This package provides install functions of other languages such as java', python'.

Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.

Download data from the Ada and Archibald MacLeish Field Station in Whately, MA. The Ada and Archibald MacLeish Field Station is a 260-acre patchwork of forest and farmland located in West Whately, MA that provides opportunities for faculty and students to pursue environmental research, outdoor education, and low-impact recreation (see <https://www.smith.edu/discover-smith/smith-action/sustainable-smith/macleish-field-station> for more information). This package contains weather data over several years, and spatial data on various man-made and natural structures.

This package provides functions for carrying out nonparametric hypothesis tests of the MCAR hypothesis based on the theory of Frechet classes and compatibility. Also gives functions for computing halfspace representations of the marginal polytope and related geometric objects.

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).

This package provides functions to perform all steps of genome-wide association meta-analysis for studying Genotype x Environment interactions, from collecting the data to the manhattan plot. The procedure accounts for the potential correlation between studies. In addition to the Fixed and Random models, one can investigate the relationship between QTL effects and some qualitative or quantitative covariate via the test of contrast and the meta-regression, respectively. The methodology is available from: (De Walsche, A., et al. (2025) \doi10.1371/journal.pgen.1011553).

Create dummy variables from categorical data. This package can convert categorical data (factor and ordered) into dummy variables and handle multiple columns simultaneously. This package enables to select whether a dummy variable for base group is included (for principal component analysis/factor analysis) or excluded (for regression analysis) by an option. makedummies function accepts data.frame', matrix', and tbl (tibble) class (by tibble package). matrix class data is automatically converted to data.frame class.

This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.

Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.

Implementation of the sampling and aggregation method for the covariate shift maximin effect, which was proposed in <arXiv:2011.07568>. It constructs the confidence interval for any linear combination of the high-dimensional maximin effect.

This package provides tools for phase-type distributions including the following variants: continuous, discrete, multivariate, in-homogeneous, right-censored, and regression. Methods for functional evaluation, simulation and estimation using the expectation-maximization (EM) algorithm are provided for all models. The methods of this package are based on the following references. Asmussen, S., Nerman, O., & Olsson, M. (1996). Fitting phase-type distributions via the EM algorithm, Olsson, M. (1996). Estimation of phase-type distributions from censored data, Albrecher, H., & Bladt, M. (2019) <doi:10.1017/jpr.2019.60>, Albrecher, H., Bladt, M., & Yslas, J. (2022) <doi:10.1111/sjos.12505>, Albrecher, H., Bladt, M., Bladt, M., & Yslas, J. (2022) <doi:10.1016/j.insmatheco.2022.08.001>, Bladt, M., & Yslas, J. (2022) <doi:10.1080/03461238.2022.2097019>, Bladt, M. (2022) <doi:10.1017/asb.2021.40>, Bladt, M. (2023) <doi:10.1080/10920277.2023.2167833>, Albrecher, H., Bladt, M., & Mueller, A. (2023) <doi:10.1515/demo-2022-0153>, Bladt, M. & Yslas, J. (2023) <doi:10.1016/j.insmatheco.2023.02.008>.

Give access to MUI X Tree View components, which lets users navigate hierarchical lists of data with nested levels that can be expanded and collapsed.

This package provides functions to perform sensitivity analysis on a model with multivariate output.

This package provides tools for calculating I-Scores, a simple way to measure how successful minor political parties are at influencing the major parties in their environment. I-Scores are designed to be a more comprehensive measurement of minor party success than vote share and legislative seats won, the current standard measurements, which do not reflect the strategies that most minor parties employ. The procedure leverages the Manifesto Project's NLP model to identify the issue areas that sentences discuss, see Burst et al. (2024) <doi:10.25522/manifesto.manifestoberta.56topics.context.2024.1.1>, and the Wordfish algorithm to estimate the relative positions that platforms take on those issue areas, see Slapin and Proksch (2008) <doi:10.1111/j.1540-5907.2008.00338.x>.

This package provides functions of marginal mean and quantile regression models are used to analyze environmental exposure and biomonitoring data with repeated measurements and non-detects (i.e., values below the limit of detection (LOD)), as well as longitudinal exposure data that include non-detects and time-dependent covariates. For more details see Chen IC, Bertke SJ, Curwin BD (2021) <doi:10.1038/s41370-021-00345-1>, Chen IC, Bertke SJ, Estill CF (2024) <doi:10.1038/s41370-024-00640-7>, Chen IC, Bertke SJ, Dahm MM (2024) <doi:10.1093/annweh/wxae068>, and Chen IC (2025) <doi:10.1038/s41370-025-00752-8>.

Discrete event simulation using both R and C++ (Karlsson et al 2016; <doi:10.1109/eScience.2016.7870915>). The C++ code is adapted from the SSIM library <https://www.inf.usi.ch/carzaniga/ssim/>, allowing for event-oriented simulation. The code includes a SummaryReport class for reporting events and costs by age and other covariates. The C++ code is available as a static library for linking to other packages. A priority queue implementation is given in C++ together with an S3 closure and a reference class implementation. Finally, some tools are provided for cost-effectiveness analysis.

This package performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.

Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation as documented by Broatch and Karl (2018) <doi:10.48550/arXiv.1710.05284>, Karl, Yang, and Lohr (2014) <doi:10.1016/j.csda.2013.11.019>, and by Karl (2012) <doi:10.1515/1559-0410.1471>. Karl and Zimmerman <doi:10.1016/j.jspi.2020.06.004> use this package to illustrate how the home field effect estimator from a mixed model can be biased under nonrandom scheduling.

This package provides an interface to the Mapbox GL JS (<https://docs.mapbox.com/mapbox-gl-js/guides>) and the MapLibre GL JS (<https://maplibre.org/maplibre-gl-js/docs/>) interactive mapping libraries to help users create custom interactive maps in R. Users can create interactive globe visualizations; layer sf objects to create filled maps, circle maps, heatmaps', and three-dimensional graphics; and customize map styles and views. The package also includes utilities to use Mapbox and MapLibre maps in Shiny web applications.

This package provides a framework for analyzing broth microdilution assays in various 96-well plate designs, visualizing results and providing descriptive and (simple) inferential statistics (i.e. summary statistics and sign test). The functions are designed to add metadata to 8 x 12 tables of absorption values, creating a tidy data frame. Users can choose between clean-up procedures via function parameters (which covers most cases) or user prompts (in cases with complex experimental designs). Users can also choose between two validation methods, i.e. exclusion of absorbance values above a certain threshold or manual exclusion of samples. A function for visual inspection of samples with their absorption values over time for certain group combinations helps with the decision. In addition, the package includes functions to subtract the background absorption (usually at time T0) and to calculate the growth performance compared to a baseline. Samples can be visually inspected with their absorption values displayed across time points for specific group combinations. Core functions of this package (i.e. background subtraction, sample validation and statistics) were inspired by the manual calculations that were applied in Tewes and Muller (2020) <doi:10.1038/s41598-020-67600-7>.

This package provides a collection of function to solve multiple criteria optimization problems using genetic algorithms (NSGA-II). Also included is a collection of test functions.

Collect and normalize local microinverter energy and power production data through off-cloud API requests. Currently supports APSystems', Enphase', and Fronius microinverters.

This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include: (i) Normalization and/or variance stabilization of the data, (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow), (iii) Generation of diverse diagnostic plots, (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.

Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in "Statistical Inference via Data Science: A ModernDive into R and the Tidyverse" available at <https://moderndive.com/>.