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r-mendelianrandomization
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Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods.
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2025-03-25 |
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r-melt
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Performs multiple empirical likelihood tests for linear and generalized linear models. The package offers an easy-to-use interface and flexibility in specifying hypotheses and calibration methods, extending the framework to simultaneous inferences. The core computational routines are implemented using the 'Eigen' C++ library and 'RcppEigen' interface, with OpenMP for parallel computation. Details of the testing procedures are given in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.
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2025-03-25 |
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r-meboot
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Maximum entropy density based dependent data bootstrap. An algorithm is provided to create a population of time series (ensemble) without assuming stationarity. The reference paper (Vinod, H.D., 2004 <DOI: 10.1016/j.jempfin.2003.06.002>) explains how the algorithm satisfies the ergodic theorem and the central limit theorem.
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2025-03-25 |
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r-mdmb
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Contains model-based treatment of missing data for regression models with missing values in covariates or the dependent variable using maximum likelihood or Bayesian estimation (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>; Luedtke, Robitzsch, & West, 2020a, 2020b; <doi:10.1080/00273171.2019.1640104><doi:10.1037/met0000233>). The regression model can be nonlinear (e.g., interaction effects, quadratic effects or B-spline functions). Multilevel models with missing data in predictors are available for Bayesian estimation. Substantive-model compatible multiple imputation can be also conducted.
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2025-03-25 |
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r-mcmcglmm
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Fits Multivariate Generalised Linear Mixed Models (and related models) using Markov chain Monte Carlo techniques (Hadfield 2010 J. Stat. Soft.).
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2025-03-25 |
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r-mboost
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Functional gradient descent algorithm (boosting) for optimizing general risk functions utilizing component-wise (penalised) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data. Models and algorithms are described in <doi:10.1214/07-STS242>, a hands-on tutorial is available from <doi:10.1007/s00180-012-0382-5>. The package allows user-specified loss functions and base-learners.
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2025-03-25 |
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r-mbrglm
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Fit generalized linear models with binomial responses using a median modified score approach (Kenne Pagui et al., 2016, <https://arxiv.org/abs/1604.04768>) to median bias reduction. This method respects equivariance under reparameterizations for each parameter component and also solves the infinite estimates problem (data separation).
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2025-03-25 |
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r-matrixextra
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Extends sparse matrix and vector classes from the 'Matrix' package by providing: (a) Methods and operators that work natively on CSR formats (compressed sparse row, a.k.a. 'RsparseMatrix') such as slicing/sub-setting, assignment, rbind(), mathematical operators for CSR and COO such as addition ("+") or sqrt(), and methods such as diag(); (b) Multi-threaded matrix multiplication and cross-product for many <sparse, dense> types, including the 'float32' type from 'float'; (c) Coercion methods between pairs of classes which are not present in 'Matrix', such as 'dgCMatrix' -> 'ngRMatrix', as well as convenience conversion functions; (d) Utility functions for sparse matrices such as sorting the indices or removing zero-valued entries; (e) Fast transposes that work by outputting in the opposite storage format; (f) Faster replacements for many 'Matrix' methods for all sparse types, such as slicing and elementwise multiplication. (g) Convenience functions for sparse objects, such as 'mapSparse' or a shorter 'show' method.
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2025-03-25 |
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r-mattransmix
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Provides matrix Gaussian mixture models, matrix transformation mixture models and their model-based clustering results. The parsimonious models of the mean matrices and variance covariance matrices are implemented with a total of 196 variations.
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2025-03-25 |
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r-maxstat
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Maximally selected rank statistics with several p-value approximations.
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2025-03-25 |
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r-matchit
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Selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>. (The 'gurobi' package, which is not on CRAN, is optional and comes with an installation of the Gurobi Optimizer, available at <https://www.gurobi.com>.)
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2025-03-25 |
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r-marked
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Functions for fitting various models to capture-recapture data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and the multi-variate state model structure for survival estimation and POPAN structured Jolly-Seber models for abundance estimation. There are also Hidden Markov model (HMM) implementations of CJS and multistate models with and without state uncertainty and a simulation capability for HMM models.
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2025-03-25 |
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r-markovchain
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Functions and S4 methods to create and manage discrete time Markov chains more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of their structural proprieties) analysis are provided. See Spedicato (2017) <doi:10.32614/RJ-2017-036>. Some functions for continuous times Markov chains depends on the suggested ctmcd package, that, as May 2023, can be retrieved from <https://cran.r-project.org/src/contrib/Archive/ctmcd/ctmcd_1.4.2.tar.gz>.
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2025-03-25 |
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r-marginaleffects
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Compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and machine learning models in R. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference.
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2025-03-25 |
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r-mapsf
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Create and integrate thematic maps in your workflow. This package helps to design various cartographic representations such as proportional symbols, choropleth or typology maps. It also offers several functions to display layout elements that improve the graphic presentation of maps (e.g. scale bar, north arrow, title, labels). 'mapsf' maps 'sf' objects on 'base' graphics.
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2025-03-25 |
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r-mapdeck
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Provides a mechanism to plot an interactive map using 'Mapbox GL' (<https://docs.mapbox.com/mapbox-gl-js/api/>), a javascript library for interactive maps, and 'Deck.gl' (<https://deck.gl/>), a javascript library which uses 'WebGL' for visualising large data sets.
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2025-03-25 |
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r-luminescence
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A collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.
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2025-03-25 |
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r-lqmm
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Functions to fit quantile regression models for hierarchical data (2-level nested designs) as described in Geraci and Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>. A vignette is given in Geraci (2014, Journal of Statistical Software) <doi:10.18637/jss.v057.i13> and included in the package documents. The packages also provides functions to fit quantile models for independent data and for count responses.
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2025-03-25 |
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r-lpirfs
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Provides functions to estimate and visualize linear as well as nonlinear impulse responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>. The methods and the package are explained in detail in Adämmer (2019) <doi:10.32614/RJ-2019-052>.
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2025-03-25 |
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r-logistf
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Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression, see Heinze and Schemper (2002) <doi:10.1002/sim.1047>. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained. Two new modifications of Firth's method, FLIC and FLAC, lead to unbiased predictions and are now available in the package as well, see Puhr et al (2017) <doi:10.1002/sim.7273>.
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2025-03-25 |
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r-lmomrfa
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Functions for regional frequency analysis using the methods of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis: an approach based on L-moments".
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2025-03-25 |
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r-lime
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When building complex models, it is often difficult to explain why the model should be trusted. While global measures such as accuracy are useful, they cannot be used for explaining why a model made a specific prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for explaining the outcome of black box models by fitting a local model around the point in question an perturbations of this point. The approach is described in more detail in the article by Ribeiro et al. (2016) <arXiv:1602.04938>.
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2025-03-25 |
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r-lightgbm
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Tree based algorithms can be improved by introducing boosting frameworks. 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision>. This package offers an R interface to work with it. It is designed to be distributed and efficient with the following advantages: 1. Faster training speed and higher efficiency. 2. Lower memory usage. 3. Better accuracy. 4. Parallel learning supported. 5. Capable of handling large-scale data. In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions. Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.
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2025-03-25 |
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r-lidr
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Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.
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2025-03-25 |
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r-lifecontingencies
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Classes and methods that allow the user to manage life table, actuarial tables (also multiple decrements tables). Moreover, functions to easily perform demographic, financial and actuarial mathematics on life contingencies insurances calculations are contained therein. See Spedicato (2013) <doi:10.18637/jss.v055.i10>.
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2025-03-25 |
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r-libgeos
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Provides the Open Source Geometry Engine ('GEOS') as a C API that can be used to write high-performance C and C++ geometry operations using R as an interface. Headers are provided to make linking to and using these functions from C++ code as easy and as safe as possible. This package contains an internal copy of the 'GEOS' library to guarantee the best possible consistency on multiple platforms.
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2025-03-25 |
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r-leidenbase
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An R to C/C++ interface that runs the Leiden community detection algorithm to find a basic partition (). It runs the equivalent of the 'leidenalg' find_partition() function, which is given in the 'leidenalg' distribution file 'leiden/src/functions.py'. This package includes the required source code files from the official 'leidenalg' distribution and functions from the R 'igraph' package. The 'leidenalg' distribution is available from <https://github.com/vtraag/leidenalg/> and the R 'igraph' package is available from <https://igraph.org/r/>. The Leiden algorithm is described in the article by Traag et al. (2019) <doi:10.1038/s41598-019-41695-z>. Leidenbase includes code from the packages: igraph version 0.9.8 with license GPL (>= 2), leidenalg version 0.8.10 with license GPL 3.
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2025-03-25 |
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r-leidenalg
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An R interface to the Leiden algorithm, an iterative community detection algorithm on networks. The algorithm is designed to converge to a partition in which all subsets of all communities are locally optimally assigned, yielding communities guaranteed to be connected. The implementation proves to be fast, scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The original implementation was constructed as a python interface "leidenalg" found here: <https://github.com/vtraag/leidenalg>. The algorithm was originally described in Traag, V.A., Waltman, L. & van Eck, N.J. "From Louvain to Leiden: guaranteeing well-connected communities". Sci Rep 9, 5233 (2019) <doi:10.1038/s41598-019-41695-z>.
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2025-03-25 |
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r-lavasearch2
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Tools for model specification in the latent variable framework (add-on to the 'lava' package). The package contains three main functionalities: Wald tests/F-tests with improved control of the type 1 error in small samples, adjustment for multiple comparisons when searching for local dependencies, and adjustment for multiple comparisons when doing inference for multiple latent variable models.
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2025-03-25 |
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r-latentnet
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Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.
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2025-03-25 |
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r-languageserver
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An implementation of the Language Server Protocol for R. The Language Server protocol is used by an editor client to integrate features like auto completion. See <https://microsoft.github.io/language-server-protocol/> for details.
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2025-03-25 |
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r-landscapemetrics
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Calculates landscape metrics for categorical landscape patterns in a tidy workflow. 'landscapemetrics' reimplements the most common metrics from 'FRAGSTATS' (<https://www.fragstats.org/>) and new ones from the current literature on landscape metrics. This package supports 'terra' SpatRaster objects as input arguments. It further provides utility functions to visualize patches, select metrics and building blocks to develop new metrics.
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2025-03-25 |
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r-labdsv
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A variety of ordination and community analyses useful in analysis of data sets in community ecology. Includes many of the common ordination methods, with graphical routines to facilitate their interpretation, as well as several novel analyses.
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2025-03-25 |
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r-lamw
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Implements both real-valued branches of the Lambert-W function (Corless et al, 1996) <doi:10.1007/BF02124750> without the need for installing the entire GSL.
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2025-03-25 |
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r-lagp
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Performs approximate GP regression for large computer experiments and spatial datasets. The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine. OpenMP and GPU features may require special compilation. An interface to lower-level (full) GP inference and prediction is provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided. For details and tutorial, see Gramacy (2016 <doi:10.18637/jss.v072.i01>.
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2025-03-25 |
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r-lambertw
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Lambert W x F distributions are a generalized framework to analyze skewed, heavy-tailed data. It is based on an input/output system, where the output random variable (RV) Y is a non-linearly transformed version of an input RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed). The transformed RV Y has a Lambert W x F distribution. This package contains functions to model and analyze skewed, heavy-tailed data the Lambert Way: simulate random samples, estimate parameters, compute quantiles, and plot/ print results nicely. The most useful function is 'Gaussianize', which works similarly to 'scale', but actually makes the data Gaussian. A do-it-yourself toolkit allows users to define their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis right away.
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2025-03-25 |
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r-kit
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Basic functions, implemented in C, for large data manipulation. Fast vectorised ifelse()/nested if()/switch() functions, psum()/pprod() functions equivalent to pmin()/pmax() plus others which are missing from base R. Most of these functions are callable at C level.
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2025-03-25 |
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r-kml
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An implementation of k-means specifically design to cluster longitudinal data. It provides facilities to deal with missing value, compute several quality criterion (Calinski and Harabatz, Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical interface for choosing the 'best' number of clusters.
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2025-03-25 |
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r-kalmanfilter
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'Rcpp' implementation of the multivariate Kalman filter for state space models that can handle missing values and exogenous data in the observation and state equations. Kim, Chang-Jin and Charles R. Nelson (1999) "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications" <doi:10.7551/mitpress/6444.001.0001><http://econ.korea.ac.kr/~cjkim/>.
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2025-03-25 |
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r-kde1d
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Provides an efficient implementation of univariate local polynomial kernel density estimators that can handle bounded and discrete data. See Geenens (2014) <arXiv:1303.4121>, Geenens and Wang (2018) <arXiv:1602.04862>, Nagler (2018a) <arXiv:1704.07457>, Nagler (2018b) <arXiv:1705.05431>.
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2025-03-25 |
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r-kdecopula
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Provides fast implementations of kernel smoothing techniques for bivariate copula densities, in particular density estimation and resampling, see Nagler (2018) <doi:10.18637/jss.v084.i07>.
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2025-03-25 |
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r-joinerml
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Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).
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2025-03-25 |
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r-jomo
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Similarly to package 'pan', 'jomo' is a package for multilevel joint modelling multiple imputation (Carpenter and Kenward, 2013) <doi:10.1002/9781119942283>. Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.
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2025-03-25 |
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r-jinjar
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Template engine powered by the 'inja' C++ library. Users write a template document, using syntax inspired by the 'Jinja' Python package, and then render the final document by passing data from R. The template syntax supports features such as variables, loops, conditions and inheritance.
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2025-03-25 |
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r-jiebar
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Chinese text segmentation, keyword extraction and speech tagging For R.
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2025-03-25 |
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r-jfa
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Provides statistical methods for auditing as implemented in JASP for Audit (Derks et al., 2021 <doi:10.21105/joss.02733>). First, the package makes it easy for an auditor to plan a statistical sample, select the sample from the population, and evaluate the misstatement in the sample compliant with international auditing standards. Second, the package provides statistical methods for auditing data, including tests of digit distributions and repeated values. Finally, the package includes methods for auditing algorithms on the aspect of fairness and bias. Next to classical statistical methodology, the package implements Bayesian equivalents of these methods whose statistical underpinnings are described in Derks et al. (2021) <doi:10.1111/ijau.12240>, Derks et al. (2021) <doi:10.31234/osf.io/kzqp5>, and Derks et al. (2022) <doi:10.31234/osf.io/8nf3e>.
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2025-03-25 |
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r-isotone
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Contains two main functions: one for solving general isotone regression problems using the pool-adjacent-violators algorithm (PAVA); another one provides a framework for active set methods for isotone optimization problems with arbitrary order restrictions. Various types of loss functions are prespecified.
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2025-03-25 |
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r-irtoys
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A collection of functions useful in learning and practicing IRT, which can be combined into larger programs. Provides basic CTT analysis, a simple common interface to the estimation of item parameters in IRT models for binary responses with three different programs (ICL, BILOG-MG, and ltm), ability estimation (MLE, BME, EAP, WLE, plausible values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord, and the complete Hebaera method), and a rich array of parametric and non-parametric (kernel) plots. Estimates and plots Haberman's interaction model when all items are dichotomously scored.
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2025-03-25 |
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r-inext
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Provides simple functions to compute and plot two types (sample-size- and coverage-based) rarefaction and extrapolation curves for species diversity (Hill numbers) based on individual-based abundance data or sampling-unit- based incidence data; see Chao and others (2014, Ecological Monographs) for pertinent theory and methodologies, and Hsieh, Ma and Chao (2016, Methods in Ecology and Evolution) for an introduction of the R package.
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2025-03-25 |
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r-inspectdf
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A collection of utilities for columnwise summary, comparison and visualisation of data frames. Functions report missingness, categorical levels, numeric distribution, correlation, column types and memory usage.
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2025-03-25 |
