Build multiscalar territorial analysis based on various contexts.

This package provides one function, which is a wrapper around purrr::map() with some extras on top, including parallel computation, progress bars, error handling, and result caching.

Implementation of two tools to merge Hardware Event Monitors (HEMs) from different subexperiments. Hardware Reading and Merging (HRM), which uses order statistics to merge; and MUlti-Correlation HEM (MUCH) which merges using a multivariate normal distribution. The reference paper for HRM is: S. Vilardell, I. Serra, R. Santalla, E. Mezzetti, J. Abella and F. J. Cazorla, "HRM: Merging Hardware Event Monitors for Improved Timing Analysis of Complex MPSoCs," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, no. 11, pp. 3662-3673, Nov. 2020, <doi:10.1109/TCAD.2020.3013051>. For MUCH: S. Vilardell, I. Serra, E. Mezzetti, J. Abella, and F. J. Cazorla. 2021. "MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs". In Proceedings of the 36th Annual ACM Symposium on Applied Computing (SAC 21). Association for Computing Machinery. <doi:10.1145/3412841.3441931>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).

This package provides a lightweight package designed to facilitate statistical simulations through functional programming. It centralizes the simulation process into a single higher-order function, enhancing manageability and usability. The package includes ready-to-use functions for common simulation targets.

Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates (Van Lissa, 2020). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.

Covariate measurement error correction is implemented by means of regression calibration by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331), efficient regression calibration by Spiegelman D, Carroll RJ & Kipnis V (2001) <doi:10.1002/1097-0258(20010115)20:1%3C139::AID-SIM644%3E3.0.CO;2-K> and maximum likelihood estimation by Bartlett JW, Stavola DBL & Frost C (2009) <doi:10.1002/sim.3713>. Outcome measurement error correction is implemented by means of the method of moments by Buonaccorsi JP (2010, ISBN:1420066560) and efficient method of moments by Keogh RH, Carroll RJ, Tooze JA, Kirkpatrick SI & Freedman LS (2014) <doi:10.1002/sim.7011>. Standard error estimation of the corrected estimators is implemented by means of the Delta method by Rosner B, Spiegelman D & Willett WC (1990) <doi:10.1093/oxfordjournals.aje.a115715> and Rosner B, Spiegelman D & Willett WC (1992) <doi:10.1093/oxfordjournals.aje.a116453>, the Fieller method described by Buonaccorsi JP (2010, ISBN:1420066560), and the Bootstrap by Carroll RJ, Ruppert D, Stefanski LA & Crainiceanu CM (2006, ISBN:1584886331).

When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. <DOI:10.1111/biom.13024>. Besides, flare is needed only you apply the SQRT or LAD method ('mcb totally has 8 methods). Although flare has been archived by CRAN, you can still get it in <https://CRAN.R-project.org/package=flare> and the latest version is useful for mcb'.

This package provides a four step change point detection method that can detect break points with the presence of missing values proposed by Liu and Safikhani (2023) <https://drive.google.com/file/d/1a8sV3RJ8VofLWikTDTQ7W4XJ76cEj4Fg/view?usp=drive_link>.

Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them. This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. Reference: Knoblauch & Maloney (2012) "Modeling Psychophysical Data in R". <doi:10.1007/978-1-4614-4475-6>.

Regression methods to quantify the relation between two measurement methods are provided by this package. In particular it addresses regression problems with errors in both variables and without repeated measurements. It implements the Clinical Laboratory Standard International (CLSI) recommendations (see J. A. Budd et al. (2018, <https://clsi.org/standards/products/method-evaluation/documents/ep09/>) for analytical method comparison and bias estimation using patient samples. Furthermore, algorithms for Theil-Sen and equivariant Passing-Bablok estimators are implemented, see F. Dufey (2020, <doi:10.1515/ijb-2019-0157>) and J. Raymaekers and F. Dufey (2022, <arXiv:2202:08060>). Further the robust M-Deming and MM-Deming (experimental) are available, see G. Pioda (2021, <arXiv:2105:04628>). A comprehensive overview over the implemented methods and references can be found in the manual pages mcrPioda-package and mcreg'.

Bayesian estimation of inverse variance weighted (IVW), Burgess et al. (2013) <doi:10.1002/gepi.21758>, and MR-Egger, Bowden et al. (2015) <doi:10.1093/ije/dyv080>, summary data models for Mendelian randomization analyses.

An implementation of the Monte Carlo techniques described in details by Dufour (2006) <doi:10.1016/j.jeconom.2005.06.007> and Dufour and Khalaf (2007) <doi:10.1002/9780470996249.ch24>. The two main features available are the Monte Carlo method with tie-breaker, mc(), for discrete statistics, and the Maximized Monte Carlo, mmc(), for statistics with nuisance parameters.

Application of a test to rule out that trends detected in hydrological time series are explained exclusively by the randomness of the climate. Based on: Ricchetti, (2018) <https://repositorio.uchile.cl/handle/2250/168487>.

This package provides functions to predict one multi-way array (i.e., a tensor) from another multi-way array, using a low-rank CANDECOMP/PARAFAC (CP) factorization and a ridge (L_2) penalty [Lock, EF (2018) <doi:10.1080/10618600.2017.1401544>]. Also includes functions to sample from the Bayesian posterior of a tensor-on-tensor model.

This package provides a variety of functions that make it easy to plot standard choropleth maps as well as choropleth alternatives in ggplot2'.

High-performance implementation of the Modified Hodrick-Prescott (HP) Filter for decomposing macroeconomic time series into trend and cyclical components. Based on the methodology of Choudhary, Hanif and Iqbal (2014) <doi:10.1080/00036846.2014.894631> "On smoothing macroeconomic time series using the modified HP filter", which uses generalized cross-validation (GCV) to automatically select the optimal smoothing parameter lambda, following McDermott (1997) "An automatic method for choosing the smoothing parameter in the HP filter" (as described in Coe and McDermott (1997) <doi:10.2307/3867497>). Unlike the standard HP filter that uses fixed lambda values (1600 for quarterly, 100 for annual data), this package estimates series-specific lambda values that minimize the GCV criterion. Implements efficient C++ routines via RcppArmadillo for fast computation, supports batch processing of multiple series, and provides comprehensive visualization tools using ggplot2'. Particularly useful for cross-country macroeconomic comparisons, business cycle analysis, and when the appropriate smoothing parameter is uncertain.

This package provides tools that extend the functionality of the RODBC package to work with Microsoft SQL Server databases. Makes it easier to browse the database and examine individual tables and views.

This package provides fundamental functions for descriptive statistics, including MODE(), estimate_mode(), center_stats(), position_stats(), pct(), spread_stats(), kurt(), skew(), and shape_stats(), which assist in summarizing the center, spread, and shape of numeric data. For more details, see McCurdy (2025), "Introduction to Data Science with R" <https://jonmccurdy.github.io/Introduction-to-Data-Science/>.

Multivariate Analysis methods and data sets used in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>. This also serves as a companion package for the STAT 571: Multivariate Analysis course offered by the Department of Statistics at the University of Illinois at Urbana-Champaign ('UIUC').

Wrapper for minepy implementation of Maximal Information-based Nonparametric Exploration statistics (MIC and MINE family). Detailed information of the ANSI C implementation of minepy can be found at <http://minepy.readthedocs.io/en/latest>.

This package provides tools for computing Monte Carlo standard errors (MCSE) in Markov chain Monte Carlo (MCMC) settings (survey in <doi:10.1201/b10905>, Chapter 7). MCSE computation for expectation and quantile estimators is supported as well as multivariate estimations. The package also provides functions for computing effective sample size and for plotting Monte Carlo estimates versus sample size.

This package provides supplemental functions for the mixRasch package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a plotting function to compare item parameters for multiple class models and a function that provides average theta values for each class in a mixture model.

Fast simulation from ordinary differential equation (ODE) based models typically employed in quantitative pharmacology and systems biology.

This package provides sample data sets that are used in statistics and data science courses at the Münster School of Business. The datasets refer to different business topics but also other domains, e.g. sports, traffic, etc.