Perform flexible simulation studies using one or multiple computer cores. The package is set up to be usable on high-performance clusters in addition to being run locally, see examples on <https://github.com/SachaEpskamp/parSim>.

This package implements schemes for estimating player or team skill based on dynamic updating. Implemented methods include Elo, Glicko, Glicko-2 and Stephenson. Contains pdf documentation of a reproducible analysis using approximately two million chess matches. Also contains an Elo based method for multi-player games where the result is a placing or a score. This includes zero-sum games such as poker and mahjong.

Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".

Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure. The details of the method are explained in Demirtas et al. (2012) <DOI:10.1002/sim.5362>.

The Prognostic Regression Offsets with Propagation of ERrors (for Treatment Effect Estimation) package facilitates direct adjustment for experiments and observational studies that is compatible with a range of study designs and covariance adjustment strategies. It uses explicit specification of clusters, blocks and treatment allocations to furnish probability of assignment-based weights targeting any of several average treatment effect parameters, and for standard error calculations reflecting these design parameters. For covariance adjustment of its Hajek and (one-way) fixed effects estimates, it enables offsetting the outcome against predictions from a dedicated covariance model, with standard error calculations propagating error as appropriate from the covariance model.

partitionMetric computes a distance between two partitions of a set.

This package performs the explicit calculation -- not estimation! -- of the Rasch item parameters for dichotomous and polytomous item responses, using a pairwise comparison approach. Person parameters (WLE) are calculated according to Warm's weighted likelihood approach.

Allow to run pylint on Python files with a R command or a RStudio addin. The report appears in the RStudio viewer pane as a formatted HTML file.

This package provides data set and functions for exploration of Multiple Indicator Cluster Survey (MICS) 2014 Child questionnaire data for Punjab, Pakistan (<http://www.mics.unicef.org/surveys>).

Simulates judgments of frequency and duration based on the Probability Associator Time (PASS-T) model. PASS-T is a memory model based on a simple competitive artificial neural network. It can imitate human judgments of frequency and duration, which have been extensively studied in cognitive psychology (e.g. Hintzman (1970) <doi:10.1037/h0028865>, Betsch et al. (2010) <https://psycnet.apa.org/record/2010-18204-003>). The PASS-T model is an extension of the PASS model (Sedlmeier, 2002, ISBN:0198508638). The package provides an easy way to run simulations, which can then be compared with empirical data in human judgments of frequency and duration.

This package implements propensity score weighting methods for estimating counterfactual survival functions, marginal hazard ratios, and weighted Kaplan-Meier and cumulative risk curves in observational studies with time-to-event outcomes. Supports binary and multiple treatment groups with inverse probability of treatment weighting (IPW), overlap weighting (OW), and average treatment effect on the treated (ATT). Includes symmetric trimming (Crump extension) for extreme propensity scores. Variance estimation via analytical M-estimation or bootstrap. Methods based on Li et al. (2018) <doi:10.1080/01621459.2016.1260466>, Li & Li (2019) <doi:10.1214/19-AOAS1282>, and Cheng et al. (2022) <doi:10.1093/aje/kwac043>.

Examples for integrating package perry for prediction error estimation into regression models.

An R implementation of methods employed in the field of pedometrics, soil science discipline dedicated to studying the spatial, temporal, and spatio-temporal variation of soil using statistical and computational methods. The methods found here include the calibration of linear regression models using covariate selection strategies, computation of summary validation statistics for predictions, generation of summary plots, evaluation of the local quality of a geostatistical model of uncertainty, and so on. Other functions simply extend the functionalities of or facilitate the usage of functions from other packages that are commonly used for the analysis of soil data. Formerly available versions of suggested packages no longer available from CRAN can be obtained from the CRAN archive <https://cran.r-project.org/src/contrib/Archive/>.

Computes nonparametric p-values for the potential class memberships of new observations as well as cross-validated p-values for the training data. The p-values are based on permutation tests applied to an estimated Bayesian likelihood ratio, using a plug-in statistic for the Gaussian model, k nearest neighbors', weighted nearest neighbors or penalized logistic regression'. Additionally, it provides graphical displays and quantitative analyses of the p-values.

This package provides access to the PlanScore Application Programming Interface (<https://github.com/PlanScore/PlanScore/blob/main/API.md>) for scoring redistricting plans. Allows for upload of plans from block assignment files and shape files. For shapes in memory, such as from sf or redist', it processes them to save and upload. Includes tools for tidying responses and saving output from the website.

Search CRAN metadata about packages by keyword, popularity, recent activity, package name and more. Uses the R-hub search server, see <https://r-pkg.org> and the CRAN metadata database, that contains information about CRAN packages. Note that this is _not_ a CRAN project.

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).

Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.

Allows the user to perform ANOVA tests (in a strict sense: continuous and normally-distributed Y variable and 1 or more factorial/categorical X variable(s)), with the possibility to specify the type of sum of squares (1, 2 or 3), the types of variables (Fixed or Random) and their relationships (crossed or nested) with the sole function of the package (FullyParamANOVA()). The resulting outputs are the same as in SAS software. A dataset (Butterfly) to test the function is also joined.

Classification based analysis of DNA sequences to taxonomic groupings. This package primarily implements Naive Bayesian Classifier from the Ribosomal Database Project. This approach has traditionally been used to classify 16S rRNA gene sequences to bacterial taxonomic outlines; however, it can be used for any type of gene sequence. The method was originally described by Wang, Garrity, Tiedje, and Cole in Applied and Environmental Microbiology 73(16):5261-7 <doi:10.1128/AEM.00062-07>. The package also provides functions to read in FASTA'-formatted sequence data.

This package implements projected sparse Gaussian process Kriging ('Ingram et. al.', 2008, <doi:10.1007/s00477-007-0163-9>) as an additional method for the intamap package. More details on implementation ('Barillec et. al.', 2010, <doi:10.1016/j.cageo.2010.05.008>).

This package provides a clustering approach applicable to every projection method is proposed here. The two-dimensional scatter plot of any projection method can construct a topographic map which displays unapparent data structures by using distance and density information of the data. The generalized U*-matrix renders this visualization in the form of a topographic map, which can be used to automatically define the clusters of high-dimensional data. The whole system is based on Thrun and Ultsch, "Using Projection based Clustering to Find Distance and Density based Clusters in High-Dimensional Data" <DOI:10.1007/s00357-020-09373-2>. Selecting the correct projection method will result in a visualization in which mountains surround each cluster. The number of clusters can be determined by counting valleys on the topographic map. Most projection methods are wrappers for already available methods in R. By contrast, the neighbor retrieval visualizer (NeRV) is based on C++ source code of the dredviz software package, and the Curvilinear Component Analysis (CCA) is translated from MATLAB ('SOM Toolbox 2.0) to R.

This package provides a simple function to bind a piped object to a placeholder symbol to enable complex function evaluation with the base R |> pipe.

Implementations of algorithms from Learning Sparse Penalties for Change-point Detection using Max Margin Interval Regression, by Hocking, Rigaill, Vert, Bach <http://proceedings.mlr.press/v28/hocking13.html> published in proceedings of ICML2013.