An (aspirational) collection of additional geometries and statistics for ggplot2'.

This package provides a Bayesian statistical model for estimating child (under-five age group) and adult (15-60 age group) mortality. The main challenge is how to combine and integrate these different time series and how to produce unified estimates of mortality rates during a specified time span. GPR is a Bayesian statistical model for estimating child and adult mortality rates which its data likelihood is mortality rates from different data sources such as: Death Registration System, Censuses or surveys. There are also various hyper-parameters for completeness of DRS, mean, covariance functions and variances as priors. This function produces estimations and uncertainty (95% or any desirable percentiles) based on sampling and non-sampling errors due to variation in data sources. The GP model utilizes Bayesian inference to update predicted mortality rates as a posterior in Bayes rule by combining data and a prior probability distribution over parameters in mean, covariance function, and the regression model. This package uses Markov Chain Monte Carlo (MCMC) to sample from posterior probability distribution by rstan package in R. Details are given in Wang H, Dwyer-Lindgren L, Lofgren KT, et al. (2012) <doi:10.1016/S0140-6736(12)61719-X>, Wang H, Liddell CA, Coates MM, et al. (2014) <doi:10.1016/S0140-6736(14)60497-9> and Mohammadi, Parsaeian, Mehdipour et al. (2017) <doi:10.1016/S2214-109X(17)30105-5>.

This package provides functions for performing graphical difference testing. Differences are generated between raster images. Comparisons can be performed between different package versions and between different R versions.

Offers various swiss maps as data frames and ggplot2 objects and gives the possibility to add layers of data on the maps. Data are publicly available from the swiss federal statistical office. In addition to the \codemaps2 object (a list of 8 swiss maps, at various levels), there are the data frames with the boundaries used to produce these maps (\codeshp_df, a list with 8 data frames).

Execute Latent Class Analysis (LCA) and Latent Class Regression (LCR) by using Generalized Structured Component Analysis (GSCA). This is explained in Ryoo, Park, and Kim (2019) <doi:10.1007/s41237-019-00084-6>. It estimates the parameters of latent class prevalence and item response probability in LCA with a single line comment. It also provides graphs of item response probabilities. In addition, the package enables to estimate the relationship between the prevalence and covariates.

Estimation of generalized linear models with correlated/clustered observations by use of generalized estimating equations (GEE). See e.g. Halekoh and Højsgaard, (2005, <doi:10.18637/jss.v015.i02>), for details. Several types of clustering are supported, including exchangeable variance structures, AR1 structures, M-dependent, user-specified variance structures and more. The model fitting computations are performed using modified code from the geeM package, while the interface and output objects have been written to resemble the geepack package. The package also contains additional tools for working with and inspecting results from the geepack package, e.g. a confint method for geeglm objects from geepack'.

This package creates tables suitable for regulatory agency submission by leveraging the gtsummary package as the back end. Tables can be exported to HTML, Word, PDF and more. Highly customized outputs are available by utilizing existing styling functions from gtsummary as well as custom options designed for regulatory tables.

This package provides a collection of commonly used visualizations of temporal and spatio-temporal health data including case counts, incidence rates, and covariates. The available plot types include time series, heatmaps, seasonality plots, maps and more. The package supports standard data transformations such as temporal and spatial aggregations, while offering extensive customization options for the resulting figures.

Make 2D and 3D plots of linear programming (LP), integer linear programming (ILP), or mixed integer linear programming (MILP) models with up to three objectives. Plots of both the solution and criterion space are possible. For instance the non-dominated (Pareto) set for bi-objective LP/ILP/MILP programming models (see vignettes for an overview). The package also contains an function for checking if a point is inside the convex hull.

Simple package to download Google Sheets using just the sharing link. Spreadsheets can be downloaded as a data frame, or as plain text to parse manually. Google Sheets is the new name for Google Docs Spreadsheets <https://www.google.com/sheets/about>.

Fits high dimensional penalized generalized linear mixed models using the Monte Carlo Expectation Conditional Minimization (MCECM) algorithm. The purpose of the package is to perform variable selection on both the fixed and random effects simultaneously for generalized linear mixed models. The package supports fitting of Binomial, Gaussian, and Poisson data with canonical links, and supports penalization using the MCP, SCAD, or LASSO penalties. The MCECM algorithm is described in Rashid et al. (2020) <doi:10.1080/01621459.2019.1671197>. The techniques used in the minimization portion of the procedure (the M-step) are derived from the procedures of the ncvreg package (Breheny and Huang (2011) <doi:10.1214/10-AOAS388>) and grpreg package (Breheny and Huang (2015) <doi:10.1007/s11222-013-9424-2>), with appropriate modifications to account for the estimation and penalization of the random effects. The ncvreg and grpreg packages also describe the MCP, SCAD, and LASSO penalties.

Basic infrastructure and several algorithms for 1d-4d bin packing problem. This package provides a set of c-level classes and solvers for 1d-4d bin packing problem, and an r-level solver for 4d bin packing problem, which is a wrapper over the c-level 4d bin packing problem solver. The 4d bin packing problem solver aims to solve bin packing problem, a.k.a container loading problem, with an additional constraint on weight. Given a set of rectangular-shaped items, and a set of rectangular-shaped bins with weight limit, the solver looks for an orthogonal packing solution such that minimizes the number of bins and maximize volume utilization. Each rectangular-shaped item i = 1, .. , n is characterized by length l_i, depth d_i, height h_i, and weight w_i, and each rectangular-shaped bin j = 1, .. , m is specified similarly by length l_j, depth d_j, height h_j, and weight limit w_j. The item can be rotated into any orthogonal direction, and no further restrictions implied.

Estimation and display of various types of population attributable fraction and impact fractions. As well as the usual calculations of attributable fractions and impact fractions, functions are provided for attributable fraction nomograms and fan plots, continuous exposures, for pathway specific population attributable fractions, and for joint, average and sequential population attributable fractions.

Procedures for calculating variance components, study variation, percent study variation, and percent tolerance for gauge repeatability and reproducibility study. Methods included are ANOVA and Average / Range methods. Requires balanced study.

Conducts hierarchical partitioning to calculate individual contributions of each predictor towards adjusted R2 and explained deviance for generalized additive models based on output of gam() and bam() in mgcv package, applying the algorithm in this paper: Lai(2024) <doi:10.1016/j.pld.2024.06.002>.

Make smoothed stacked area charts in ggplot2'. Stream plots are useful to show magnitude trends over time.

Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

The basic idea of this package is provides some tools to help the researcher to work with geostatistics. Initially, we present a collection of functions that allow the researchers to deal with spatial data using bootstrap procedure. There are five methods available and two ways to display them: bootstrap confidence interval - provides a two-sided bootstrap confidence interval; bootstrap plot - a graphic with the original variogram and each of the B bootstrap variograms.

Detailed functionality for working with the univariate and multivariate Generalized Hyperbolic distribution and its special cases (Hyperbolic (hyp), Normal Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t and Gaussian distribution). Especially, it contains fitting procedures, an AIC-based model selection routine, and functions for the computation of density, quantile, probability, random variates, expected shortfall and some portfolio optimization and plotting routines as well as the likelihood ratio test. In addition, it contains the Generalized Inverse Gaussian distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. Quantitative risk management: Concepts, techniques and tools. Princeton University Press, Princeton (2005).

Function gmcmtx0() computes a more reliable (general) correlation matrix. Since causal paths from data are important for all sciences, the package provides many sophisticated functions. causeSummBlk() and causeSum2Blk() give easy-to-interpret causal paths. Let Z denote control variables and compare two flipped kernel regressions: X=f(Y, Z)+e1 and Y=g(X, Z)+e2. Our criterion Cr1 says that if |e1*Y|>|e2*X| then variation in X is more "exogenous or independent" than in Y, and the causal path is X to Y. Criterion Cr2 requires |e2|<|e1|. These inequalities between many absolute values are quantified by four orders of stochastic dominance. Our third criterion Cr3, for the causal path X to Y, requires new generalized partial correlations to satisfy |r*(x|y,z)|< |r*(y|x,z)|. The function parcorVec() reports generalized partials between the first variable and all others. The package provides several R functions including get0outliers() for outlier detection, bigfp() for numerical integration by the trapezoidal rule, stochdom2() for stochastic dominance, pillar3D() for 3D charts, canonRho() for generalized canonical correlations, depMeas() measures nonlinear dependence, and causeSummary(mtx) reports summary of causal paths among matrix columns. Portfolio selection: decileVote(), momentVote(), dif4mtx(), exactSdMtx() can rank several stocks. Functions whose names begin with boot provide bootstrap statistical inference, including a new bootGcRsq() test for "Granger-causality" allowing nonlinear relations. A new tool for evaluation of out-of-sample portfolio performance is outOFsamp(). Panel data implementation is now included. See eight vignettes of the package for theory, examples, and usage tips. See Vinod (2019) \doi10.1080/03610918.2015.1122048.

Implementation of global envelopes for a set of general d-dimensional vectors T in various applications. A 100(1-alpha)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to alpha. Global means that the probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be used for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and MrkviÄ ka (2024) <doi:10.18637/jss.v111.i03>, Myllymäki et al. (2017) <doi:10.1111/rssb.12172>, MrkviÄ ka and Myllymäki (2023) <doi:10.1007/s11222-023-10275-7>, MrkviÄ ka et al. (2016) <doi:10.1016/j.spasta.2016.04.005>, MrkviÄ ka et al. (2017) <doi:10.1007/s11222-016-9683-9>, MrkviÄ ka et al. (2020) <doi:10.14736/kyb-2020-3-0432>, MrkviÄ ka et al. (2021) <doi:10.1007/s11009-019-09756-y>, Myllymäki et al. (2021) <doi:10.1016/j.spasta.2020.100436>, MrkviÄ ka et al. (2022) <doi:10.1002/sim.9236>, Dai et al. (2022) <doi:10.5772/intechopen.100124>, DvoŠák and MrkviÄ ka (2022) <doi:10.1007/s00180-021-01134-y>, MrkviÄ ka et al. (2023) <doi:10.48550/arXiv.2309.04746>, and Konstantinou et al. (2024) <doi: 10.1007/s00180-024-01569-z>.

Set of functions to create datasets using a correlation matrix.

Data-driven approach for arriving at person-specific time series models from within a Graphical Vector Autoregression (VAR) framework. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. All estimates are obtained uniquely for each individual in the final models. The method for the graphicalVAR approach is found in Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

This package provides a compilation of nonlinear growth models.