Showing 195 of total 195 results (show query)
spatstat
spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family
Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Maintained by Adrian Baddeley. Last updated 13 days ago.
cluster-detectionconfidence-intervalshypothesis-testingk-functionroc-curvesscan-statisticssignificance-testingsimulation-envelopesspatial-analysisspatial-data-analysisspatial-sharpeningspatial-smoothingspatial-statistics
32.3 match 1 stars 10.18 score 67 scripts 150 dependents
jeremygelb
spNetwork:Spatial Analysis on Network
Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).
Maintained by Jeremy Gelb. Last updated 2 days ago.
kernelkernel-density-estimationnetworknetwork-analysisspatialspatial-analysisspatial-data-analysiscpp
23.1 match 38 stars 7.74 score 52 scripts
jeffreyracine
np:Nonparametric Kernel Smoothing Methods for Mixed Data Types
Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.
Maintained by Jeffrey S. Racine. Last updated 2 months ago.
13.6 match 49 stars 12.64 score 672 scripts 44 dependents
tarnduong
ks:Kernel Smoothing
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>.
Maintained by Tarn Duong. Last updated 6 months ago.
13.3 match 6 stars 10.19 score 920 scripts 262 dependents
lbb220
GWmodel:Geographically-Weighted Models
Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002)<doi: 10.1016/s0198-9715(01)00009-6>, GW principal components analysis (Harris et al., 2011)<doi: 10.1080/13658816.2011.554838>, GW discriminant analysis (Brunsdon et al., 2007)<doi: 10.1111/j.1538-4632.2007.00709.x> and various forms of GW regression (Brunsdon et al., 1996)<doi: 10.1111/j.1538-4632.1996.tb00936.x>; some of which are provided in basic and robust (outlier resistant) forms.
Maintained by Binbin Lu. Last updated 7 months ago.
19.4 match 18 stars 6.38 score 266 scripts 4 dependents
tilmandavies
sparr:Spatial and Spatiotemporal Relative Risk
Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.
Maintained by Tilman M. Davies. Last updated 13 days ago.
15.9 match 8 stars 7.38 score 112 scripts 8 dependents
iagogv3
kedd:Kernel Estimator and Bandwidth Selection for Density and Its Derivatives
Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data (described in Arsalane Chouaib Guidoum (2020) <arXiv:2012.06102> [stat.CO]).
Maintained by Iago Giné-Vázquez. Last updated 1 years ago.
20.6 match 5.23 score 57 scripts 1 dependents
cran
MASS:Support Functions and Datasets for Venables and Ripley's MASS
Functions and datasets to support Venables and Ripley, "Modern Applied Statistics with S" (4th edition, 2002).
Maintained by Brian Ripley. Last updated 1 months ago.
9.4 match 19 stars 10.64 score 11k dependentsspatstat
spatstat.univar:One-Dimensional Probability Distribution Support for the 'spatstat' Family
Estimation of one-dimensional probability distributions including kernel density estimation, weighted empirical cumulative distribution functions, Kaplan-Meier and reduced-sample estimators for right-censored data, heat kernels, kernel properties, quantiles and integration.
Maintained by Adrian Baddeley. Last updated 19 hours ago.
9.5 match 3 stars 10.14 score 1 scripts 245 dependents
andyliaw-mrk
locfit:Local Regression, Likelihood and Density Estimation
Local regression, likelihood and density estimation methods as described in the 1999 book by Loader.
Maintained by Andy Liaw. Last updated 27 days ago.
9.2 match 1 stars 9.40 score 428 scripts 606 dependents
jmsigner
amt:Animal Movement Tools
Manage and analyze animal movement data. The functionality of 'amt' includes methods to calculate home ranges, track statistics (e.g. step lengths, speed, or turning angles), prepare data for fitting habitat selection analyses, and simulation of space-use from fitted step-selection functions.
Maintained by Johannes Signer. Last updated 5 months ago.
7.5 match 41 stars 10.54 score 418 scripts
bquast
rddtools:Toolbox for Regression Discontinuity Design ('RDD')
Set of functions for Regression Discontinuity Design ('RDD'), for data visualisation, estimation and testing.
Maintained by Bastiaan Quast. Last updated 1 years ago.
11.1 match 11 stars 6.65 score 203 scripts
tnagler
kdecopula:Kernel Smoothing for Bivariate Copula Densities
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>.
Maintained by Thomas Nagler. Last updated 8 days ago.
12.4 match 8 stars 5.57 score 31 scripts 1 dependents
salbeke
rKIN:(Kernel) Isotope Niche Estimation
Applies methods used to estimate animal homerange, but instead of geospatial coordinates, we use isotopic coordinates. The estimation methods include: 1) 2-dimensional bivariate normal kernel utilization density estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon estimator, all applied to stable isotope data. Additionally, functions to determine niche area, polygon overlap between groups and levels (confidence contours) and plotting capabilities.
Maintained by Shannon E Albeke. Last updated 29 days ago.
12.2 match 4 stars 5.13 score 34 scripts
robjhyndman
hdrcde:Highest Density Regions and Conditional Density Estimation
Computation of highest density regions in one and two dimensions, kernel estimation of univariate density functions conditional on one covariate,and multimodal regression.
Maintained by Rob Hyndman. Last updated 2 years ago.
5.9 match 24 stars 10.38 score 128 scripts 158 dependents
rkoenker
quantreg:Quantile Regression
Estimation and inference methods for models for conditional quantile functions: Linear and nonlinear parametric and non-parametric (total variation penalized) models for conditional quantiles of a univariate response and several methods for handling censored survival data. Portfolio selection methods based on expected shortfall risk are also now included. See Koenker, R. (2005) Quantile Regression, Cambridge U. Press, <doi:10.1017/CBO9780511754098> and Koenker, R. et al. (2017) Handbook of Quantile Regression, CRC Press, <doi:10.1201/9781315120256>.
Maintained by Roger Koenker. Last updated 23 days ago.
4.1 match 18 stars 13.93 score 2.6k scripts 1.5k dependents
spatstat
spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family
Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models.
Maintained by Adrian Baddeley. Last updated 2 months ago.
density-estimationheat-equationkernel-density-estimationnetwork-analysispoint-processesspatial-data-analysisstatistical-analysisstatistical-inferencestatistical-models
5.5 match 6 stars 9.58 score 35 scripts 42 dependents
reinhardfurrer
spam:SPArse Matrix
Set of functions for sparse matrix algebra. Differences with other sparse matrix packages are: (1) we only support (essentially) one sparse matrix format, (2) based on transparent and simple structure(s), (3) tailored for MCMC calculations within G(M)RF. (4) and it is fast and scalable (with the extension package spam64). Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details.
Maintained by Reinhard Furrer. Last updated 2 months ago.
5.3 match 1 stars 9.36 score 420 scripts 439 dependents
hotneim
lg:Locally Gaussian Distributions: Estimation and Methods
An implementation of locally Gaussian distributions. It provides methods for implementing locally Gaussian multivariate density estimation, conditional density estimation, various independence tests for iid and time series data, a test for conditional independence and a test for financial contagion.
Maintained by Håkon Otneim. Last updated 5 years ago.
11.3 match 4 stars 4.18 score 25 scripts
rubenfcasal
baggingbwsel:Bagging Bandwidth Selection in Kernel Density and Regression Estimation
Bagging bandwidth selection methods for the Parzen-Rosenblatt and Nadaraya-Watson estimators. These bandwidth selectors can achieve greater statistical precision than their non-bagged counterparts while being computationally fast. See Barreiro-Ures et al. (2020) <doi:10.1093/biomet/asaa092> and Barreiro-Ures et al. (2021) <doi:10.48550/arXiv.2105.04134>.
Maintained by Ruben Fernandez-Casal. Last updated 6 months ago.
15.4 match 3.00 score
bioc
RBGL:An interface to the BOOST graph library
A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library.
Maintained by Bioconductor Package Maintainer. Last updated 5 months ago.
5.2 match 8.59 score 320 scripts 132 dependents
felixthoemmes
rddapp:Regression Discontinuity Design Application
Estimation of both single- and multiple-assignment Regression Discontinuity Designs (RDDs). Provides both parametric (global) and non-parametric (local) estimation choices for both sharp and fuzzy designs, along with power analysis and assumption checks. Introductions to the underlying logic and analysis of RDDs are in Thistlethwaite, D. L., Campbell, D. T. (1960) <doi:10.1037/h0044319> and Lee, D. S., Lemieux, T. (2010) <doi:10.1257/jel.48.2.281>.
Maintained by Felix Thoemmes. Last updated 2 years ago.
non-parametric-rddparametric-rddrdd
7.0 match 9 stars 6.30 score 44 scripts
icasas
tvReg:Time-Varying Coefficient for Single and Multi-Equation Regressions
Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.
Maintained by Isabel Casas. Last updated 2 years ago.
autoregressivenonparametricregressionsurevectorautoregressive
6.6 match 19 stars 6.25 score 62 scripts
paulponcet
statip:Statistical Functions for Probability Distributions and Regression
A collection of miscellaneous statistical functions for probability distributions: 'dbern()', 'pbern()', 'qbern()', 'rbern()' for the Bernoulli distribution, and 'distr2name()', 'name2distr()' for distribution names; probability density estimation: 'densityfun()'; most frequent value estimation: 'mfv()', 'mfv1()'; other statistical measures of location: 'cv()' (coefficient of variation), 'midhinge()', 'midrange()', 'trimean()'; construction of histograms: 'histo()', 'find_breaks()'; calculation of the Hellinger distance: 'hellinger()'; use of classical kernels: 'kernelfun()', 'kernel_properties()'; univariate piecewise-constant regression: 'picor()'.
Maintained by Paul Poncet. Last updated 5 years ago.
5.6 match 2 stars 7.17 score 73 scripts 52 dependents
jagm03
kernstadapt:Adaptive Kernel Estimators for Point Process Intensities on Linear Networks
Adaptive estimation of the first-order intensity function of a spatio-temporal point process using kernels and variable bandwidths. The methodology used for estimation is presented in González and Moraga (2022). <doi:10.48550/arXiv.2208.12026>.
Maintained by Jonatan A González. Last updated 6 months ago.
7.1 match 6 stars 5.51 score 12 scripts
tobiaskley
quantspec:Quantile-Based Spectral Analysis of Time Series
Methods to determine, smooth and plot quantile periodograms for univariate and multivariate time series.
Maintained by Tobias Kley. Last updated 9 years ago.
6.6 match 10 stars 5.84 score 46 scripts 1 dependents
r-forge
lokern:Kernel Regression Smoothing with Local or Global Plug-in Bandwidth
Kernel regression smoothing with adaptive local or global plug-in bandwidth selection.
Maintained by Martin Maechler. Last updated 4 months ago.
6.9 match 5.53 score 64 scripts 5 dependents
mjskay
ggdist:Visualizations of Distributions and Uncertainty
Provides primitives for visualizing distributions using 'ggplot2' that are particularly tuned for visualizing uncertainty in either a frequentist or Bayesian mode. Both analytical distributions (such as frequentist confidence distributions or Bayesian priors) and distributions represented as samples (such as bootstrap distributions or Bayesian posterior samples) are easily visualized. Visualization primitives include but are not limited to: points with multiple uncertainty intervals, eye plots (Spiegelhalter D., 1999) <https://ideas.repec.org/a/bla/jorssa/v162y1999i1p45-58.html>, density plots, gradient plots, dot plots (Wilkinson L., 1999) <doi:10.1080/00031305.1999.10474474>, quantile dot plots (Kay M., Kola T., Hullman J., Munson S., 2016) <doi:10.1145/2858036.2858558>, complementary cumulative distribution function barplots (Fernandes M., Walls L., Munson S., Hullman J., Kay M., 2018) <doi:10.1145/3173574.3173718>, and fit curves with multiple uncertainty ribbons.
Maintained by Matthew Kay. Last updated 4 months ago.
ggplot2uncertaintyuncertainty-visualizationvisualizationcpp
2.3 match 859 stars 14.95 score 3.1k scripts 62 dependents
maikol-solis
sobolnp:Nonparametric Sobol Estimator with Bootstrap Bandwidth
Algorithm to estimate the Sobol indices using a non-parametric fit of the regression curve. The bandwidth is estimated using bootstrap to reduce the finite-sample bias. The package is based on the paper Solís, M. (2018) <arXiv:1803.03333>.
Maintained by Maikol Solís. Last updated 2 years ago.
bandwidthbootstrapcross-validationnonparametric-regressionsensitivity-analysis
16.3 match 2.00 score 1 scriptscran
locpol:Kernel Local Polynomial Regression
Computes local polynomial estimators for the regression and also density. It comprises several different utilities to handle kernel estimators.
Maintained by Jorge Luis Ojeda Cabrera. Last updated 2 years ago.
9.4 match 2 stars 3.38 score 22 dependents
rsbivand
spgwr:Geographically Weighted Regression
Functions for computing geographically weighted regressions are provided, based on work by Chris Brunsdon, Martin Charlton and Stewart Fotheringham.
Maintained by Roger Bivand. Last updated 10 months ago.
3.7 match 3 stars 8.14 score 436 scripts 3 dependentscran
KernSmooth:Functions for Kernel Smoothing Supporting Wand & Jones (1995)
Functions for kernel smoothing (and density estimation) corresponding to the book: Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
Maintained by Brian Ripley. Last updated 3 months ago.
3.6 match 1 stars 8.19 score 2.5k dependents
cran
rlcv:Robust Likelihood Cross Validation Bandwidth Selection
Robust likelihood cross validation bandwidth for uni- and multi-variate kernel densities. It is robust against fat-tailed distributions and/or outliers. Based on "Robust Likelihood Cross-Validation for Kernel Density Estimation," Wu (2019) <doi:10.1080/07350015.2018.1424633>.
Maintained by Ximing Wu. Last updated 3 years ago.
10.3 match 2.70 score
egarpor
polykde:Polyspherical Kernel Density Estimation
Kernel density estimation on the polysphere, hypersphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to García-Portugués and Meilán-Vila (2024) <doi:10.48550/arXiv.2411.04166> and García-Portugués and Meilán-Vila (2023) <doi:10.1007/978-3-031-32729-2_4>.
Maintained by Eduardo García-Portugués. Last updated 2 months ago.
circular-statisticsdirectional-statisticskernel-smoothingopenblascpp
9.0 match 3.00 score 5 scripts
kevinjaunatre
extremefit:Estimation of Extreme Conditional Quantiles and Probabilities
Extreme value theory, nonparametric kernel estimation, tail conditional probabilities, extreme conditional quantile, adaptive estimation, quantile regression, survival probabilities.
Maintained by Kevin Jaunatre. Last updated 6 years ago.
8.6 match 1 stars 3.07 score 39 scripts 1 dependents
kangy10
DRIP:Discontinuous Regression and Image Processing
A collection of functions that perform jump regression and image analysis such as denoising, deblurring and jump detection. The implemented methods are based on the following research: Qiu, P. (1998) <doi:10.1214/aos/1024691468>, Qiu, P. and Yandell, B. (1997) <doi: 10.1080/10618600.1997.10474746>, Qiu, P. (2009) <doi: 10.1007/s10463-007-0166-9>, Kang, Y. and Qiu, P. (2014) <doi: 10.1080/00401706.2013.844732>, Qiu, P. and Kang, Y. (2015) <doi: 10.5705/ss.2014.054>, Kang, Y., Mukherjee, P.S. and Qiu, P. (2018) <doi: 10.1080/00401706.2017.1415975>, Kang, Y. (2020) <doi: 10.1080/10618600.2019.1665536>.
Maintained by Yicheng Kang. Last updated 4 months ago.
3.5 match 5.49 score 31 scriptsegarpor
DirStats:Nonparametric Methods for Directional Data
Nonparametric kernel density estimation, bandwidth selection, and other utilities for analyzing directional data. Implements the estimator in Bai, Rao and Zhao (1987) <doi:10.1016/0047-259X(88)90113-3>, the cross-validation bandwidth selectors in Hall, Watson and Cabrera (1987) <doi:10.1093/biomet/74.4.751> and the plug-in bandwidth selectors in García-Portugués (2013) <doi:10.1214/13-ejs821>.
Maintained by Eduardo García-Portugués. Last updated 2 years ago.
directional-statisticsnonparametric-statisticsstatisticsfortran
4.5 match 12 stars 4.26 score 7 scripts 1 dependents
jonasmoss
kdensity:Kernel Density Estimation with Parametric Starts and Asymmetric Kernels
Handles univariate non-parametric density estimation with parametric starts and asymmetric kernels in a simple and flexible way. Kernel density estimation with parametric starts involves fitting a parametric density to the data before making a correction with kernel density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>. Asymmetric kernels make kernel density estimation more efficient on bounded intervals such as (0, 1) and the positive half-line. Supported asymmetric kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>, the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>. User-supplied kernels, parametric starts, and bandwidths are supported.
Maintained by Jonas Moss. Last updated 29 days ago.
asymmetric-kernelsdensity-estimationkernel-density-estimationnon-parametric
2.8 match 16 stars 6.87 score 153 scripts 1 dependentsrubenfcasal
npsp:Nonparametric Spatial Statistics
Multidimensional nonparametric spatial (spatio-temporal) geostatistics. S3 classes and methods for multidimensional: linear binning, local polynomial kernel regression (spatial trend estimation), density and variogram estimation. Nonparametric methods for simultaneous inference on both spatial trend and variogram functions (for spatial processes). Nonparametric residual kriging (spatial prediction). For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) <doi:10.1007/s00477-013-0817-8> or Castillo-Paez et al. (2019) <doi:10.1016/j.csda.2019.01.017>.
Maintained by Ruben Fernandez-Casal. Last updated 5 months ago.
geostatisticsspatial-data-analysisstatisticsfortranopenblas
3.3 match 4 stars 5.71 score 64 scripts
xinweima
lpdensity:Local Polynomial Density Estimation and Inference
Without imposing stringent distributional assumptions or shape restrictions, nonparametric estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density (and derivatives) estimator based on local polynomial regressions, documented in Cattaneo, Jansson and Ma (2022) <doi:10.18637/jss.v101.i02>: lpdensity() to construct local polynomial based density (and derivatives) estimator, and lpbwdensity() to perform data-driven bandwidth selection.
Maintained by Xinwei Ma. Last updated 6 months ago.
7.3 match 2.50 score 37 scripts 2 dependents
gdurif
plsgenomics:PLS Analyses for Genomics
Routines for PLS-based genomic analyses, implementing PLS methods for classification with microarray data and prediction of transcription factor activities from combined ChIP-chip analysis. The >=1.2-1 versions include two new classification methods for microarray data: GSIM and Ridge PLS. The >=1.3 versions includes a new classification method combining variable selection and compression in logistic regression context: logit-SPLS; and an adaptive version of the sparse PLS.
Maintained by Ghislain Durif. Last updated 1 years ago.
3.3 match 5.53 score 140 scripts 2 dependents
bblonder
hypervolume:High Dimensional Geometry, Set Operations, Projection, and Inference Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls
Estimates the shape and volume of high-dimensional datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and species distribution modeling.
Maintained by Benjamin Blonder. Last updated 2 months ago.
1.9 match 23 stars 9.69 score 211 scripts 7 dependents
mercedesconde
BwQuant:Bandwidth Selectors for Local Linear Quantile Regression
Bandwidth selectors for local linear quantile regression, including cross-validation and plug-in methods. The local linear quantile regression estimate is also implemented.
Maintained by Mercedes Conde-Amboage. Last updated 3 years ago.
12.1 match 1 stars 1.48 score 1 dependents
thie1e
cutpointr:Determine and Evaluate Optimal Cutpoints in Binary Classification Tasks
Estimate cutpoints that optimize a specified metric in binary classification tasks and validate performance using bootstrapping. Some methods for more robust cutpoint estimation are supported, e.g. a parametric method assuming normal distributions, bootstrapped cutpoints, and smoothing of the metric values per cutpoint using Generalized Additive Models. Various plotting functions are included. For an overview of the package see Thiele and Hirschfeld (2021) <doi:10.18637/jss.v098.i11>.
Maintained by Christian Thiele. Last updated 4 months ago.
bootstrappingcutpoint-optimizationroc-curvecpp
1.7 match 88 stars 10.44 score 322 scripts 1 dependents
jungmoyoon
QTE.RD:Quantile Treatment Effects under the Regression Discontinuity Design
Provides comprehensive methods for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs, incorporating covariates and implementing robust bias correction methods of Qu, Yoon, Perron (2024) <doi:10.1162/rest_a_01168>.
Maintained by Jungmo Yoon. Last updated 7 months ago.
8.8 match 2.00 score
nibortolum
GWlasso:Geographically Weighted Lasso
Performs geographically weighted Lasso regressions. Find optimal bandwidth, fit a geographically weighted lasso or ridge regression, and make predictions. These methods are specially well suited for ecological inferences. Bandwidth selection algorithm is from A. Comber and P. Harris (2018) <doi:10.1007/s10109-018-0280-7>.
Maintained by Matthieu Mulot. Last updated 4 months ago.
4.3 match 4.00 score 5 scripts
bastian-schaefer
DCSmooth:Nonparametric Regression and Bandwidth Selection for Spatial Models
Nonparametric smoothing techniques for data on a lattice and functional time series. Smoothing is done via kernel regression or local polynomial regression, a bandwidth selection procedure based on an iterative plug-in algorithm is implemented. This package allows for modeling a dependency structure of the error terms of the nonparametric regression model. Methods used in this paper are described in Feng/Schaefer (2021) <https://ideas.repec.org/p/pdn/ciepap/144.html>, Schaefer/Feng (2021) <https://ideas.repec.org/p/pdn/ciepap/143.html>.
Maintained by Bastian Schaefer. Last updated 3 years ago.
6.4 match 2.70 score 5 scripts
schnorr
starvz:R-Based Visualization Techniques for Task-Based Applications
Performance analysis workflow that combines the power of the R language (and the tidyverse realm) and many auxiliary tools to provide a consistent, flexible, extensible, fast, and versatile framework for the performance analysis of task-based applications that run on top of the StarPU runtime (with its MPI (Message Passing Interface) layer for multi-node support). Its goal is to provide a fruitful prototypical environment to conduct performance analysis hypothesis-checking for task-based applications that run on heterogeneous (multi-GPU, multi-core) multi-node HPC (High-performance computing) platforms.
Maintained by Lucas Leandro Nesi. Last updated 6 months ago.
3.5 match 13 stars 4.94 score 27 scripts
jrjthompson
kdml:Kernel Distance Metric Learning for Mixed-Type Data
Distance metrics for mixed-type data consisting of continuous, nominal, and ordinal variables. This methodology uses additive and product kernels to calculate similarity functions and metrics, and selects variables relevant to the underlying distance through bandwidth selection via maximum similarity cross-validation. These methods can be used in any distance-based algorithm, such as distance-based clustering. For further details, we refer the reader to Ghashti and Thompson (2024) <doi:10.1007/s00357-024-09493-z> for dkps() methodology, and Ghashti (2024) <doi:10.14288/1.0443975> for dkss() methodology.
Maintained by John R. J. Thompson. Last updated 1 months ago.
5.2 match 3.18 score 4 scripts
pchausse
gmm:Generalized Method of Moments and Generalized Empirical Likelihood
It is a complete suite to estimate models based on moment conditions. It includes the two step Generalized method of moments (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several methods that belong to the Generalized Empirical Likelihood family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>, Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>, and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
Maintained by Pierre Chausse. Last updated 1 years ago.
1.9 match 2 stars 8.75 score 304 scripts 65 dependentscran
md:Selecting Bandwidth for Kernel Density Estimator with Minimum Distance Method
Selects bandwidth for the kernel density estimator with minimum distance method as proposed by Devroye and Lugosi (1996). The minimum distance method directly selects the optimal kernel density estimator from countably infinite kernel density estimators and indirectly selects the optimal bandwidth. This package selects the optimal bandwidth from finite kernel density estimators.
Maintained by Genzo Kaga. Last updated 9 years ago.
8.3 match 1.95 scorecran
HQM:Superefficient Estimation of Future Conditional Hazards Based on Marker Information
Provides a nonparametric smoothed kernel estimator for the future conditional hazard rate function when time-dependent covariates are present, a bandwidth selector for the estimator's implementation and pointwise and uniform confidence bands. Methods used in the package refer to Bagkavos, Isakson, Mammen, Nielsen and Proust-Lima (2025) <doi:10.1093/biomet/asaf008>.
Maintained by Dimitrios Bagkavos. Last updated 2 months ago.
9.9 match 1.60 scorecran
rdrobust:Robust Data-Driven Statistical Inference in Regression-Discontinuity Designs
Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).
Maintained by Sebastian Calonico. Last updated 1 years ago.
3.9 match 4 stars 4.11 score 7 dependentscran
lpcde:Boundary Adaptive Local Polynomial Conditional Density Estimator
Tools for estimation and inference of conditional densities, derivatives and functions. This is the companion software for Cattaneo, Chandak, Jansson and Ma (2024) <doi:10.3150/23-BEJ1711>.
Maintained by Rajita Chandak. Last updated 1 months ago.
6.8 match 2.30 score
baddstats
spatstat.local:Extension to 'spatstat' for Local Composite Likelihood
Extension to the 'spatstat' package, enabling the user to fit point process models to point pattern data by local composite likelihood ('geographically weighted regression').
Maintained by Adrian Baddeley. Last updated 9 months ago.
spatial-analysisspatial-dataspatstat
3.3 match 4.66 score 23 scripts
wanbitching
Ake:Associated Kernel Estimations
Continuous and discrete (count or categorical) estimation of density, probability mass function (p.m.f.) and regression functions are performed using associated kernels. The cross-validation technique and the local Bayesian procedure are also implemented for bandwidth selection.
Maintained by W. E. Wansouwé. Last updated 3 years ago.
9.1 match 1.59 score 13 scripts 1 dependents
rudjer
REBayes:Empirical Bayes Estimation and Inference
Kiefer-Wolfowitz maximum likelihood estimation for mixture models and some other density estimation and regression methods based on convex optimization. See Koenker and Gu (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, <DOI:10.18637/jss.v082.i08>.
Maintained by Roger Koenker. Last updated 9 months ago.
3.7 match 3 stars 3.89 score 27 scripts 1 dependents
weng-gw
lsbs:Bandwidth Selection for Level Sets and HDR Estimation
Bandwidth selection for kernel density estimators of 2-d level sets and highest density regions. It applies a plug-in strategy to estimate the asymptotic risk function and minimize to get the optimal bandwidth matrix. See Doss and Weng (2018) <arXiv:1806.00731> for more detail.
Maintained by Guangwei Weng. Last updated 6 years ago.
7.1 match 1 stars 2.00 score 2 scripts
aqlt
publishTC:Tools to help publish the trend-cycle
This package provides several functions to facilitate the computation of trend-cycle component. In particular, the computation can be done: using the Cascade Linear Filter (CLF) Dagum, E. B., & Luati, A. (2008); using the classical Henderson symmetric filter and the surrogate Musgrave asymmetric filters; using a local Parametrization of the Musgrave asymmetric filters (Quartier-la-Tente 2024); extending the Henderson symmetric fiter and the surrogate Musgrave asymmetric filters to take into account additive outliers and level shifts (Quartier-la-Tente 2025). Confidence intervals can be computed and several plots are available.
Maintained by Alain Quartier-la-Tente. Last updated 12 days ago.
5.6 match 2.54 score
cran
PCObw:Bandwidth Selector with Penalized Comparison to Overfitting Criterion
Bandwidth selector according to the Penalised Comparison to Overfitting (P.C.O.) criterion as described in Varet, S., Lacour, C., Massart, P., Rivoirard, V., (2019) <https://hal.archives-ouvertes.fr/hal-02002275>. It can be used with univariate and multivariate data.
Maintained by S. Varet. Last updated 2 years ago.
5.2 match 2.70 score
romainazais
cvmgof:Cramer-von Mises Goodness-of-Fit Tests
It is devoted to Cramer-von Mises goodness-of-fit tests. It implements three statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.
Maintained by Romain Azais. Last updated 4 years ago.
10.9 match 1.28 score 19 scriptscran
mgwrsar:GWR, Mixed GWR and Multiscale GWR with Spatial Autocorrelation
Functions for computing (Mixed and Multiscale) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.
Maintained by Ghislain Geniaux. Last updated 1 months ago.
3.9 match 7 stars 3.54 score
jmbh
mgm:Estimating Time-Varying k-Order Mixed Graphical Models
Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.
Maintained by Jonas Haslbeck. Last updated 21 days ago.
1.7 match 29 stars 8.16 score 125 scripts 6 dependents
osavchuk
ICV:Indirect Cross-Validation (ICV) for Kernel Density Estimation
Functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.
Maintained by Olga Savchuk. Last updated 8 years ago.
6.1 match 2.24 score 43 scriptsbioc
netprioR:A model for network-based prioritisation of genes
A model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts.
Maintained by Fabian Schmich. Last updated 5 months ago.
immunooncologycellbasedassayspreprocessingnetwork
3.3 match 4.00 score 1 scripts
mikemeredith
overlap:Estimates of Coefficient of Overlapping for Animal Activity Patterns
Provides functions to fit kernel density functions to data on temporal activity patterns of animals; estimate coefficients of overlapping of densities for two species; and calculate bootstrap estimates of confidence intervals.
Maintained by Liz Campbell. Last updated 2 years ago.
2.0 match 2 stars 6.38 score 265 scripts 1 dependents
arpapiemonte
OpeNoise:Environmental Noise Pollution Data Analysis
Provides analyse, interpret and understand noise pollution data. Data are typically regular time series measured with sound meter. The package is partially described in Fogola, Grasso, Masera and Scordino (2023, <DOI:10.61782/fa.2023.0063>).
Maintained by Pasquale Scordino. Last updated 4 months ago.
3.6 match 2 stars 3.48 score 5 scriptscran
fmri:Analysis of fMRI Experiments
Contains R-functions to perform an fMRI analysis as described in Polzehl and Tabelow (2019) <DOI:10.1007/978-3-030-29184-6>, Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>, Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>, Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.
Maintained by Karsten Tabelow. Last updated 8 months ago.
3.6 match 2 stars 3.48 score 1 dependents
hugogogo
varband:Variable Banding of Large Precision Matrices
Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.
Maintained by Guo Yu. Last updated 7 years ago.
3.1 match 2 stars 4.00 score 10 scripts
iullibarri
npcure:Nonparametric Estimation in Mixture Cure Models
Performs nonparametric estimation in mixture cure models, and significance tests for the cure probability. For details, see López-Cheda et al. (2017a) <doi:10.1016/j.csda.2016.08.002> and López-Cheda et al. (2017b) <doi:10.1007/s11749-016-0515-1>.
Maintained by Ignacio López-de-Ullibarri. Last updated 5 years ago.
6.8 match 1.78 score 1 scripts 2 dependents
santagos
dad:Three-Way / Multigroup Data Analysis Through Densities
The data consist of a set of variables measured on several groups of individuals. To each group is associated an estimated probability density function. The package provides tools to create or manage such data and functional methods (principal component analysis, multidimensional scaling, cluster analysis, discriminant analysis...) for such probability densities.
Maintained by Pierre Santagostini. Last updated 4 months ago.
2.3 match 5.32 score 92 scripts
16cile
locpolExpectile:Local Polynomial Expectile Regression
Provides the local polynomial expectile regression method and different bandwidth selection procedures. The codes include local polynomial univariate expectile regression with several data-driven methods for bandwidth selection; local linear bivariate and trivariate expectile regression; and partially linear expectile regression, allowing for different errors structures (homoscedastic error and various heteroscedastic error structures). For more details, see Adam and Gijbels (2021a) <doi:10.1007/s10463-021-00799-y> and Adam and Gijbels (2021b) <doi:10.1007/978-3-030-73249-3_8>.
Maintained by Cécile Adam. Last updated 4 years ago.
12.0 match 1.00 score 4 scripts
kimhendrickx
curstatCI:Confidence Intervals for the Current Status Model
Computes the maximum likelihood estimator, the smoothed maximum likelihood estimator and pointwise bootstrap confidence intervals for the distribution function under current status data. Groeneboom and Hendrickx (2017) <doi:10.1214/17-EJS1345>.
Maintained by Kim Hendrickx. Last updated 7 years ago.
3.0 match 3.98 score 19 scripts
sevvandi
lookout:Leave One Out Kernel Density Estimates for Outlier Detection
Outlier detection using leave-one-out kernel density estimates and extreme value theory. The bandwidth for kernel density estimates is computed using persistent homology, a technique in topological data analysis. Using peak-over-threshold method, a generalized Pareto distribution is fitted to the log of leave-one-out kde values to identify outliers.
Maintained by Sevvandi Kandanaarachchi. Last updated 12 months ago.
2.4 match 28 stars 4.92 score 9 scripts 2 dependentscran
lctools:Local Correlation, Spatial Inequalities, Geographically Weighted Regression and Other Tools
Provides researchers and educators with easy-to-learn user friendly tools for calculating key spatial statistics and to apply simple as well as advanced methods of spatial analysis in real data. These include: Local Pearson and Geographically Weighted Pearson Correlation Coefficients, Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Global and Local Moran's I), several Geographically Weighted Regression techniques and other Spatial Analysis tools (other geographically weighted statistics). This package also contains functions for measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Maintained by Stamatis Kalogirou. Last updated 1 years ago.
5.1 match 1 stars 2.30 scorecran
iosmooth:Functions for Smoothing with Infinite Order Flat-Top Kernels
Density, spectral density, and regression estimation using infinite order flat-top kernels.
Maintained by Timothy L. McMurry. Last updated 8 years ago.
11.3 match 1.00 score
ips-lmu
wrassp:Interface to the 'ASSP' Library
A wrapper around Michel Scheffers's 'libassp' (<https://libassp.sourceforge.net/>). The 'libassp' (Advanced Speech Signal Processor) library aims at providing functionality for handling speech signal files in most common audio formats and for performing analyses common in phonetic science/speech science. This includes the calculation of formants, fundamental frequency, root mean square, auto correlation, a variety of spectral analyses, zero crossing rate, filtering etc. This wrapper provides R with a large subset of 'libassp's signal processing functions and provides them to the user in a (hopefully) user-friendly manner.
Maintained by Markus Jochim. Last updated 1 years ago.
1.5 match 24 stars 7.43 score 62 scripts 3 dependents
alexisvdb
singleCellHaystack:A Universal Differential Expression Prediction Tool for Single-Cell and Spatial Genomics Data
One key exploratory analysis step in single-cell genomics data analysis is the prediction of features with different activity levels. For example, we want to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, spatial DEGs in spatial transcriptomics data, or differentially accessible regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially active features in single cell omics datasets without relying on the clustering of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler divergence to find features (e.g., genes, genomic regions, etc) that are active in subsets of cells that are non-randomly positioned inside an input space (such as 1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For the theoretical background of 'singleCellHaystack' we refer to our original paper Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3> and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.
Maintained by Alexis Vandenbon. Last updated 1 years ago.
bioinformaticscite-seqpseudotimescatac-seqsingle-cellspatial-proteomicsspatial-transcriptomicstranscriptomics
1.7 match 81 stars 6.71 score 64 scripts
mlysy
LocalCop:Local Likelihood Inference for Conditional Copula Models
Implements a local likelihood estimator for the dependence parameter in bivariate conditional copula models. Copula family and local likelihood bandwidth parameters are selected by leave-one-out cross-validation. The models are implemented in 'TMB', meaning that the local score function is efficiently calculated via automated differentiation (AD), such that quasi-Newton algorithms may be used for parameter estimation.
Maintained by Martin Lysy. Last updated 6 months ago.
2.3 match 1 stars 4.78 score 9 scripts
seananderson
metafolio:Metapopulation Simulations for Conserving Salmon Through Portfolio Optimization
A tool to simulate salmon metapopulations and apply financial portfolio optimization concepts. The package accompanies the paper Anderson et al. (2015) <doi:10.1101/2022.03.24.485545>.
Maintained by Sean C. Anderson. Last updated 1 years ago.
2.3 match 2 stars 4.89 score 77 scripts
ajdamico
convey:Income Concentration Analysis with Complex Survey Samples
Variance estimation on indicators of income concentration and poverty using complex sample survey designs. Wrapper around the 'survey' package.
Maintained by Anthony Damico. Last updated 6 months ago.
1.7 match 19 stars 6.09 score 201 scripts
jackdunnnz
iai:Interface to 'Interpretable AI' Modules
An interface to the algorithms of 'Interpretable AI' <https://www.interpretable.ai> from the R programming language. 'Interpretable AI' provides various modules, including 'Optimal Trees' for classification, regression, prescription and survival analysis, 'Optimal Imputation' for missing data imputation and outlier detection, and 'Optimal Feature Selection' for exact sparse regression. The 'iai' package is an open-source project. The 'Interpretable AI' software modules are proprietary products, but free academic and evaluation licenses are available.
Maintained by Jack Dunn. Last updated 6 months ago.
5.1 match 1 stars 2.00 score 7 scripts
ustervbo
beadplexr:Analysis of Multiplex Cytometric Bead Assays
Reproducible and automated analysis of multiplex bead assays such as CBA (Morgan et al. 2004; <doi: 10.1016/j.clim.2003.11.017>), LEGENDplex (Yu et al. 2015; <doi: 10.1084/jem.20142318>), and MACSPlex (Miltenyi Biotec 2014; Application note: Data acquisition and analysis without the MACSQuant analyzer; <https://www.miltenyibiotec.com/upload/assets/IM0021608.PDF>). The package provides functions for streamlined reading of fcs files, and identification of bead clusters and analyte expression. The package eases the calculation of standard curves and the subsequent calculation of the analyte concentration.
Maintained by Ulrik Stervbo. Last updated 2 years ago.
2.0 match 5.07 score 39 scriptsrobjhyndman
weird:Functions and Data Sets for "That's Weird: Anomaly Detection Using R" by Rob J Hyndman
All functions and data sets required for the examples in the book Hyndman (2024) "That's Weird: Anomaly Detection Using R" <https://OTexts.com/weird/>. All packages needed to run the examples are also loaded.
Maintained by Rob Hyndman. Last updated 3 months ago.
1.8 match 17 stars 5.74 score 18 scripts
marco-geraci
lqmm:Linear Quantile Mixed Models
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.
Maintained by Marco Geraci. Last updated 3 years ago.
2.3 match 4.38 score 75 scripts 5 dependentscran
circular:Circular Statistics
Circular Statistics, from "Topics in circular Statistics" (2001) S. Rao Jammalamadaka and A. SenGupta, World Scientific.
Maintained by Eduardo García-Portugués. Last updated 7 months ago.
1.7 match 7 stars 5.71 score 40 dependents
ncordon
imbalance:Preprocessing Algorithms for Imbalanced Datasets
Class imbalance usually damages the performance of classifiers. Thus, it is important to treat data before applying a classifier algorithm. This package includes recent resampling algorithms in the literature: (Barua et al. 2014) <doi:10.1109/tkde.2012.232>; (Das et al. 2015) <doi:10.1109/tkde.2014.2324567>, (Zhang et al. 2014) <doi:10.1016/j.inffus.2013.12.003>; (Gao et al. 2014) <doi:10.1016/j.neucom.2014.02.006>; (Almogahed et al. 2014) <doi:10.1007/s00500-014-1484-5>. It also includes an useful interface to perform oversampling.
Maintained by Ignacio Cordón. Last updated 5 years ago.
binary-classificationimbalanced-dataoversamplingopenblascpp
1.3 match 36 stars 7.18 score 98 scripts
lbelzile
mig:Multivariate Inverse Gaussian Distribution
Provides utilities for estimation for the multivariate inverse Gaussian distribution of Minami (2003) <doi:10.1081/STA-120025379>, including random vector generation and explicit estimators of the location vector and scale matrix. The package implements kernel density estimators discussed in Belzile, Desgagnes, Genest and Ouimet (2024) <doi:10.48550/arXiv.2209.04757> for smoothing multivariate data on half-spaces.
Maintained by Leo Belzile. Last updated 1 months ago.
2.0 match 4.65 score 1 scripts
elianachristou
quantdr:Dimension Reduction Techniques for Conditional Quantiles
An implementation of dimension reduction techniques for conditional quantiles. Nonparametric estimation of conditional quantiles is also available.
Maintained by Eliana Christou. Last updated 3 years ago.
1.7 match 3 stars 5.30 score 11 scripts 4 dependents
overton-group
eHDPrep:Quality Control and Semantic Enrichment of Datasets
A tool for the preparation and enrichment of health datasets for analysis (Toner et al. (2023) <doi:10.1093/gigascience/giad030>). Provides functionality for assessing data quality and for improving the reliability and machine interpretability of a dataset. 'eHDPrep' also enables semantic enrichment of a dataset where metavariables are discovered from the relationships between input variables determined from user-provided ontologies.
Maintained by Ian Overton. Last updated 2 years ago.
data-qualityhealth-informaticssemantic-enrichment
1.8 match 8 stars 4.90 score 10 scripts
kolesarm
RDHonest:Honest Inference in Regression Discontinuity Designs
Honest and nearly-optimal confidence intervals in fuzzy and sharp regression discontinuity designs and for inference at a point based on local linear regression. The implementation is based on Armstrong and Kolesár (2018) <doi:10.3982/ECTA14434>, and Kolesár and Rothe (2018) <doi:10.1257/aer.20160945>. Supports covariates, clustering, and weighting.
Maintained by Michal Kolesár. Last updated 3 months ago.
confidence-intervalsregression-discontinuity-designs
1.3 match 60 stars 6.56 score 20 scripts
thomasfillon
kernopt:Estimating Count Data Distributions with Discrete Optimal Symmetric Kernel
Implementation of Discrete Symmetric Optimal Kernel for estimating count data distributions, as described by T. Senga Kiessé and G. Durrieu (2024) <doi:10.1016/j.spl.2024.110078>.The nonparametric estimator using the discrete symmetric optimal kernel was illustrated on simulated data sets and a real-word data set included in the package, in comparison with two other discrete symmetric kernels.
Maintained by Thomas Fillon. Last updated 13 days ago.
1.7 match 5.18 score
ddimmery
rdd:Regression Discontinuity Estimation
Provides the tools to undertake estimation in Regression Discontinuity Designs. Both sharp and fuzzy designs are supported. Estimation is accomplished using local linear regression. A provided function will utilize Imbens-Kalyanaraman optimal bandwidth calculation. A function is also included to test the assumption of no-sorting effects.
Maintained by Drew Dimmery. Last updated 9 years ago.
2.3 match 3.77 score 224 scripts 1 dependents
marcusrowcliffe
activity:Animal Activity Statistics
Provides functions to express clock time data relative to anchor points (typically solar); fit kernel density functions to animal activity time data; plot activity distributions; quantify overall levels of activity; statistically compare activity metrics through bootstrapping; evaluate variation in linear variables with time (or other circular variables).
Maintained by Marcus Rowcliffe. Last updated 2 years ago.
1.9 match 1 stars 4.45 score 190 scripts 1 dependents
dcoffman
tvmediation:Time Varying Mediation Analysis
Provides functions for estimating mediation effects that vary over time as described in Cai X, Coffman DL, Piper ME, Li R. Estimation and inference for the mediation effect in a time-varying mediation model. BMC Med Res Methodol. 2022;22(1):1-12.
Maintained by Donna Coffman. Last updated 3 years ago.
1.7 match 3 stars 4.95 score 4 scriptsspatstat
spatstat:Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Maintained by Adrian Baddeley. Last updated 11 days ago.
cluster-processcox-point-processgibbs-processkernel-densitynetwork-analysispoint-processpoisson-processspatial-analysisspatial-dataspatial-data-analysisspatial-statisticsspatstatstatistical-methodsstatistical-modelsstatistical-testsstatistics
0.5 match 200 stars 16.25 score 5.5k scripts 40 dependentscran
nprobust:Nonparametric Robust Estimation and Inference Methods using Local Polynomial Regression and Kernel Density Estimation
Tools for data-driven statistical analysis using local polynomial regression and kernel density estimation methods as described in Calonico, Cattaneo and Farrell (2018, <doi:10.1080/01621459.2017.1285776>): lprobust() for local polynomial point estimation and robust bias-corrected inference, lpbwselect() for local polynomial bandwidth selection, kdrobust() for kernel density point estimation and robust bias-corrected inference, kdbwselect() for kernel density bandwidth selection, and nprobust.plot() for plotting results. The main methodological and numerical features of this package are described in Calonico, Cattaneo and Farrell (2019, <doi:10.18637/jss.v091.i08>).
Maintained by Sebastian Calonico. Last updated 5 years ago.
4.2 match 1 stars 1.95 score 3 dependents
kzychaluk
modelfree:Model-Free Estimation of a Psychometric Function
Local linear estimation of psychometric functions. Provides functions for nonparametric estimation of a psychometric function and for estimation of a derived threshold and slope, and their standard deviations and confidence intervals.
Maintained by Kamila Zychaluk. Last updated 2 years ago.
5.1 match 1.58 score 38 scripts
kisungyou
CovTools:Statistical Tools for Covariance Analysis
Covariance is of universal prevalence across various disciplines within statistics. We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) <doi:10.1214/ss/1177013111>.
Maintained by Kisung You. Last updated 2 years ago.
covariancecovariance-estimationopenblascpp
1.7 match 14 stars 4.59 score 55 scripts
alexisderumigny
CondCopulas:Estimation and Inference for Conditional Copula Models
Provides functions for the estimation of conditional copulas models, various estimators of conditional Kendall's tau (proposed in Derumigny and Fermanian (2019a, 2019b, 2020) <doi:10.1515/demo-2019-0016>, <doi:10.1016/j.csda.2019.01.013>, <doi:10.1016/j.jmva.2020.104610>), and test procedures for the simplifying assumption (proposed in Derumigny and Fermanian (2017) <doi:10.1515/demo-2017-0011> and Derumigny, Fermanian and Min (2022) <doi:10.1002/cjs.11742>).
Maintained by Alexis Derumigny. Last updated 7 months ago.
conditional-copulasconditional-kendalls-taucopulasr-pkgsimplifying-assumption
1.7 match 2 stars 4.70 score 7 scripts
tkmckenzie
snfa:Smooth Non-Parametric Frontier Analysis
Fitting of non-parametric production frontiers for use in efficiency analysis. Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for multiple inputs and a single output using constrained kernel smoothing as in Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints on the estimated frontier.
Maintained by Taylor McKenzie. Last updated 5 years ago.
2.0 match 3.70 score 8 scriptscran
PLRModels:Statistical Inference in Partial Linear Regression Models
Contains statistical inference tools applied to Partial Linear Regression (PLR) models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of covariance are considered. Kernel-based methods, combined with ordinary least squares estimation, are used and time series errors are allowed. In addition, these techniques are also implemented for both parametric (linear) and nonparametric regression models.
Maintained by Ana Lopez-Cheda. Last updated 2 years ago.
7.4 match 1.00 scorealexisderumigny
ElliptCopulas:Inference of Elliptical Distributions and Copulas
Provides functions for the simulation and the nonparametric estimation of elliptical distributions, meta-elliptical copulas and trans-elliptical distributions, following the article Derumigny and Fermanian (2022) <doi:10.1016/j.jmva.2022.104962>.
Maintained by Alexis Derumigny. Last updated 7 months ago.
copulaelliptical-copulaelliptical-distributionr-pkg
1.7 match 4 stars 4.38 score 2 scripts 1 dependentsr-forge
plugdensity:Plug-in Kernel Density Estimation
Kernel density estimation with global bandwidth selection via "plug-in".
Maintained by Martin Maechler. Last updated 4 months ago.
2.3 match 3.15 score 4 scripts
haeran-cho
mosum:Moving Sum Based Procedures for Changes in the Mean
Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) <doi:10.3150/16-BEJ887> and the multiscale algorithmic extension from Cho and Kirch (2022) <doi:10.1007/s10463-021-00811-5>, as well as the bootstrap procedure for generating confidence intervals about the locations of change points as proposed in Cho and Kirch (2022) <doi:10.1016/j.csda.2022.107552>. See also Meier, Kirch and Cho (2021) <doi:10.18637/jss.v097.i08> which accompanies the R package.
Maintained by Haeran Cho. Last updated 2 years ago.
4.0 match 2 stars 1.78 score 9 scripts 1 dependentsxinweima
rddensity:Manipulation Testing Based on Density Discontinuity
Density discontinuity testing (a.k.a. manipulation testing) is commonly employed in regression discontinuity designs and other program evaluation settings to detect perfect self-selection (manipulation) around a cutoff where treatment/policy assignment changes. This package implements manipulation testing procedures using the local polynomial density estimators: rddensity() to construct test statistics and p-values given a prespecified cutoff, rdbwdensity() to perform data-driven bandwidth selection, and rdplotdensity() to construct density plots.
Maintained by Xinwei Ma. Last updated 6 months ago.
2.4 match 2.97 score 206 scripts 1 dependentscran
npbr:Nonparametric Boundary Regression
A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods. Its use is illustrated via a number of empirical applications and simulated examples.
Maintained by Thibault Laurent. Last updated 2 years ago.
3.5 match 1 stars 2.00 score
harveyklyne
drape:Doubly Robust Average Partial Effects
Doubly robust average partial effect estimation. This implementation contains methods for adding additional smoothness to plug-in regression procedures and for estimating score functions using smoothing splines. Details of the method can be found in Harvey Klyne and Rajen D. Shah (2023) <doi:10.48550/arXiv.2308.09207>.
Maintained by Harvey Klyne. Last updated 4 months ago.
1.8 match 2 stars 4.00 score 4 scriptscran
MSinference:Multiscale Inference for Nonparametric Time Trend(s)
Performs a multiscale analysis of a nonparametric regression or nonparametric regressions with time series errors. In case of one regression, with the help of this package it is possible to detect the regions where the trend function is increasing or decreasing. In case of multiple regressions, the test identifies regions where the trend functions are different from each other. See Khismatullina and Vogt (2020) <doi:10.1111/rssb.12347>, Khismatullina and Vogt (2022) <doi:10.48550/arXiv.2209.10841> and Khismatullina and Vogt (2023) <doi:10.1016/j.jeconom.2021.04.010> for more details on theory and applications.
Maintained by Marina Khismatullina. Last updated 7 months ago.
3.5 match 2.00 score
andrewthomasjones
logKDE:Computing Log-Transformed Kernel Density Estimates for Positive Data
Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.
Maintained by Andrew Thomas Jones. Last updated 7 years ago.
1.8 match 1 stars 3.78 score 12 scriptsosavchuk
OSCV:One-Sided Cross-Validation
Functions for implementing different versions of the OSCV method in the kernel regression and density estimation frameworks. The package mainly supports the following articles: (1) Savchuk, O.Y., Hart, J.D. (2017). Fully robust one-sided cross-validation for regression functions. Computational Statistics, <doi:10.1007/s00180-017-0713-7> and (2) Savchuk, O.Y. (2017). One-sided cross-validation for nonsmooth density functions, <arXiv:1703.05157>.
Maintained by Olga Savchuk. Last updated 8 years ago.
5.5 match 1.23 score 17 scripts
wdy9
sharpPen:Penalized Data Sharpening for Local Polynomial Regression
Functions and data sets for data sharpening. Nonparametric regressions are computed subject to smoothness and other kinds of penalties.
Maintained by D. Wang. Last updated 2 months ago.
6.6 match 1.00 score
pierre-andre
ibr:Iterative Bias Reduction
Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.
Maintained by "Pierre-Andre Cornillon". Last updated 2 years ago.
5.1 match 1.28 score 19 scripts
barnhilldave
TML:Tropical Geometry Tools for Machine Learning
Suite of tropical geometric tools for use in machine learning applications. These methods may be summarized in the following references: Yoshida, et al. (2022) <arxiv:2209.15045>, Barnhill et al. (2023) <arxiv:2303.02539>, Barnhill and Yoshida (2023) <doi:10.3390/math11153433>, Aliatimis et al. (2023) <arXiv:2306.08796>, Yoshida et al. (2022) <arXiv:2206.04206>, and Yoshida et al. (2019) <doi:10.1007/s11538-018-0493-4>.
Maintained by David Barnhill. Last updated 8 months ago.
1.9 match 3 stars 3.48 score 1 scripts
jose-ameijeiras
multimode:Mode Testing and Exploring
Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.
Maintained by Jose Ameijeiras-Alonso. Last updated 4 years ago.
2.3 match 1 stars 2.85 score 76 scripts 2 dependents
cran
NPHazardRate:Nonparametric Hazard Rate Estimation
Provides functions and examples for histogram, kernel (classical, variable bandwidth and transformations based), discrete and semiparametric hazard rate estimators.
Maintained by Dimitrios Bagkavos. Last updated 6 years ago.
6.2 match 1.00 scorecran
evmix:Extreme Value Mixture Modelling, Threshold Estimation and Boundary Corrected Kernel Density Estimation
The usual distribution functions, maximum likelihood inference and model diagnostics for univariate stationary extreme value mixture models are provided. Kernel density estimation including various boundary corrected kernel density estimation methods and a wide choice of kernels, with cross-validation likelihood based bandwidth estimator. Reasonable consistency with the base functions in the 'evd' package is provided, so that users can safely interchange most code.
Maintained by Carl Scarrott. Last updated 6 years ago.
2.2 match 2 stars 2.85 score 7 dependents
italo-granato
snpReady:Preparing Genotypic Datasets in Order to Run Genomic Analysis
Three functions to clean, summarize and prepare genomic datasets to Genome Selection and Genome Association analysis and to estimate population genetic parameters.
Maintained by Italo Granato. Last updated 5 years ago.
1.0 match 4 stars 5.90 score 33 scripts
hailyee-ha
SSIMmap:The structural similarity index measure (SSIM) for maps
The SSIMmap package extend 'the classical SSIM method <https://doi.org/10.1109/TIP.2003.819861> for irregular lattice-based maps and raster images. The SSIMmap package applies this method to two types of maps (polygon and raster). The geographical SSIM method incorporates well-developed 'geographically weighted summary statistics' <https://doi.org/10.1016/S0198-9715(01)00009-6> with an adaptive bandwidth kernel function for irregular lattice-based maps.
Maintained by Hui Jeong (Hailyee) Ha. Last updated 2 years ago.
2.2 match 1 stars 2.70 score 10 scripts
spatlyu
tidyrgeoda:A tidy interface for rgeoda
An interface for 'rgeoda' to integrate with 'sf' objects and the 'tidyverse'.
Maintained by Wenbo Lv. Last updated 8 months ago.
geocomputationgeoinformaticsgisciencespatial-analysisspatial-statistics
1.2 match 16 stars 5.11 score 5 scripts
abshev
superMICE:SuperLearner Method for MICE
Adds a Super Learner ensemble model method (using the 'SuperLearner' package) to the 'mice' package. Laqueur, H. S., Shev, A. B., Kagawa, R. M. C. (2021) <doi:10.1093/aje/kwab271>.
Maintained by Aaron B. Shev. Last updated 3 years ago.
1.9 match 3 stars 3.18 score
davidhofmeyr
FKSUM:Fast Kernel Sums
Implements the method of Hofmeyr, D.P. (2021) <DOI:10.1109/TPAMI.2019.2930501> for fast evaluation of univariate kernel smoothers based on recursive computations. Applications to the basic problems of density and regression function estimation are provided, as well as some projection pursuit methods for which the objective is based on non-parametric functionals of the projected density, or conditional density of a response given projected covariates. The package is accompanied by an instructive paper in the Journal of Statistical Software <doi:10.18637/jss.v101.i03>.
Maintained by David P. Hofmeyr. Last updated 2 years ago.
4.0 match 1.48 score 2 scripts 1 dependents
twolodzko
kernelboot:Smoothed Bootstrap and Random Generation from Kernel Densities
Smoothed bootstrap and functions for random generation from univariate and multivariate kernel densities. It does not estimate kernel densities.
Maintained by Tymoteusz Wolodzko. Last updated 2 years ago.
bootstrapdensitykernel-densityrandom-generationsimulationcpp
1.8 match 3 stars 3.35 score 15 scripts
floo66
cylcop:Circular-Linear Copulas with Angular Symmetry for Movement Data
Classes (S4) of circular-linear, symmetric copulas with corresponding methods, extending the 'copula' package. These copulas are especially useful for modeling correlation in discrete-time movement data. Methods for density, (conditional) distribution, random number generation, bivariate dependence measures and fitting parameters using maximum likelihood and other approaches. The package also contains methods for visualizing movement data and copulas.
Maintained by Florian Hodel. Last updated 2 years ago.
3.4 match 1 stars 1.70 scorecran
kequate:The Kernel Method of Test Equating
Implements the kernel method of test equating as defined in von Davier, A. A., Holland, P. W. and Thayer, D. T. (2004) <doi:10.1007/b97446> and Andersson, B. and Wiberg, M. (2017) <doi:10.1007/s11336-016-9528-7> using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting Gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Maintained by Björn Andersson. Last updated 3 years ago.
2.0 match 2 stars 2.90 score
cyrillsch
IVDML:Double Machine Learning with Instrumental Variables and Heterogeneity
Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" <doi:10.48550/arXiv.2503.03530>.
Maintained by Cyrill Scheidegger. Last updated 22 days ago.
1.8 match 1 stars 3.18 score
mavpanos
bunching:Estimate Bunching
Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.
Maintained by Panos Mavrokonstantis. Last updated 2 years ago.
1.2 match 5 stars 4.70 score 5 scripts
nparamestimcurves
quantCurves:Estimate Quantiles Curves
Non-parametric methods as local normal regression, polynomial local regression and penalized cubic B-splines regression are used to estimate quantiles curves. See Fan and Gijbels (1996) <doi:10.1201/9780203748725> and Perperoglou et al.(2019) <doi:10.1186/s12874-019-0666-3>.
Maintained by Sandie Ferrigno. Last updated 3 years ago.
5.3 match 1.00 scorecran
cenROC:Estimating Time-Dependent ROC Curve and AUC for Censored Data
Contains functions to estimate a smoothed and a non-smoothed (empirical) time-dependent receiver operating characteristic curve and the corresponding area under the receiver operating characteristic curve and the optimal cutoff point for the right and interval censored survival data. See Beyene and El Ghouch (2020)<doi:10.1002/sim.8671> and Beyene and El Ghouch (2022) <doi:10.1002/bimj.202000382>.
Maintained by Kassu Mehari Beyene. Last updated 2 years ago.
5.3 match 1.00 score
youyifong
mdw:Maximum Diversity Weighting
Dimension-reduction methods aim at defining a score that maximizes signal diversity. Three approaches, tree weight, maximum entropy weights, and maximum variance weights are provided. These methods are described in He and Fong (2019) <DOI:10.1002/sim.8212>.
Maintained by Youyi Fong. Last updated 8 months ago.
2.3 match 2.34 score 22 scripts
fvidoli
Compind:Composite Indicators Functions
A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.
Maintained by Francesco Vidoli. Last updated 3 months ago.
1.8 match 1 stars 2.90 score 40 scripts
erlisr
robustBLME:Robust Bayesian Linear Mixed-Effects Models using ABC
Bayesian robust fitting of linear mixed effects models though weighted likelihood equations and approximate Bayesian computation.
Maintained by Erlis Ruli. Last updated 8 years ago.
1.9 match 1 stars 2.70 score 2 scriptscran
SpatialML:Spatial Machine Learning
Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>). Allows for a geographically weighted random forest regression including a function to find the optical bandwidth. (Georganos and Kalogirou (2022) <https://www.mdpi.com/2220-9964/11/9/471>).
Maintained by Stamatis Kalogirou. Last updated 1 years ago.
2.2 match 8 stars 2.20 score
bruceqd
PLSiMCpp:Methods for Partial Linear Single Index Model
Estimation, hypothesis tests, and variable selection in partially linear single-index models. Please see H. (2010) at <doi:10.1214/10-AOS835> for more details.
Maintained by Shunyao Wu. Last updated 3 years ago.
2.3 match 3 stars 2.18 score 1 scripts
dsjohnson
crawlUtils:Enhance And Integrate the {crawl} Package For Spatial Analysis Of Telemetry Output
Utility functions to augment the the {crawl} package and integrate it with the {sf} package for spatial analysis of telemetry model output. The additional function are targeted toward analysis of marine mammal telemetry, but can be used or easily modified for other situations.
Maintained by Devin S. Johnson. Last updated 7 months ago.
1.9 match 2 stars 2.60 score 1 scripts
christopherstrothmann
RDM:Quantify Dependence using Rearranged Dependence Measures
Estimates the rearranged dependence measure ('RDM') of two continuous random variables for different underlying measures. Furthermore, it provides a method to estimate the (SI)-rearrangement copula using empirical checkerboard copulas. It is based on the theoretical results presented in Strothmann et al. (2022) <arXiv:2201.03329> and Strothmann (2021) <doi:10.17877/DE290R-22733>.
Maintained by Christopher Strothmann. Last updated 2 years ago.
1.8 match 2.70 score 3 scriptscran
astrochron:A Computational Tool for Astrochronology
Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.
Maintained by Stephen Meyers. Last updated 7 months ago.
1.8 match 5 stars 2.70 scorecran
gplm:Generalized Partial Linear Models (GPLM)
Provides functions for estimating a generalized partial linear model, a semiparametric variant of the generalized linear model (GLM) which replaces the linear predictor by the sum of a linear and a nonparametric function.
Maintained by Marlene Mueller. Last updated 9 years ago.
2.3 match 2.00 scorecran
SCBmeanfd:Simultaneous Confidence Bands for the Mean of Functional Data
Statistical methods for estimating and inferring the mean of functional data. The methods include simultaneous confidence bands, local polynomial fitting, bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.
Maintained by David Degras. Last updated 8 years ago.
4.0 match 1 stars 1.00 scorecran
KFPCA:Kendall Functional Principal Component Analysis
Implementation for Kendall functional principal component analysis. Kendall functional principal component analysis is a robust functional principal component analysis technique for non-Gaussian functional/longitudinal data. The crucial function of this package is KFPCA() and KFPCA_reg(). Moreover, least square estimates of functional principal component scores are also provided. Refer to Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <arXiv:2102.01286>. Rou Zhong, Shishi Liu, Haocheng Li, Jingxiao Zhang. (2021) <doi:10.1016/j.jmva.2021.104864>.
Maintained by Rou Zhong. Last updated 3 years ago.
3.5 match 1.00 scorecran
FastBandChol:Fast Estimation of a Covariance Matrix by Banding the Cholesky Factor
Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
Maintained by Aaron Molstad. Last updated 10 years ago.
3.5 match 1.00 score
laylaparast
landpred:Landmark Prediction of a Survival Outcome
Provides functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307, <doi:10.1002/bimj.201000150>.
Maintained by Layla Parast. Last updated 2 years ago.
1.9 match 1.82 score 11 scripts 2 dependentscran
L2DensityGoFtest:Density Goodness-of-Fit Test
Provides functions for the implementation of a density goodness-of-fit test, based on piecewise approximation of the L2 distance.
Maintained by Dimitrios Bagkavos. Last updated 2 years ago.
3.3 match 1.00 scorecran
locits:Test of Stationarity and Localized Autocovariance
Provides test of second-order stationarity for time series (for dyadic and arbitrary-n length data). Provides localized autocovariance, with confidence intervals, for locally stationary (nonstationary) time series. See Nason, G P (2013) "A test for second-order stationarity and approximate confidence intervals for localized autocovariance for locally stationary time series." Journal of the Royal Statistical Society, Series B, 75, 879-904. <doi:10.1111/rssb.12015>.
Maintained by Guy Nason. Last updated 2 years ago.
1.7 match 1 stars 1.95 score 3 dependentscran
mscp:Multiscale Change Point Detection via Gradual Bandwidth Adjustment in Moving Sum Processes
Multiscale moving sum procedure for the detection of changes in expectation in univariate sequences. References - Multiscale change point detection via gradual bandwidth adjustment in moving sum processes (2021+), Tijana Levajkovic and Michael Messer.
Maintained by Michael Messer. Last updated 4 years ago.
3.2 match 1.00 score
lorismichel
drf:Distributional Random Forests
An implementation of distributional random forests as introduced in Cevid & Michel & Meinshausen & Buhlmann (2020) <arXiv:2005.14458>.
Maintained by Loris Michel. Last updated 4 years ago.
1.8 match 1.59 score 39 scripts
dereksonderegger
SiZer:Significant Zero Crossings
Calculates and plots the SiZer map for scatterplot data. A SiZer map is a way of examining when the p-th derivative of a scatterplot-smoother is significantly negative, possibly zero or significantly positive across a range of smoothing bandwidths.
Maintained by Derek Sonderegger. Last updated 3 years ago.
0.5 match 1 stars 5.00 score 33 scripts 2 dependents
dcwheels
gwrr:Fits Geographically Weighted Regression Models with Diagnostic Tools
Fits geographically weighted regression (GWR) models and has tools to diagnose and remediate collinearity in the GWR models. Also fits geographically weighted ridge regression (GWRR) and geographically weighted lasso (GWL) models. See Wheeler (2009) <doi:10.1068/a40256> and Wheeler (2007) <doi:10.1068/a38325> for more details.
Maintained by David Wheeler. Last updated 3 years ago.
1.8 match 2 stars 1.41 score 13 scripts
adunaic
lpacf:Local Partial Autocorrelation Function Estimation for Locally Stationary Wavelet Processes
Provides the method for computing the local partial autocorrelation function for locally stationary wavelet time series from Killick, Knight, Nason, Eckley (2020) <doi:10.1214/20-EJS1748>.
Maintained by Rebecca Killick. Last updated 2 years ago.
1.7 match 1.48 score 2 scripts 1 dependents
maalam79
RKUM:Robust Kernel Unsupervised Methods
Robust kernel center matrix, robust kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A, Fukumizu, K., Wang Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>. Alam, M. A, Calhoun, C. D., Wang Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.
Maintained by Md Ashad Alam. Last updated 3 years ago.
2.0 match 1.20 score 16 scripts
resplab
cumulcalib:Cumulative Calibration Assessment for Prediction Models
Tools for visualization of, and inference on, the calibration of prediction models on the cumulative domain. This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Patkau (2024) <doi:10.1002/sim.10138>. The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The plot() and summary() methods are implemented for the results returned by cumulcalib().
Maintained by Mohsen Sadatsafavi. Last updated 9 months ago.
0.5 match 2 stars 4.60 score 8 scriptswanbitching
Conake:Continuous Associated Kernel Estimation
Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection.
Maintained by W. E. Wansouwé. Last updated 3 years ago.
2.3 match 1.00 score 6 scripts
leilamarvian
ADTSA:Time Series Analysis
Analyzes autocorrelation and partial autocorrelation using surrogate methods and bootstrapping, and computes the acceleration constants for the vectorized moving block bootstrap provided by this package. It generates percentile, bias-corrected, and accelerated intervals and estimates partial autocorrelations using Durbin-Levinson. This package calculates the autocorrelation power spectrum, computes cross-correlations between two time series, computes bandwidth for any time series, and performs autocorrelation frequency analysis. It also calculates the periodicity of a time series.
Maintained by Leila Marvian Mashhad. Last updated 1 years ago.
2.3 match 1 stars 1.00 score
jahmadkhan
AsyK:Kernel Density Estimation
A collection of functions related to density estimation by using Chen's (2000) idea. Mean Squared Errors (MSE) are calculated for estimated curves. For this purpose, R functions allow the distribution to be Gamma, Exponential or Weibull. For details see Chen (2000), Scaillet (2004) <doi:10.1080/10485250310001624819> and Khan and Akbar.
Maintained by Javaria Ahmad Khan. Last updated 3 years ago.
2.3 match 1.00 score
wjbraun
sharpData:Data Sharpening
Functions and data sets inspired by data sharpening - data perturbation to achieve improved performance in nonparametric estimation, as described in Choi, E., Hall, P. and Rousson, V. (2000). Capabilities for enhanced local linear regression function and derivative estimation are included, as well as an asymptotically correct iterated data sharpening estimator for any degree of local polynomial regression estimation. A cross-validation-based bandwidth selector is included which, in concert with the iterated sharpener, will often provide superior performance, according to a median integrated squared error criterion. Sample data sets are provided to illustrate function usage.
Maintained by W.J. Braun. Last updated 4 years ago.
2.2 match 1.00 score
marcellochiodi
etasFLP:Mixed FLP and ML Estimation of ETAS Space-Time Point Processes for Earthquake Description
Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive). New version 2.0.0: covariates have been introduced to explain the effects of external factors on the induced seismicity; the parametrization has been changed; Chiodi, Adelfio (2017)<doi:10.18637/jss.v076.i03>.
Maintained by Marcello Chiodi. Last updated 2 years ago.
1.8 match 1 stars 1.20 score 16 scripts
courtiol
timevarcorr:Time Varying Correlation
Computes how the correlation between 2 time-series changes over time. To do so, the package follows the method from Choi & Shin (2021) <doi:10.1007/s42952-020-00073-6>. It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy). An automatic selection procedure for the bandwidth parameter is implemented. Alternative kernels can be used (Epanechnikov, box and normal). Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested.
Maintained by Alexandre Courtiol. Last updated 1 years ago.
0.8 match 1 stars 2.70 score 5 scripts
dschulz13
smoots:Nonparametric Estimation of the Trend and Its Derivatives in TS
The nonparametric trend and its derivatives in equidistant time series (TS) with short-memory stationary errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel smoother is also built-in as a comparison. With version 1.1.0, a linearity test for the trend function, forecasting methods and backtesting approaches are implemented as well. The smoothing methods of the package are described in Feng, Y., Gries, T., and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.
Maintained by Dominik Schulz. Last updated 2 years ago.
0.8 match 2.65 score 6 scripts 3 dependentsdschulz13
esemifar:Smoothing Long-Memory Time Series
The nonparametric trend and its derivatives in equidistant time series (TS) with long-memory errors can be estimated. The estimation is conducted via local polynomial regression using an automatically selected bandwidth obtained by a built-in iterative plug-in algorithm or a bandwidth fixed by the user. The smoothing methods of the package are described in Letmathe, S., Beran, J. and Feng, Y., (2023) <doi:10.1080/03610926.2023.2276049>.
Maintained by Dominik Schulz. Last updated 11 months ago.
0.8 match 1 stars 2.48 score 1 scripts 1 dependents
hippolyteboucher
SpeTestNP:Non-Parametric Tests of Parametric Specifications
Performs non-parametric tests of parametric specifications. Five tests are available. Specific bandwidth and kernel methods can be chosen along with many other options. Allows parallel computing to quickly compute p-values based on the bootstrap. Methods implemented in the package are H.J. Bierens (1982) <doi:10.1016/0304-4076(82)90105-1>, J.C. Escanciano (2006) <doi:10.1017/S0266466606060506>, P.L. Gozalo (1997) <doi:10.1016/S0304-4076(97)86571-2>, P. Lavergne and V. Patilea (2008) <doi:10.1016/j.jeconom.2007.08.014>, P. Lavergne and V. Patilea (2012) <doi:10.1198/jbes.2011.07152>, J.H. Stock and M.W. Watson (2006) <doi:10.1111/j.1538-4616.2007.00014.x>, C.F.J. Wu (1986) <doi:10.1214/aos/1176350142>, J. Yin, Z. Geng, R. Li, H. Wang (2010) <https://www.jstor.org/stable/24309002> and J.X. Zheng (1996) <doi:10.1016/0304-4076(95)01760-7>.
Maintained by Hippolyte Boucher. Last updated 2 years ago.
0.5 match 3.70 score 2 scripts
yqml
NPCox:Nonparametric and Semiparametric Proportional Hazards Model
An estimation procedure for the analysis of nonparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z)), providing estimation of b(t) and its pointwise standard errors, and semiparametric proportional hazards model (e.g. h(t) = h0(t)exp(b(t)'Z1 + c*Z2)), providing estimation of b(t), c and their standard errors. More details can be found in Lu Tian et al. (2005) <doi:10.1198/016214504000000845>.
Maintained by Qi Yang. Last updated 6 months ago.
1.8 match 1.00 score 3 scripts
rl1081
GCCfactor:GCC Estimation of the Multilevel Factor Model
Provides methods for model selection, estimation, bootstrap inference, and simulation for the multilevel factor model, based on the principal component estimation and generalised canonical correlation approach. Details can be found in "Generalised Canonical Correlation Estimation of the Multilevel Factor Model." Lin and Shin (2023) <doi:10.2139/ssrn.4295429>.
Maintained by Rui Lin. Last updated 1 years ago.
1.8 match 1.00 scorecran
MKLE:Maximum Kernel Likelihood Estimation
Package for fast computation of the maximum kernel likelihood estimator (mkle).
Maintained by Thomas Jaki. Last updated 2 years ago.
1.8 match 1.00 score
zhangyk8
npDoseResponse:Nonparametric Estimation and Inference on Dose-Response Curves
A novel integral estimator for estimating the causal effects with continuous treatments (or dose-response curves) and a localized derivative estimator for estimating the derivative effects. The inference on the dose-response curve and its derivative is conducted via nonparametric bootstrap. The reference paper is Zhang, Chen, and Giessing (2024) <doi:10.48550/arXiv.2405.09003>.
Maintained by Yikun Zhang. Last updated 10 months ago.
1.7 match 1.00 score
mcguirebc
KernSmoothIRT:Nonparametric Item Response Theory
Fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools. The kernel smoothing is based on methods described in Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.
Maintained by Brian McGuire. Last updated 5 years ago.
0.5 match 1 stars 1.98 score 24 scriptscran
kernhaz:Kernel Estimation of Hazard Function in Survival Analysis
Producing kernel estimates of the unconditional and conditional hazard function for right-censored data including methods of bandwidth selection.
Maintained by Iveta Selingerova. Last updated 6 years ago.
0.5 match 1 stars 1.70 score
dxy99999
rbbnp:A Bias Bound Approach to Non-Parametric Inference
A novel bias-bound approach for non-parametric inference is introduced, focusing on both density and conditional expectation estimation. It constructs valid confidence intervals that account for the presence of a non-negligible bias and thus make it possible to perform inference with optimal mean squared error minimizing bandwidths. This package is based on Schennach (2020) <doi:10.1093/restud/rdz065>.
Maintained by Xinyu DAI. Last updated 6 days ago.
0.5 match 1.70 scorecran
rtsdata:R Time Series Intelligent Data Storage
A tool that allows to download and save historical time series data for future use offline. The intelligent updating functionality will only download the new available information; thus, saving you time and Internet bandwidth. It will only re-download the full data-set if any inconsistencies are detected. This package supports following data provides: 'Yahoo' (finance.yahoo.com), 'FRED' (fred.stlouisfed.org), 'Quandl' (data.nasdaq.com), 'AlphaVantage' (www.alphavantage.co), 'Tiingo' (www.tiingo.com).
Maintained by Irina Kapler. Last updated 2 years ago.
0.5 match 1 stars 1.70 score
theoverhelst
lazy:Lazy Learning for Local Regression
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.
Maintained by Theo Verhelst. Last updated 2 years ago.
0.5 match 1.60 score 20 scripts
carlammoreira
DTDA:Doubly Truncated Data Analysis
Implementation of different algorithms for analyzing randomly truncated data, one-sided and two-sided (i.e. doubly) truncated data. It serves to compute empirical cumulative distributions and also kernel density and hazard functions using different bandwidth selectors. Several real data sets are included.
Maintained by Carla Moreira. Last updated 3 years ago.
0.5 match 1.60 score 6 scripts
hamed-ebi
SmoothWin:Soft Windowing on Linear Regression
The main function in the package utilizes a windowing function in the form of an exponential weighting function to linear models. The bandwidth and sharpness of the window are controlled by two parameters. Then, a series of tests are used to identify the right parameters of the window (see Hamed Haselimashhadi et al (2019) <https://www.biorxiv.org/content/10.1101/656678v1>).
Maintained by Hamed Haselimashhadi. Last updated 6 years ago.
0.5 match 1.48 score 4 scripts 1 dependentscran
DOvalidation:Kernel Hazard Estimation with Best One-Sided and Double One-Sided Cross-Validation
Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.
Maintained by Maria Dolores Martinez-Miranda. Last updated 7 years ago.
0.5 match 1 stars 1.00 score
boxiang-wang
dcsvm:Density Convoluted Support Vector Machines
Implements an efficient algorithm for solving sparse-penalized support vector machines with kernel density convolution. This package is designed for high-dimensional classification tasks, supporting lasso (L1) and elastic-net penalties for sparse feature selection and providing options for tuning kernel bandwidth and penalty weights. The 'dcsvm' is applicable to fields such as bioinformatics, image analysis, and text classification, where high-dimensional data commonly arise. Learn more about the methodology and algorithm at Wang, Zhou, Gu, and Zou (2023) <doi:10.1109/TIT.2022.3222767>.
Maintained by Boxiang Wang. Last updated 3 months ago.
0.5 match 1.00 score
