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r-leadercluster
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public |
The leader clustering algorithm provides a means for clustering a set of data points. Unlike many other clustering algorithms it does not require the user to specify the number of clusters, but instead requires the approximate radius of a cluster as its primary tuning parameter. The package provides a fast implementation of this algorithm in n-dimensions using Lp-distances (with special cases for p=1,2, and infinity) as well as for spatial data using the Haversine formula, which takes latitude/longitude pairs as inputs and clusters based on great circle distances.
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2024-01-16 |
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r-lcopula
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public |
Collections of functions allowing random number generations and estimation of 'Liouville' copulas, as described in Belzile and Neslehova (2017) <doi:10.1016/j.jmva.2017.05.008>.
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2024-01-16 |
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r-lcmm
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public |
Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).
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2024-01-16 |
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r-latentnet
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public |
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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2024-01-16 |
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r-lcmcr
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public |
Bayesian population size estimation using non parametric latent-class models.
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2024-01-16 |
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r-lbfgsb3c
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public |
Interfacing to Nocedal et al. L-BFGS-B.3.0 (See <http://users.iems.northwestern.edu/~nocedal/lbfgsb.html>) limited memory BFGS minimizer with bounds on parameters. This is a fork of 'lbfgsb3'. This registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same function types and optimization as the optim() function (see writing 'R' extensions and source for details). This package also adds more stopping criteria as well as allowing the adjustment of more tolerances.
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2024-01-16 |
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r-lbfgs
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public |
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
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2024-01-16 |
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r-lazyeval
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None |
An alternative approach to non-standard evaluation using formulas. Provides a full implementation of LISP style 'quasiquotation', making it easier to generate code with other code.
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2024-01-16 |
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r-lazy
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public |
By combining constant, linear, and quadratic local models, lazy estimates the value of an unknown multivariate function on the basis of a set of possibly noisy samples of the function itself. This implementation of lazy learning automatically adjusts the bandwidth on a query-by-query basis through a leave-one-out cross-validation.
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2024-01-16 |
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r-latticedesign
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public |
Lattice-based space-filling designs with fill or separation distance properties including interleaved lattice-based minimax distance designs proposed in Xu He (2017) <doi:10.1093/biomet/asx036>, interleaved lattice-based maximin distance designs proposed in Xu He (2018) <doi:10.1093/biomet/asy069>, (sliced) rotated sphere packing designs proposed in Xu He (2017) <doi:10.1080/01621459.2016.1222289> and Xu He (2019) <doi:10.1080/00401706.2018.1458655>, and densest packing-based maximum projections designs proposed in Xu He (2020) <doi:10.1093/biomet/asaa057> and Xu He (2018) <arXiv:1709.02062v2>.
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2024-01-16 |
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r-languageserver
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public |
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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2024-01-16 |
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r-lattice
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None |
A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. Lattice is sufficient for typical graphics needs, and is also flexible enough to handle most nonstandard requirements. See ?Lattice for an introduction.
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2024-01-16 |
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r-later
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public |
Executes arbitrary R or C functions some time after the current time, after the R execution stack has emptied. The functions are scheduled in an event loop.
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2024-01-16 |
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r-lassobacktracking
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public |
Implementation of the algorithm introduced in Shah, R. D. (2016) <https://www.jmlr.org/papers/volume17/13-515/13-515.pdf>. Data with thousands of predictors can be handled. The algorithm performs sequential Lasso fits on design matrices containing increasing sets of candidate interactions. Previous fits are used to greatly speed up subsequent fits, so the algorithm is very efficient.
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2024-01-16 |
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r-landscapemetrics
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public |
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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2024-01-16 |
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r-lassoshooting
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public |
L1 regularized regression (Lasso) solver using the Cyclic Coordinate Descent algorithm aka Lasso Shooting is fast. This implementation can choose which coefficients to penalize. It support coefficient-specific penalties and it can take X'X and X'y instead of X and y.
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2024-01-16 |
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r-labdsv
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public |
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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2024-01-16 |
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r-lars
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public |
Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitesimal forward stagewise regression are related to the lasso, as described in the paper below.
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2024-01-16 |
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r-langevin
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public |
Estimate drift and diffusion functions from time series and generate synthetic time series from given drift and diffusion coefficients.
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2024-01-16 |
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r-landscaper
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public |
Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format.
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2024-01-16 |
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r-lagp
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public |
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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2024-01-16 |
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r-lamw
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public |
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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2024-01-16 |
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r-lambertw
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public |
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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2024-01-16 |
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r-laf
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public |
Methods for fast access to large ASCII files. Currently the following file formats are supported: comma separated format (CSV) and fixed width format. It is assumed that the files are too large to fit into memory, although the package can also be used to efficiently access files that do fit into memory. Methods are provided to access and process files blockwise. Furthermore, an opened file can be accessed as one would an ordinary data.frame. The LaF vignette gives an overview of the functionality provided.
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2024-01-16 |
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r-l1pack
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public |
L1 estimation for linear regression using Barrodale and Roberts' method <doi:10.1145/355616.361024> and the EM algorithm <doi:10.1023/A:1020759012226>, density, distribution function, quantile function and random number generation for univariate and multivariate Laplace distribution <doi:10.1080/03610929808832115>.
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2024-01-16 |
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r-l0ara
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public |
An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.
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2024-01-16 |
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r-kml
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public |
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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2024-01-16 |
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r-kyotil
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public |
Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.
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2024-01-16 |
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r-kza
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public |
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
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2024-01-16 |
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r-kvh
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public |
The format KVH is a lightweight format that can be read/written both by humans and machines. It can be useful in situations where XML or alike formats seem to be an overkill. We provide an ability to parse KVH files in R pretty fast due to 'Rcpp' use.
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2024-01-16 |
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r-kit
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public |
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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2024-01-16 |
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r-ksnn
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public |
Prediction with k* nearest neighbor algorithm based on a publication by Anava and Levy (2016) <arXiv:1701.07266>.
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2024-01-16 |
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r-ksgeneral
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public |
Computes a p-value of the one-sample two-sided (or one-sided, as a special case) Kolmogorov-Smirnov (KS) statistic, for any fixed critical level, and an arbitrary, possibly large sample size for a pre-specified purely discrete, mixed or continuous cumulative distribution function (cdf) under the null hypothesis. If a data sample is supplied, 'KSgeneral' computes the p-value corresponding to the value of the KS test statistic computed based on the user provided data sample. The package 'KSgeneral' implements a novel, accurate and efficient method named Exact-KS-FFT, expressing the p-value as a double-boundary non-crossing probability for a homogeneous Poisson process, which is then efficiently computed using Fast Fourier Transform (FFT). The package can also be used to compute and plot the complementary cdf of the KS statistic which is known to depend on the hypothesized distribution when the latter is discontinuous (i.e. purely discrete or mixed). To cite this package in publication use: Dimitrina S. Dimitrova, Vladimir K. Kaishev, and Senren Tan. Computing the Kolmogorov-Smirnov Distribution When the Underlying CDF is Purely Discrete, Mixed, or Continuous. Journal of Statistical Software. 2020; 95(10): 1--42. <doi:10.18637/jss.v095.i10>.
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2024-01-16 |
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r-ksamples
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public |
Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie pattern. Except for Steel's test and the JT test it also combines these tests across several blocks of samples. Also analyzed are 2 x t contingency tables and their blocked combinations using the Kruskal-Wallis criterion. Steel's test is inverted to provide simultaneous confidence bounds for shift parameters. A plotting function compares tail probabilities obtained under asymptotic approximation with those obtained via simulation or exact calculations.
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2024-01-16 |
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r-ks
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public |
Kernel smoothers for univariate and multivariate data, with comprehensive visualisation and bandwidth selection capabilities, including for densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Chacon & Duong (2018) <doi:10.1201/9780429485572>.
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2024-01-16 |
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r-kriging
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public |
An implementation of a simple and highly optimized ordinary kriging algorithm to plot geographical data.
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2024-01-16 |
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r-krige
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public |
Estimates kriging models for geographical point-referenced data. Method is described in Gill (2020) <doi:10.1177/1532440020930197>.
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2024-01-16 |
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r-konpsurv
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public |
The K-sample omnibus non-proportional hazards (KONP) tests are powerful non-parametric tests for comparing K (>=2) hazard functions based on right-censored data (Gorfine, Schlesinger and Hsu, 2020, <doi:10.1177/0962280220907355>). These tests are consistent against any differences between the hazard functions of the groups. The KONP tests are often more powerful than other existing tests, especially under non-proportional hazard functions.
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2024-01-16 |
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r-kohonen
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None |
Functions to train self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
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2024-01-16 |
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r-kodama
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public |
An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.
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2024-01-16 |
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r-kalmanfilter
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public |
'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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2024-01-16 |
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r-kknn
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public |
Weighted k-Nearest Neighbors for Classification, Regression and Clustering.
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2024-01-16 |
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r-keyring
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public |
Platform independent 'API' to access the operating system's credential store. Currently supports: 'Keychain' on 'macOS', Credential Store on 'Windows', the Secret Service 'API' on 'Linux', and a simple, platform independent store implemented with environment variables. Additional storage back-ends can be added easily.
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2024-01-16 |
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r-kissmig
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public |
Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, stochastic migration model. Providing accessibility from an origin or previous distribution for niche-based species distribution models. Nobis & Normand (2014) <doi:10.1111/ecog.00930>.
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2024-01-16 |
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r-kfas
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public |
State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
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2024-01-16 |
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r-kde1d
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public |
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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2024-01-16 |
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r-keypress
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public |
Wait for a single key press at the 'R' prompt. This works in terminals, but does not currently work in the 'Windows' 'GUI', the 'OS X' 'GUI' ('R.app'), in 'Emacs' 'ESS', in an 'Emacs' shell buffer or in 'R Studio'. In these cases 'keypress' stops with an error message.
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2024-01-16 |
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r-kdecopula
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public |
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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2024-01-16 |
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r-kernsmooth
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None |
Functions for kernel smoothing (and density estimation) corresponding to the book: Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
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2024-01-16 |
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r-kernlab
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public |
Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.
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2024-01-16 |
