Latin Hypercube Designs (LHDs) Algorithms (LHD)
Contains functions for finding space-filling Latin Hypercube Designs (LHDs), e.g. maximin distance LHDs. Unlike other packages, our package is particularly useful in the area of Design and Analysis of Experiments (DAE). More specifically, it is very useful in design of computer experiments. One advantage of our package is its comprehensiveness. It contains a variety of heuristic algorithms (and their modifications) for searching maximin distance LHDs. In addition to that, it also contains other useful tools for developing and constructing maximin distance LHDs. In the future, algebraic construction methods will be added. Please refer to the function documentations for the detailed references of each function. Among all the references we used, one reference should be highlighted here, which is Ruichen Jin, Wei Chen, Agus Sudjianto (2005) <doi:10.1016/j.jspi.2004.02.014>. They provided a new form of phi_p criterion, which does not lose the space-filling property and simultaneously reduces the computational complexity when evaluating (or re-evaluating) an LHD. Their new phi_p criterion is a fundamental component of our many functions. Besides, the computation nature of the new phi_p criterion enables our functions to have less CPU time.

Generalized Gauss Markov Regression (ggmr)
Implements the generalized Gauss Markov regression, this is useful when both predictor and response have uncertainty attached to them and also when covariance within the predictor, within the response and between the predictor and the response is present. Base on the results published in guide ISO/TS 28037 (2010) <https://…/44473.html>.

Tools for Matrix Algebra, Optimization and Inference (maotai)
Matrix is an universal and sometimes primary object/unit in applied mathematics and statistics. We provide a number of algorithms for selected problems in optimization and statistical inference. For general exposition to the topic with focus on statistical context, see the book by Banerjee and Roy (2014, ISBN:9781420095388).

Profiling Compliers and Non-Compliers for Instrumental Variable Analysis (ivdesc)
Estimating the mean and variance of a covariate for the complier, never-taker and always-taker subpopulation in the context of instrumental variable estimation. This package implements the method described in Marbach and Hangartner (2019) <doi:10.2139/ssrn.3380247>.

Kumaraswamy Complementary Weibull Geometric (Kw-CWG) Probability Distribution (elfDistr)
Density, distribution function, quantile function and random generation for the Kumaraswamy Complementary Weibull Geometric (Kw-CWG) lifetime probability distribution proposed in Afify, A.Z. et al (2017) <doi:10.1214/16-BJPS322>.

Non-Homogeneous Markov and Hidden Markov Multistate Models (nhm)
Fits non-homogeneous Markov multistate models and misclassification-type hidden Markov models in continuous time to intermittently observed data. Implements the methods in Titman (2011) <doi:10.1111/j.1541-0420.2010.01550.x>. Uses direct numerical solution of the Kolmogorov forward equations to calculate the transition probabilities.

Hypothesis Matrix Translation (hypr)
Translation between experimental null hypotheses, hypothesis matrices, and contrast matrices as used in linear regression models. The package is based on the method described in Schad, Vasishth, Hohenstein, and Kliegl (2019) <arXiv:1807.10451>.

Misclassified Interval Censored Time-to-Event Data (miscIC)
Estimation of the survivor function for interval censored time-to-event data subject to misclassification using nonparametric maximum likelihood estimation, implementing the methods of Titman (2017) <doi:10.1007/s11222-016-9705-7>. Misclassification probabilities can either be specified as fixed or estimated. Models with time dependent misclassification may also be fitted.

Generate Raster Data Lazily from ‘GDAL’ (lazyraster)
Read raster data at a specified resolution on-demand via ‘GDAL’ (the Geospatial Data Abstraction Library <https://gdal.org/> ). Augments the ‘raster’ package by never reading data from a raster source until necessary for generating an in-memory ‘raster’ object. A ‘lazyraster’ object may be cropped and converted to ‘raster’ object, and by default will only read a small amount of data sufficient for an overall summary. The amount of data read can be controlled by specifying the output dimensions.

Index Numbers in Social Sciences (IndexNumber)
We provide an R tool to determine index numbers. It is a measure of the evolution of a fixed magnitude for only a product of for several products. It is very useful in Social Sciences. Among others, we obtain simple index numbers (in chain or in serie), index numbers for not only a product or weighted index numbers as the Laspeyres index (Laspeyres, 1864), the Paasche index (Paasche, 1874) or the Fisher index (Lapedes, 1978).

Predictive Models Homologator (traineR)
Methods to unify the different ways of creating predictive models and their different predictive formats. It includes methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning, Support Vector Machines and Bayesian Methods.

Autograph R for ‘Tensorflow’ (tfautograph)
Translate R control flow expressions into ‘Tensorflow’ graphs.

Unsupervised Gaussian Mixture and Minkowski K-Means (GMKMcharlie)
High performance trainers for parameterizing and clustering weighted data. The Gaussian mixture (GM) module includes the conventional EM (expectation maximization) trainer, the component-wise EM trainer, the minimum-message-length EM trainer by Figueiredo and Jain (2002) <doi:10.1109/34.990138>. These trainers accept additional constraints on mixture weights and covariance eigen ratios. The K-means (KM) module offers clustering with the options of (i) deterministic and stochastic K-means++ initializations, (ii) upper bounds on cluster weights (sizes), (iii) Minkowski distances, (iv) cosine dissimilarity, (v) dense and sparse representation of data input. The package improved the usual implementations of GM and KM training algorithms in various aspects. It is carefully crafted in multithreaded C++ for processing large data in industry use.

Representing and Inferring Single-Cell Trajectories (dynwrap)
Provides functionality to infer trajectories from single-cell data, represent them into a common format, and adapt them. Other biological information can also be added, such as cellular grouping, RNA velocity and annotation. Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.

Bring Local Sf to Spark (geospark)
R binds ‘GeoSpark’ <http://…/> extending ‘sparklyr’ <https://…/> R package to make distributed ‘geocomputing’ easier. Sf is a package that provides [simple features] <https://…/Simple_Features> access for R and which is a leading ‘geospatial’ data processing tool. ‘Geospark’ R package bring the same simple features access like sf but running on Spark distributed system.

Machine Learning and Judgement (mnj)
Perform FlexBoost in R. FlexBoost is a newly suggested algorithm based on AdaBoost by adjusting adaptive loss functions. Not only FlexBoost but also other machine learning algorithms (e.g. Support Vector Machines) will be added. For more details on FlexBoost see Jeon, Y. S., Yang, D. H., & Lim, D. J. (2019) <doi:10.1109/access.2019.2938356>.

Download and Manage Optional Package Data (pkgfilecache)
Manage optional data for your package. The data can be hosted anywhere, and you have to give a Uniform Resource Locator (URL) for each file. File integrity checks are supported. This is useful for package authors who need to ship more than the 5 Megabyte of data currently allowed by the the Comprehensive R Archive Network (CRAN).

Utilities for Fundamental Geo-Spatial Data (fgdr)
Read and Parse for Fundamental Geo-Spatial Data (FGD) which downloads XML file from providing site (<https://…/menu.php> ). The JPGIS format file provided by FGD so that it can be handled as an R spatial object such as ‘sf’ and ‘raster’ or ‘stars’. Supports the FGD version 4.1, and accepts fundamental items and digital elevation models.

Tree Structured Hidden Markov Model (treeHMM)
Used for Inference, Prediction and Parameter learning for tree structured Hidden Markov Model. The package propose a new architecture of Hidden Markov Model(HMM) known as Tree Structured HMM which could be used in various applications which involves graphs, trees etc.

Read and Write ‘Excel’ Sheets into and from List of Data Frames (xlsx2dfs)
Reading and writing sheets of a single ‘Excel’ file into and from a list of data frames. Eases I/O of tabular data in bioinformatics while keeping them in a human readable format.

Phase Plane Analysis of One- And Two-Dimensional Autonomous ODE Systems (phaseR)
Performs a qualitative analysis of one- and two-dimensional autonomous ordinary differential equation systems, using phase plane methods. Programs are available to identify and classify equilibrium points, plot the direction field, and plot trajectories for multiple initial conditions. In the one-dimensional case, a program is also available to plot the phase portrait. Whilst in the two-dimensional case, programs are additionally available to plot nullclines and stable/unstable manifolds of saddle points. Many example systems are provided for the user. For further details can be found in Grayling (2014) <doi:10.32614/RJ-2014-023>.

Distributions for Ecological Models in ‘nimble’ (nimbleEcology)
Common ecological distributions for ‘nimble’ models in the form of nimbleFunction objects. Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, and dynamic hidden Markov models. (Jolly (1965) <doi:10.2307/2333826>, Seber (1965) <10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).

Bayesian Estimation of Bivariate Volatility Model (BayesBEKK)
The Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) models are used for modelling the volatile multivariate data sets. In this package a variant of MGARCH called BEKK (Baba, Engle, Kraft, Kroner) proposed by Engle and Kroner (1995) <http://…/3532933> has been used to estimate the bivariate time series data using Bayesian technique.

Data Transformation or Simulation with Empirical Covariance Matrix (simTargetCov)
Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

Graphical and Numerical Checks for Mode-Finding Routines (optimCheck)
Tools for checking that the output of an optimization algorithm is indeed at a local mode of the objective function. This is accomplished graphically by calculating all one-dimensional ‘projection plots’ of the objective function, i.e., varying each input variable one at a time with all other elements of the potential solution being fixed. The numerical values in these plots can be readily extracted for the purpose of automated and systematic unit-testing of optimization routines.

Blyth-Still-Casella Exact Binomial Confidence Intervals (BlythStillCasellaCI)
Computes Blyth-Still-Casella exact binomial confidence intervals based on a refining procedure proposed by George Casella (1986) <doi:10.2307/3314658>.

Genome-Wide Structural Equation Modeling (gwsem)
Melds genome-wide association tests with structural equation modeling (SEM) using ‘OpenMx’. This package contains low-level C/C++ code to rapidly read genetic data encoded in U.K. Biobank or ‘plink’ formats. Prebuilt modeling options include one and two factor models. Alternately, analyses may utilize arbitrary, user-provided SEMs. See Verhulst, Maes, & Neale (2017) <doi:10.1007/s10519-017-9842-6> for details. An updated manuscript is in preparation.

Food Network Inference and Visualization (foodingraph)
Displays a weighted undirected food graph from an adjacency matrix. Can perform confidence-interval bootstrap inference with mutual information or maximal information coefficient. Based on my Master 1 internship at the Bordeaux Population Health center. References : Reshef et al. (2011) <doi:10.1126/science.1205438>, Meyer et al. (2008) <doi:10.1186/1471-2105-9-461>, Liu et al. (2016) <doi:10.1371/journal.pone.0158247>.

Varying Coefficients (varycoef)
Gives maximum likelihood estimation (MLE) method to estimate and predict spatially varying coefficient (SVC) Models. It supports covariance tapering by Furrer et al. (2006) <doi:10.1198/106186006X132178> to allow MLE on large data.

An Extension of the Taylor Diagram to Two-Dimensional Vector Data (SailoR)
A new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.

Matching Multiply Imputed Datasets (MatchThem)
Provides the necessary tools for the pre-processing technique of matching in multiply imputed datasets, to improve the robustness and transparency of deriving causal inferences from studying these datasets. This package includes functions to perform propensity score matching within or across the imputed datasets as well as to estimate weights (including inverse propensity score weights) of observations, to analyze each matched or weighted datasets using parametric or non-parametric statistical models, and to combine the obtained results from these models according to Rubin’s rules. Please see the package repository <https://…/MatchThem> for details.

Recursive Partitioning Based Multivariate Adaptive Regression Models, Classification Trees, Survival Trees (macs)
Implements recursive partitioning based, nonparametric methods for high dimensional regression and classification. Depending on the aims of data analysis as well as the structures of the data, macs provides three major functions: multivariate adaptive regression models, classification trees and survival trees. A list of references for this package is, Zhang, H. (1997) <doi:10.1080/10618600.1997.10474728>, Zhang, H. et al. (1999) <ISBN:978-1-4757-3027-2>, Zhang, H. et al. (2014) <doi:10.1002/gepi.21843>.

Logistic Regression Trees (glmtree)
A logistic regression tree is a decision tree with logistic regressions at its leaves. A particular stochastic expectation maximization algorithm is used to draw a few good trees, that are then assessed via the user’s criterion of choice among BIC / AIC / test set Gini. The formal development is given in a PhD chapter, see Ehrhardt (2019) <https://…/>.

Forecast Verification for Extreme Events (extremeIndex)
An index measuring the amount of information brought by forecasts for extreme events, subject to calibration, is computed. This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) <arXiv:1905.04022>.

A Collection of Useful Functions by John (usefun)
A set of general functions that I have used in various projects and in other R packages. They support some miscellaneous operations on data frames, matrices and vectors: adding a row on a ternary (3-value) data.frame based on positive and negative vector-indicators, rearranging a list of data.frames by rownames, pruning rows or columns of a data.frame that contain only one specific value given by the user, checking for matrix equality, pruning and reordering a vector according to the common elements between its names and elements of another given vector, finding the non-common elements between two vectors (outer-section), normalization of a vector, matrix or data.frame’s numeric values in a specified range, pretty printing of vector names and values in an R notebook (common names and values between two vectors also supported), retrieving the parent directory of any string path, checking whether a numeric value is inside a given interval, trim the decimal points of a given numeric value, quick saving of data to a file, making a multiple densities plot and a color bar plot and executing a plot string expression while generating the result to the specified file format.

Create an SVG-Based Mathematical Formula (eqn2svg)
If you have a mathematical formula and the need to have that formula in the form of scalable vector graphics (SVG), you’ll be delighted by what ‘eqn2svg’ will let you do. The incoming LaTeX math formula will be nicely converted to SVG tags. And you can use that code wherever SVGs are accepted.

Yang and Prentice Model with Piecewise Exponential Baseline Distribution (YPPE)
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution curves. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.

Non-Parametric Sampling with Parallel Monte Carlo (PosteriorBootstrap)
An implementation of a non-parametric statistical model using a parallelised Monte Carlo sampling scheme. The method implemented in this package allows non-parametric inference to be regularized for small sample sizes, while also being more accurate than approximations such as variational Bayes. The concentration parameter is an effective sample size parameter, determining the faith we have in the model versus the data. When the concentration is low, the samples are close to the exact Bayesian logistic regression method; when the concentration is high, the samples are close to the simplified variational Bayes logistic regression. The method is described in full in the paper Lyddon, Walker, and Holmes (2018), ‘Nonparametric learning from Bayesian models with randomized objective functions’ <arXiv:1806.11544>.

Preprocessing Operators and Pipelines for ‘mlr3’ (mlr3pipelines)
Dataflow programming toolkit that enriches ‘mlr3’ with a diverse set of pipelining operators (‘PipeOps’) that can be composed into graphs. Operations exist for data preprocessing, model fitting, and ensemble learning. Graphs can themselves be treated as ‘mlr3’ ‘Learners’ and can therefore be resampled, benchmarked, and tuned.

Single-Index Models with a Surface-Link (simsl)
An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2019) ‘A single-index model with a surface-link for optimizing individualized dose rules’ (pre-print) for detail of the method. The main function of this package is simsl().

Fit Interpretable Models and Explain Blackbox Machine Learning (interpret)
Machine Learning package for training interpretable models and explaining blackbox systems. Historically, the most intelligible models were not very accurate, and the most accurate models were not intelligible. Microsoft Research has developed an algorithm called the Explainable Boosting Machine (EBM) which has both high accuracy and intelligibility. EBM uses modern machine learning techniques like bagging and boosting to breathe new life into traditional GAMs (Generalized Additive Models). This makes them as accurate as random forests and gradient boosted trees, and also enhances their intelligibility and editability. Details on the EBM algorithm can be found in the paper by Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad (2015, <doi:10.1145/2783258.2788613>).

Bayesian Logistic Regression with Heavy-Tailed Priors (HTLR)
Efficient Bayesian multinomial logistic regression based on heavy-tailed (hyper-LASSO, non-convex) priors. The posterior of coefficients and hyper-parameters is sampled with restricted Gibbs sampling for leveraging the high-dimensionality and Hamiltonian Monte Carlo for handling the high-correlation among coefficients. A detailed description of the method: Li and Yao (2018), JSCS, 88:14, 2827-2851, <arXiv:1405.3319>.

Fitting RT-MPT Models (rtmpt)
Fit response-time extended multinomial processing tree (RT-MPT) models by Klauer and Kellen (2018) <doi:10.1016/j.jmp.2017.12.003>. The RT-MPT class not only incorporate frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it to estimate process completion times and encoding plus motor execution times next to the process probabilities of traditional MPTs. ‘rtmpt’ is a Bayesian framework and posterior samples are sampled using a Metropolis-Gibbs sampler like the one described in the Klauer and Kellen (2018), but with some modifications. Other than in the original C++ program we use the free and open source GNU Scientific Library (GSL). There is also the possibility to suppress single process completion times.

Modeling of Ordinal Random Variables via Softmax Regression (ohenery)
Supports the modeling of ordinal random variables, like the outcomes of races, via Softmax regression, under the Harville <doi:10.1080/01621459.1973.10482425> and Henery <doi:10.1111/j.2517-6161.1981.tb01153.x> models.

Color Manipulation Tools (prismatic)
Manipulate and visualize colors in a intuitive, low-dependency and functional way.

Generic implementation of the ‘RStudio’ connections contract (rscontract)
Provides a generic implementation of the ‘RStudio’ connection contract to make it easier for database connections, and other type of connections, opened via R packages integrate with the connections pane inside the ‘RStudio’ interactive development environment (IDE).

Post Double Selection with Double Machine Learning (postDoubleR)
Implements post double selection using double machine learning, see Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins (2017) <doi:10.1111/ectj.12097>, for various models, using several back ends. Allows the user flexibility in specifying their own methods for estimation.

Recursive Partitioning for Longitudinal Profiles Using Baseline Covariates (LongCART)
Constructs longitudinal tree (i.e., regression tree with heterogeneous longitudinal profile) for continuous longitudinal outcome using baseline covariates as partitioning variables according to the ‘LongCART’ algorithm as described in Kundu and Harezlak (2019) <doi:10.1080/24709360.2018.1557797>.

Robust Regression with Asymmetric Heavy-Tail Noise Distributions (rrat)
Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.

Estimate the Burden of Healthcare-Associated Infections (BHAI)
Provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.