark Beasley & Randall E. Schumacker (1995) <doi: 10.1080/00220973.1995.9943797>.

Partial Separability and Functional Gaussian Graphical Models (fgm)
Estimates a functional graphical model and a partially separable Karhunen-Loève decomposition for a multivariate Gaussian process. See Zapata J., Oh S. and Petersen A. (2019) <arXiv:1910.03134>.

Create and Format Correlation Matrices (corx)
Create correlation (or partial correlation) matrices. Correlation matrices are formatted with significance stars based on user preferences. Matrices of coefficients, p-values, and number of pairwise observations are returned. Send resultant formatted matrices to the clipboard to be pasted into excel and other programs. A plot method allows users to visualize correlation matrices created with ‘corx’.

Downscale of RS Images using Deep Learning (downscaledl)
Downscaling the coarse-resolution remote sensing images into fined-resolution images is important for preprocessing. This package provides the functions for this with high accuracy. Currently, the resautonet is used as the deep learning algorithm for downscaling. This work was done based on this paper, Lianfa Li (2019) <doi:10.3390/rs11111378>.

Tidyverse-Style Interface for Geocoding (tidygeocoder)
An intuitive tidyverse-style interface for geocoding. Obtains latitude and longitude coordinates in tibble format from addresses. The currently supported services are the US Census geocoder and Nominatim (OSM).

Measurement Nonequivalence Effect Size Calculator (dmacs)
Computes measurement nonequivalence effect size indices described in Nye and Drasgow (2011) <doi:10.1037/a0022955>.

Multilevel Hidden Markov Models Using Bayesian Estimation (mHMMbayes)
An implementation of the multilevel (also known as mixed or random effects) hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The multilevel HMM is tailored to accommodate (intense) longitudinal data of multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The model can be fitted on multivariate data with a categorical distribution, and include individual level covariates (allowing for e.g., group comparisons on model parameters). Parameters are estimated using Bayesian estimation utilizing the forward-backward recursion within a hybrid Metropolis within Gibbs sampler. The package also includes various visualization options, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm.

Create Custom ‘Bootstrap’ Themes to Use in ‘Shiny’ (fresh)
Customize ‘Bootstrap’ and ‘Bootswatch’ themes, like colors, fonts, grid layout, to use in ‘Shiny’ applications, ‘rmarkdown’ documents and ‘flexdashboard’.

Sparse Principal Component Based on Least Trimmed Squares (ltsspca)
Implementation of robust and sparse PCA algorithm of Wang and Van Aelst (2019) <DOI:10.1080/00401706.2019.1671234>.

Interactive Document for Working with Confusion Matrix (CMShiny)
An interactive document on the topic of confusion matrix analysis using ‘rmarkdown’ and ‘shiny’ packages. Runtime examples are provided in the package function as well as at <https://…/>.

Assertion and Message Functions (bigassertr)
Enhanced message functions (cat() / message() / warning() / error()) using wrappers around sprintf(). Also, multiple assertion functions (e.g. to check class, length, values, files, arguments, etc.).

GIFT Questions Format Generator from Dataframes (GIFTr)
A framework and functions to create ‘MOODLE’ quizzes. ‘GIFTr’ takes dataframe of questions of four types: multiple choices, numerical, true or false and short answer questions, and exports a text file formatted in ‘MOODLE’ GIFT format. You can prepare a spreadsheet in any software and import it into R to generate any number of questions with ‘HTML’, ‘markdown’ and ‘LaTeX’ support.

Connect to ‘AWS Athena’ using R ‘AWS SDK’ ‘paws’ (‘DBI’ Interface) (noctua)
Designed to be compatible with the ‘R’ package ‘DBI’ (Database Interface) when connecting to Amazon Web Service (‘AWS’) Athena <https://…/>. To do this the ‘R’ ‘AWS’ Software Development Kit (‘SDK’) ‘paws’ <https://…/paws> is used as a driver.

Hierarchical Bayesian Benefit-Risk Assessment Using Discrete Choice Experiment (hbbr)
Implements assessment of benefit-risk balance using Bayesian Discrete Choice Experiment. For more details see the article by Mukhopadhyay et al. (2019) <DOI:10.1080/19466315.2018.1527248>.

Anonymization of Spatial Point Patterns and Raster Objects (tangles)
Spatial data anonymization preserves confidentiality. Using methods described in Zandbergen (2014) <doi:10.1155/2014/567049>, spatial data anonymization is achieved by dithering original spatial coordinates with combinations of randomized vertical, horizontal and rotational shifts. This can apply to non-grid spatial point patterns and raster objects, and the methods preserve the same spatial characteristics and relationships of the original data. Unique hash keying enables data subjected to anonymization sequences to be re-identified where required.

Stratification and Matching for Large Observational Data Sets (stratamatch)
A pilot matching design to automatically stratify and match large datasets. The manual_stratify() function allows users to manually stratify a dataset based on categorical variables of interest, while the auto_stratify() function does automatically by allocating a held-aside (pilot) data set, fitting a prognostic score (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based on prognostic score quantiles. The strata_match() function then does optimal matching of the data set in parallel within strata.

Time Series Forecasting with Machine Learning Methods (forecastML)
The purpose of ‘forecastML’ is to simplify the process of multi-step-ahead direct forecasting with standard machine learning algorithms. ‘forecastML’ supports lagged, dynamic, static, and grouping features for modeling single and grouped time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo’s (2018) paper ‘A note on the validity of cross-validation for evaluating autoregressive time series prediction’ <doi:10.1016/j.csda.2017.11.003>.

Model Wrappers for Discriminant Analysis (discrim)
Bindings for additional classification models for use with the ‘parsnip’ package. Models include flavors of discriminant analysis, such as linear (Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>), regularized (Friedman (1989) <doi:10.1080/01621459.1989.10478752>), and flexible (Hastie, Tibshirani, and Buja (1994) <doi:10.1080/01621459.1994.10476866>), as well as naive Bayes classifiers (Hand and Yu (2007) <doi:10.1111/j.1751-5823.2001.tb00465.x>).

Restricted Bridge Estimation (rbridge)
Bridge Regression estimation with linear restrictions defined in Yuzbasi et al. (2019) <arXiv:910.03660>. Special cases of this approach fit the restricted LASSO, restricted RIDGE and restricted Elastic Net estimators.