R-universe search: needs:evd

bioc

scRepertoire:A toolkit for single-cell immune receptor profiling

scRepertoire is a toolkit for processing and analyzing single-cell T-cell receptor (TCR) and immunoglobulin (Ig). The scRepertoire framework supports use of 10x, AIRR, BD, MiXCR, Omniscope, TRUST4, and WAT3R single-cell formats. The functionality includes basic clonal analyses, repertoire summaries, distance-based clustering and interaction with the popular Seurat and SingleCellExperiment/Bioconductor R workflows.

Maintained by Nick Borcherding. Last updated 10 days ago.

software immunooncology singlecell classification annotation sequencing cpp

327 stars 10.42 score 240 scripts

dmphillippo

multinma:Bayesian Network Meta-Analysis of Individual and Aggregate Data

Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using 'Stan'.

Maintained by David M. Phillippo. Last updated 3 days ago.

statistics cpp

35 stars 9.34 score 163 scripts

sym33

RecordLinkage:Record Linkage Functions for Linking and Deduplicating Data Sets

Provides functions for linking and deduplicating data sets. Methods based on a stochastic approach are implemented as well as classification algorithms from the machine learning domain. For details, see our paper "The RecordLinkage Package: Detecting Errors in Data" Sariyar M / Borg A (2010) <doi:10.32614/RJ-2010-017>.

Maintained by Murat Sariyar. Last updated 2 years ago.

6 stars 8.96 score 454 scripts 8 dependents

rtdists

rtdists:Response Time Distributions

Provides response time distributions (density/PDF, distribution function/CDF, quantile function, and random generation): (a) Ratcliff diffusion model (Ratcliff & McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C code by Andreas and Jochen Voss and (b) linear ballistic accumulator (LBA; Brown & Heathcote, 2008, <doi:10.1016/j.cogpsych.2007.12.002>) with different distributions underlying the drift rate.

Maintained by Henrik Singmann. Last updated 3 years ago.

cpp

46 stars 8.85 score 116 scripts 2 dependents

liamdbailey

climwin:Climate Window Analysis

Contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response. Please see van de Pol et al. (2016) <doi:10.1111/2041-210X.12590> and Bailey and van de Pol (2016) <doi:10.1371/journal.pone.0167980> for details.

Maintained by Liam D. Bailey. Last updated 5 years ago.

13 stars 7.46 score 138 scripts

koenderks

jfa:Statistical Methods for Auditing

Provides statistical methods for auditing as implemented in JASP for Audit (Derks et al., 2021 <doi:10.21105/joss.02733>). First, the package makes it easy for an auditor to plan a statistical sample, select the sample from the population, and evaluate the misstatement in the sample compliant with international auditing standards. Second, the package provides statistical methods for auditing data, including tests of digit distributions and repeated values. Finally, the package includes methods for auditing algorithms on the aspect of fairness and bias. Next to classical statistical methodology, the package implements Bayesian equivalents of these methods whose statistical underpinnings are described in Derks et al. (2021) <doi:10.1111/ijau.12240>, Derks et al. (2024) <doi:10.2308/AJPT-2021-086>, Derks et al. (2022) <doi:10.31234/osf.io/8nf3e> Derks et al. (2024) <doi:10.31234/osf.io/tgq5z>, and Derks et al. (2025) <doi:10.31234/osf.io/b8tu2>.

Maintained by Koen Derks. Last updated 14 days ago.

algorithm-auditing audit audit-sampling bayesian data-auditing jasp jasp-for-audit statistical-audit statistics cpp

8 stars 6.69 score 17 scripts

matildabrown

rWCVP:Generating Summaries, Reports and Plots from the World Checklist of Vascular Plants

A companion to the World Checklist of Vascular Plants (WCVP). It includes functions to generate maps and species lists, as well as match names to the WCVP. For more details and to cite the package, see: Brown M.J.M., Walker B.E., Black N., Govaerts R., Ondo I., Turner R., Nic Lughadha E. (in press). "rWCVP: A companion R package to the World Checklist of Vascular Plants". New Phytologist.

Maintained by Matilda Brown. Last updated 1 years ago.

22 stars 6.17 score 45 scripts 1 dependents

anestistouloumis

SimCorMultRes:Simulates Correlated Multinomial Responses

Simulates correlated multinomial responses conditional on a marginal model specification.

Maintained by Anestis Touloumis. Last updated 1 years ago.

binary longitudinal-studies multinomial simulation

7 stars 6.04 score 26 scripts 2 dependents

brry

extremeStat:Extreme Value Statistics and Quantile Estimation

Fit, plot and compare several (extreme value) distribution functions. Compute (truncated) distribution quantile estimates and plot return periods on a linear scale. On the fitting method, see Asquith (2011): Distributional Analysis with L-moment Statistics [...] ISBN 1463508417.

Maintained by Berry Boessenkool. Last updated 1 years ago.

14 stars 5.88 score 36 scripts 1 dependents

robjhyndman

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.

17 stars 5.74 score 18 scripts

alowis

RtsEva:Performs the Transformed-Stationary Extreme Values Analysis

Adaptation of the 'Matlab' 'tsEVA' toolbox developed by Lorenzo Mentaschi available here: <https://github.com/menta78/tsEva>. It contains an implementation of the Transformed-Stationary (TS) methodology for non-stationary extreme value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>. In synthesis this approach consists in: (i) transforming a non-stationary time series into a stationary one to which the stationary extreme value theory can be applied; and (ii) reverse-transforming the result into a non-stationary extreme value distribution. 'RtsEva' offers several options for trend estimation (mean, extremes, seasonal) and contains multiple plotting functions displaying different aspects of the non-stationarity of extremes.

Maintained by Alois Tilloy. Last updated 6 months ago.

extreme-value-statistics non-stationary-environment

4 stars 5.41 score 4 scripts

bioc

DeMixT:Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms

DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.

Maintained by Ruonan Li. Last updated 5 months ago.

software statisticalmethod classification geneexpression sequencing microarray tissuemicroarray coverage cpp openmp

5.27 score 25 scripts

pbiecek

ddst:Data Driven Smooth Tests

Smooth tests are data driven (alternative hypothesis is dynamically selected based on data). In this package you will find two groups of smooth of test: goodness-of-fit tests and nonparametric tests for comparing distributions. Among goodness-of-fit tests there are tests for exponent, Gaussian, Gumbel and uniform distribution. Among nonparametric tests there are tests for stochastic dominance, k-sample test, test with umbrella alternatives and test for change-point problems.

Maintained by Przemyslaw Biecek. Last updated 2 years ago.

data-driven smooth-test statistics test

6 stars 5.26 score 6 scripts 2 dependents

zhiwent

BCClong:Bayesian Consensus Clustering for Multiple Longitudinal Features

It is very common nowadays for a study to collect multiple features and appropriately integrating multiple longitudinal features simultaneously for defining individual clusters becomes increasingly crucial to understanding population heterogeneity and predicting future outcomes. 'BCClong' implements a Bayesian consensus clustering (BCC) model for multiple longitudinal features via a generalized linear mixed model. Compared to existing packages, several key features make the 'BCClong' package appealing: (a) it allows simultaneous clustering of mixed-type (e.g., continuous, discrete and categorical) longitudinal features, (b) it allows each longitudinal feature to be collected from different sources with measurements taken at distinct sets of time points (known as irregularly sampled longitudinal data), (c) it relaxes the assumption that all features have the same clustering structure by estimating the feature-specific (local) clusterings and consensus (global) clustering.

Maintained by Zhiwen Tan. Last updated 9 months ago.

openblas cpp openmp

4 stars 5.20 score 10 scripts

bioc

PWMEnrich:PWM enrichment analysis

A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools.

Maintained by Diego Diez. Last updated 5 months ago.

motifannotation sequencematching software

5.08 score 60 scripts

bioc

fobitools:Tools for Manipulating the FOBI Ontology

A set of tools for interacting with the Food-Biomarker Ontology (FOBI). A collection of basic manipulation tools for biological significance analysis, graphs, and text mining strategies for annotating nutritional data.

Maintained by Pol Castellano-Escuder. Last updated 4 months ago.

massspectrometry metabolomics software visualization biomedicalinformatics graphandnetwork annotation cheminformatics pathways genesetenrichment biological-intrerpretation biological-knowledge biological-significance-analysis enrichment-analysis food-biomarker-ontology knowledge-graph nutrition obofoundry ontology text-mining

1 stars 5.08 score 5 scripts

lidiamandre

ReturnCurves:Estimation of Return Curves

Estimates the p-probability return curve proposed by Murphy-Barltrop et al. (2023) <doi:10.1002/env.2797>. Implements pointwise and smooth estimation of the angular dependence function introduced by Wadsworth and Tawn (2013) <doi:10.3150/12-BEJ471>.

Maintained by Lídia André. Last updated 2 months ago.

1 stars 5.06 score 19 scripts

irsn

Renext:Renewal Method for Extreme Values Extrapolation

Peaks Over Threshold (POT) or 'methode du renouvellement'. The distribution for the excesses can be chosen, and heterogeneous data (including historical data or block data) can be used in a Maximum-Likelihood framework.

Maintained by Yann Richet. Last updated 1 years ago.

4.99 score 82 scripts 4 dependents

sujit-sahu

bmstdr:Bayesian Modeling of Spatio-Temporal Data with R

Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer', 'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.

Maintained by Sujit K. Sahu. Last updated 1 days ago.

bayesian modelling spatio-temporal-data cpp

16 stars 4.98 score 12 scripts

padrinodb

Rpadrino:Interact with the 'PADRINO' IPM Database

'PADRINO' houses textual representations of Integral Projection Models which can be converted from their table format into full kernels to reproduce or extend an already published analysis. 'Rpadrino' is an R interface to this database. For more information on Integral Projection Models, see Easterling et al. (2000) <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>, and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. See Levin et al. (2021) for more information on 'ipmr', the engine that powers model reconstruction <doi:10.1111/2041-210X.13683>.

Maintained by Sam Levin. Last updated 2 years ago.

3 stars 4.95 score 15 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.

28 stars 4.92 score 9 scripts 2 dependents

davidvandijcke

DiSCos:Distributional Synthetic Controls Estimation

The method of synthetic controls is a widely-adopted tool for evaluating causal effects of policy changes in settings with observational data. In many settings where it is applicable, researchers want to identify causal effects of policy changes on a treated unit at an aggregate level while having access to data at a finer granularity. This package implements a simple extension of the synthetic controls estimator, developed in Gunsilius (2023) <doi:10.3982/ECTA18260>, that takes advantage of this additional structure and provides nonparametric estimates of the heterogeneity within the aggregate unit. The idea is to replicate the quantile function associated with the treated unit by a weighted average of quantile functions of the control units. The package contains tools for aggregating and plotting the resulting distributional estimates, as well as for carrying out inference on them.

Maintained by David Van Dijcke. Last updated 16 days ago.

1 stars 4.81 score 8 scripts

dfrancom

BASS:Bayesian Adaptive Spline Surfaces

Bayesian fitting and sensitivity analysis methods for adaptive spline surfaces described in <doi:10.18637/jss.v094.i08>. Built to handle continuous and categorical inputs as well as functional or scalar output. An extension of the methodology in Denison, Mallick and Smith (1998) <doi:10.1023/A:1008824606259>.

Maintained by Devin Francom. Last updated 2 years ago.

1 stars 4.78 score 173 scripts

egeminiani

penfa:Single- And Multiple-Group Penalized Factor Analysis

Fits single- and multiple-group penalized factor analysis models via a trust-region algorithm with integrated automatic multiple tuning parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.

Maintained by Elena Geminiani. Last updated 4 years ago.

factor-analysis lasso latent-variables multiple-group optimization penalization psychometrics

3 stars 4.48 score 5 scripts

manueleleonelli

extrememix:Bayesian Estimation of Extreme Value Mixture Models

Fits extreme value mixture models, which are models for tails not requiring selection of a threshold, for continuous data. It includes functions for model comparison, estimation of quantity of interest in extreme value analysis and plotting. Reference: CN Behrens, HF Lopes, D Gamerman (2004) <doi:10.1191/1471082X04st075oa>. FF do Nascimento, D. Gamerman, HF Lopes <doi:10.1007/s11222-011-9270-z>.

Maintained by Manuele Leonelli. Last updated 5 months ago.

cpp

2 stars 4.48 score 4 scripts

opasche

EQRN:Extreme Quantile Regression Neural Networks for Risk Forecasting

This framework enables forecasting and extrapolating measures of conditional risk (e.g. of extreme or unprecedented events), including quantiles and exceedance probabilities, using extreme value statistics and flexible neural network architectures. It allows for capturing complex multivariate dependencies, including dependencies between observations, such as sequential dependence (time-series). The methodology was introduced in Pasche and Engelke (2024) <doi:10.1214/24-AOAS1907> (also available in preprint: Pasche and Engelke (2022) <doi:10.48550/arXiv.2208.07590>).

Maintained by Olivier C. Pasche. Last updated 9 days ago.

7 stars 4.24 score

sevvandi

oddnet:Anomaly Detection in Temporal Networks

Anomaly detection in dynamic, temporal networks. The package 'oddnet' uses a feature-based method to identify anomalies. First, it computes many features for each network. Then it models the features using time series methods. Using time series residuals it detects anomalies. This way, the temporal dependencies are accounted for when identifying anomalies (Kandanaarachchi, Hyndman 2022) <arXiv:2210.07407>.

Maintained by Sevvandi Kandanaarachchi. Last updated 10 months ago.

3 stars 4.22 score 11 scripts

bking124

countSTAR:Flexible Modeling of Count Data

For Bayesian and classical inference and prediction with count-valued data, Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable, and easy-to-use approach. STAR models the observed count data using a rounded continuous data model and incorporates a transformation for greater flexibility. Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of (i) transforming count-valued data and subsequently (ii) modeling the transformed data using Gaussian models. STAR is well-defined for count-valued data, which is reflected in predictive accuracy, and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. Importantly, STAR is easy to combine with existing MCMC or point estimation methods for continuous data, which allows seamless adaptation of continuous data models (such as linear regressions, additive models, BART, random forests, and gradient boosting machines) for count-valued data. The package also includes several methods for modeling count time series data, namely via warped Dynamic Linear Models. For more details and background on these methodologies, see the works of Kowal and Canale (2020) <doi:10.1214/20-EJS1707>, Kowal and Wu (2022) <doi:10.1111/biom.13617>, King and Kowal (2023) <doi:10.1214/23-BA1394>, and Kowal and Wu (2023) <arXiv:2110.12316>.

Maintained by Brian King. Last updated 2 years ago.

cpp

3 stars 4.18 score 3 scripts

giampmarra

GJRM:Generalised Joint Regression Modelling

Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.

Maintained by Giampiero Marra. Last updated 5 months ago.

4 stars 4.04 score 67 scripts 5 dependents

bioc

MetaPhOR:Metabolic Pathway Analysis of RNA

MetaPhOR was developed to enable users to assess metabolic dysregulation using transcriptomic-level data (RNA-sequencing and Microarray data) and produce publication-quality figures. A list of differentially expressed genes (DEGs), which includes fold change and p value, from DESeq2 or limma, can be used as input, with sample size for MetaPhOR, and will produce a data frame of scores for each KEGG pathway. These scores represent the magnitude and direction of transcriptional change within the pathway, along with estimated p-values.MetaPhOR then uses these scores to visualize metabolic profiles within and between samples through a variety of mechanisms, including: bubble plots, heatmaps, and pathway models.

Maintained by Emily Isenhart. Last updated 5 months ago.

metabolomics rnaseq pathways geneexpression differentialexpression kegg sequencing microarray

4.00 score 1 scripts

bioc

powerTCR:Model-Based Comparative Analysis of the TCR Repertoire

This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits.

Maintained by Hillary Koch. Last updated 5 months ago.

software clustering biomedicalinformatics

4.00 score 4 scripts

biorabbit

RegDDM:Generalized Linear Regression with DDM

Drift-Diffusion Model (DDM) has been widely used to model binary decision-making tasks, and many research studies the relationship between DDM parameters and other characteristics of the subject. This package uses 'RStan' to perform generalized liner regression analysis over DDM parameters via a single Bayesian Hierarchical model. Compared to estimating DDM parameters followed by a separate regression model, 'RegDDM' reduces bias and improves statistical power.

Maintained by Zekai Jin. Last updated 4 months ago.

1 stars 3.93 score 3 scripts

sidiwang

snSMART:Small N Sequential Multiple Assignment Randomized Trial Methods

Consolidated data simulation, sample size calculation and analysis functions for several snSMART (small sample sequential, multiple assignment, randomized trial) designs under one library. See Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian analysis of small n sequential multiple assignment randomized trials (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732 <doi:10.1002/sim.7900>.

Maintained by Michael Kleinsasser. Last updated 6 months ago.

bayesian-analysis clinical-trials rare-disease small-sample jags cpp

1 stars 3.90 score 3 scripts

benrenard

HydroPortailStats:'HydroPortail' Statistical Functions

Statistical functions used in the French 'HydroPortail' <https://hydro.eaufrance.fr/>. This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities. Technical details are available (in French) in Renard (2016) <https://hal.inrae.fr/hal-02605318>.

Maintained by Benjamin Renard. Last updated 5 months ago.

hydrology statistical-distributions statistics

3 stars 3.78 score 1 scripts

guillaumeevin

GWEX:Multi-Site Stochastic Models for Daily Precipitation and Temperature

Application of multi-site models for daily precipitation and temperature data. This package is designed for an application to 105 precipitation and 26 temperature gauges located in Switzerland. It applies fitting procedures and provides weather generators described in the following references: - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.5194/hess-22-655-2018>. - Evin, G., A.-C. Favre, and B. Hingray. (2018) <doi:10.1007/s00704-018-2404-x>.

Maintained by Guillaume Evin. Last updated 4 months ago.

cpp

2 stars 3.70 score 6 scripts

r-forge

RobExtremes:Optimally Robust Estimation for Extreme Value Distributions

Optimally robust estimation for extreme value distributions using S4 classes and methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and 'ROptEst'); the underlying theoretic results can be found in Ruckdeschel and Horbenko, (2013 and 2012), \doi{10.1080/02331888.2011.628022} and \doi{10.1007/s00184-011-0366-4}.

Maintained by Peter Ruckdeschel. Last updated 2 months ago.

3.67 score 39 scripts

aleshing

multilink:Multifile Record Linkage and Duplicate Detection

Implementation of the methodology of Aleshin-Guendel & Sadinle (2022) <doi:10.1080/01621459.2021.2013242>. It handles the general problem of multifile record linkage and duplicate detection, where any number of files are to be linked, and any of the files may have duplicates.

Maintained by Serge Aleshin-Guendel. Last updated 2 years ago.

openblas cpp openmp

9 stars 3.65 score 4 scripts

lbenitesanchez

ssmn:Skew Scale Mixtures of Normal Distributions

Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.

Maintained by Luis Benites Sanchez. Last updated 8 years ago.

2 stars 3.48 score 4 scripts 1 dependents

gshs-ornl

revengc:Reverse Engineering Summarized Data

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses). The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers decoupled and/or censored count data with two main functions. The cnbinom.pars() function estimates the average and dispersion parameter of a censored univariate frequency table. The rec() function reverse engineers summarized data into an uncensored bivariate table of probabilities.

Maintained by Samantha Duchscherer. Last updated 6 years ago.

5 stars 3.44 score 11 scripts

vandenman

DstarM:Analyze Two Choice Reaction Time Data with the D*M Method

A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.

Maintained by Don van den Bergh. Last updated 3 years ago.

hacktoberfest openblas cpp

3 stars 3.41 score 17 scripts

borisberanger

ExtremalDep:Extremal Dependence Models

A set of procedures for parametric and non-parametric modelling of the dependence structure of multivariate extreme-values is provided. The statistical inference is performed with non-parametric estimators, likelihood-based estimators and Bayesian techniques. It adapts the methodologies of Beranger and Padoan (2015) <doi:10.48550/arXiv.1508.05561>, Marcon et al. (2016) <doi:10.1214/16-EJS1162>, Marcon et al. (2017) <doi:10.1002/sta4.145>, Marcon et al. (2017) <doi:10.1016/j.jspi.2016.10.004> and Beranger et al. (2021) <doi:10.1007/s10687-019-00364-0>. This package also allows for the modelling of spatial extremes using flexible max-stable processes. It provides simulation algorithms and fitting procedures relying on the Stephenson-Tawn likelihood as per Beranger at al. (2021) <doi:10.1007/s10687-020-00376-1>.

Maintained by Simone Padoan. Last updated 3 months ago.

openblas

3.30 score 1 scripts

indecis-project

INQC:Quality Control of Climatological Daily Time Series

Collection of functions for quality control (QC) of climatological daily time series (e.g. the ECA&D station data).

Maintained by Enric Aguilar. Last updated 4 years ago.

4 stars 3.30 score 1 scripts

annaltyler

cape:Combined Analysis of Pleiotropy and Epistasis for Diversity Outbred Mice

Combined Analysis of Pleiotropy and Epistasis infers predictive networks between genetic variants and phenotypes. It can be used with standard two-parent populations as well as multi-parent populations, such as the Diversity Outbred (DO) mice, Collaborative Cross (CC) mice, or the multi-parent advanced generation intercross (MAGIC) population of Arabidopsis thaliana. It uses complementary information of pleiotropic gene variants across different phenotypes to resolve models of epistatic interactions between alleles. To do this, cape reparametrizes main effect and interaction coefficients from pairwise variant regressions into directed influence parameters. These parameters describe how alleles influence each other, in terms of suppression and enhancement, as well as how gene variants influence phenotypes. All of the final interactions are reported as directed interactions between pairs of parental alleles. For detailed descriptions of the methods used in this package please see the following references. Carter, G. W., Hays, M., Sherman, A. & Galitski, T. (2012) <doi:10.1371/journal.pgen.1003010>. Tyler, A. L., Lu, W., Hendrick, J. J., Philip, V. M. & Carter, G. W. (2013) <doi:10.1371/journal.pcbi.1003270>.

Maintained by Anna Tyler. Last updated 1 years ago.

3.27 score 37 scripts

cran

truncdist:Truncated Random Variables

A collection of tools to evaluate probability density functions, cumulative distribution functions, quantile functions and random numbers for truncated random variables. These functions are provided to also compute the expected value and variance. Nadarajah and Kotz (2006) developed most of the functions. QQ plots can be produced. All the probability functions in the stats, stats4 and evd packages are automatically available for truncation..

Maintained by Frederick Novomestky. Last updated 9 years ago.

3.25 score 20 dependents

hosseinazari

StatRank:Statistical Rank Aggregation: Inference, Evaluation, and Visualization

A set of methods to implement Generalized Method of Moments and Maximal Likelihood methods for Random Utility Models. These methods are meant to provide inference on rank comparison data. These methods accept full, partial, and pairwise rankings, and provides methods to break down full or partial rankings into their pairwise components. Please see Generalized Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of some of our methods.

Maintained by Hossein Azari Soufiani. Last updated 10 years ago.

3.24 score 58 scripts 2 dependents

cmollica

PLMIX:Bayesian Analysis of Finite Mixture of Plackett-Luce Models

Fit finite mixtures of Plackett-Luce models for partial top rankings/orderings within the Bayesian framework. It provides MAP point estimates via EM algorithm and posterior MCMC simulations via Gibbs Sampling. It also fits MLE as a special case of the noninformative Bayesian analysis with vague priors. In addition to inferential techniques, the package assists other fundamental phases of a model-based analysis for partial rankings/orderings, by including functions for data manipulation, simulation, descriptive summary, model selection and goodness-of-fit evaluation. Main references on the methods are Mollica and Tardella (2017) <doi.org/10.1007/s11336-016-9530-0> and Mollica and Tardella (2014) <doi/10.1002/sim.6224>.

Maintained by Cristina Mollica. Last updated 4 years ago.

cpp

3.15 score 28 scripts

vaudigier

micemd:Multiple Imputation by Chained Equations with Multilevel Data

Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'.

Maintained by Vincent Audigier. Last updated 1 years ago.

1 stars 3.08 score 80 scripts 1 dependents

vaudigier

clusterMI:Cluster Analysis with Missing Values by Multiple Imputation

Allows clustering of incomplete observations by addressing missing values using multiple imputation. For achieving this goal, the methodology consists in three steps, following Audigier and Niang 2022 <doi:10.1007/s11634-022-00519-1>. I) Missing data imputation using dedicated models. Four multiple imputation methods are proposed, two are based on joint modelling and two are fully sequential methods, as discussed in Audigier et al. (2021) <doi:10.48550/arXiv.2106.04424>. II) cluster analysis of imputed data sets. Six clustering methods are available (distances-based or model-based), but custom methods can also be easily used. III) Partition pooling. The set of partitions is aggregated using Non-negative Matrix Factorization based method. An associated instability measure is computed by bootstrap (see Fang, Y. and Wang, J., 2012 <doi:10.1016/j.csda.2011.09.003>). Among applications, this instability measure can be used to choose a number of clusters with missing values. The package also proposes several diagnostic tools to tune the number of imputed data sets, to tune the number of iterations in fully sequential imputation, to check the fit of imputation models, etc.

Maintained by Vincent Audigier. Last updated 1 months ago.

openblas cpp

2.90 score

lbelzile

evt0:Mean of Order P, Peaks over Random Threshold Hill and High Quantile Estimates

The R package proposes extreme value index estimators for heavy tailed models by mean of order p <DOI:10.1016/j.csda.2012.07.019>, peaks over random threshold <DOI:10.57805/revstat.v4i3.37> and a bias-reduced estimator <DOI:10.1080/00949655.2010.547196>. The package also computes moment, generalised Hill <DOI:10.2307/3318416> and mixed moment estimates for the extreme value index. High quantiles and value at risk estimators based on these estimators are implemented.

Maintained by Leo Belzile. Last updated 1 years ago.

2.81 score 13 scripts

liygcr

LassoGEE:High-Dimensional Lasso Generalized Estimating Equations

Fits generalized estimating equations with L1 regularization to longitudinal data with high dimensional covariates. Use a efficient iterative composite gradient descent algorithm.

Maintained by Yaguang Li. Last updated 4 years ago.

openblas cpp openmp

2.70 score

cran

SpatialGEV:Fit Spatial Generalized Extreme Value Models

Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2024) <doi:10.48550/arXiv.2110.07051>.

Maintained by Meixi Chen. Last updated 10 months ago.

2.70 score

tlugrin

tsxtreme:Bayesian Modelling of Extremal Dependence in Time Series

Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.

Maintained by Thomas Lugrin. Last updated 18 days ago.

cpp

2.70 score 7 scripts

meganheyman

lmboot:Bootstrap in Linear Models

Various efficient and robust bootstrap methods are implemented for linear models with least squares estimation. Functions within this package allow users to create bootstrap sampling distributions for model parameters, test hypotheses about parameters, and visualize the bootstrap sampling or null distributions. Methods implemented for linear models include the wild bootstrap by Wu (1986) <doi:10.1214/aos/1176350142>, the residual and paired bootstraps by Efron (1979, ISBN:978-1-4612-4380-9), the delete-1 jackknife by Quenouille (1956) <doi:10.2307/2332914>, and the Bayesian bootstrap by Rubin (1981) <doi:10.1214/aos/1176345338>.

Maintained by Megan Heyman. Last updated 5 years ago.

2 stars 2.70 score 25 scripts

nyilin

ROlogit:Fit Rank-Ordered Logit (RO-Logit) Model

Implements the rank-ordered logit (RO-logit) model for stratified analysis of continuous outcomes introduced by Tan et al. (2017) <doi:10.1177/0962280217747309>. Model diagnostics based on the heuristic residuals and estimates in linear scales are available from the package, and outcomes with ties are supported.

Maintained by Ning Yilin. Last updated 4 years ago.

2.70 score 2 scripts

jskoien

intamap:Procedures for Automated Interpolation

Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. Here, we introduce classes and methods that can do this interpolation automatically. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.

Maintained by Jon Olav Skoien. Last updated 1 years ago.

1 stars 2.61 score 68 scripts 2 dependents

r-fndv

mvPot:Multivariate Peaks-over-Threshold Modelling for Spatial Extreme Events

Tools for high-dimensional peaks-over-threshold inference and simulation of Brown-Resnick and extremal Student spatial extremal processes. These include optimization routines based on censored likelihood and gradient scoring, and exact simulation algorithms for max-stable and multivariate Pareto distributions based on rejection sampling. Fast multivariate Gaussian and Student distribution functions using separation-of-variable algorithm with quasi Monte Carlo integration are also provided. Key references include de Fondeville and Davison (2018) <doi:10.1093/biomet/asy026>, Thibaud and Opitz (2015) <doi:10.1093/biomet/asv045>, Wadsworth and Tawn (2014) <doi:10.1093/biomet/ast042> and Genz and Bretz (2009) <doi:10.1007/978-3-642-01689-9>.

Maintained by Leo Belzile. Last updated 19 days ago.

cpp

2 stars 2.60 score

cran

IDF:Estimation and Plotting of IDF Curves

Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>]. The package 'IDF' provides functions to estimate IDF parameters for given precipitation time series on the basis of a duration-dependent generalized extreme value distribution [Koutsoyiannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>].

Maintained by Felix S. Fauer. Last updated 3 years ago.

2.30 score

extremestats

extremis:Statistics of Extremes

Conducts inference in statistical models for extreme values (de Carvalho et al (2012), <doi:10.1080/03610926.2012.709905>; de Carvalho and Davison (2014), <doi:10.1080/01621459.2013.872651>; Einmahl et al (2016), <doi:10.1111/rssb.12099>).

Maintained by Miguel de Carvalho. Last updated 3 years ago.

fortran

2.00 score 10 scripts

cran

STCCGEV:Conditional Copula Model for Crop Yield Forecasting

Provides functions to model and forecast crop yields using a spatial temporal conditional copula approach. The package incorporates extreme weather covariates and Bayesian Structural Time Series models to analyze crop yield dependencies across multiple regions. Includes tools for fitting, simulating, and visualizing results. This method build upon established R packages, including 'Hofert' 'et' 'al'. (2025) <doi:10.32614/CRAN.package.copula>, 'Scott' (2024) <doi:10.32614/CRAN.package.bsts>, and 'Stephenson' 'et' 'al'. (2024) <doi:10.32614/CRAN.package.evd>.

Maintained by Yongkun Li. Last updated 2 days ago.

2.00 score

pakillo

taxonomy.tools:Tools to Work with Taxonomic Data

Tools to Work with Taxonomic Data.

Maintained by Francisco Rodriguez-Sanchez. Last updated 3 months ago.

2.00 score 3 scripts

pigian

janus:Optimized Recommending System Based on 'tensorflow'

Proposes a coarse-to-fine optimization of a recommending system based on deep-neural networks using 'tensorflow'.

Maintained by Giancarlo Vercellino. Last updated 2 years ago.

1.81 score 65 scripts

tyler-hansen

HodgesTools:Common Use Tools for Genomic Analysis

Built by Hodges lab members for current and future Hodges lab members. Other individuals are welcome to use as well. Provides useful functions that the lab uses everyday to analyze various genomic datasets. Critically, only general use functions are provided; functions specific to a given technique are reserved for a separate package. As the lab grows, we expect to continue adding functions to the package to build on previous lab members code.

Maintained by Tyler Hansen. Last updated 2 years ago.

1.70 score 1 scripts

haijiangq

EFAfactors:Determining the Number of Factors in Exploratory Factor Analysis

Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.

Maintained by Haijiang Qin. Last updated 1 months ago.

openblas cpp openmp

1.70 score

sbaran0225

ensembleMOS:Ensemble Model Output Statistics

Ensemble Model Output Statistics to create probabilistic forecasts from ensemble forecasts and weather observations.

Maintained by Sandor Baran. Last updated 7 years ago.

1 stars 1.43 score 27 scripts

jskoien

intamapInteractive:Interactive Add-on Functionality for 'intamap'

The methods in this package adds to the functionality of the 'intamap' package, such as bias correction and network optimization. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.

Maintained by Jon Skoien. Last updated 1 years ago.

openjdk

1.41 score 26 scripts

cran

bssn:Birnbaum-Saunders Model

It provides the density, distribution function, quantile function, random number generator, reliability function, failure rate, likelihood function, moments and EM algorithm for Maximum Likelihood estimators, also empirical quantile and generated envelope for a given sample, all this for the three parameter Birnbaum-Saunders model based on Skew-Normal Distribution. Also, it provides the random number generator for the mixture of Birnbaum-Saunders model based on Skew-Normal distribution. Additionally, we incorporate the EM algorithm based on the assumption that the error term follows a finite mixture of Sinh-normal distributions.

Maintained by Rocio Paola Maehara. Last updated 5 years ago.

1.00 score

jdench

rSHAPE:Simulated Haploid Asexual Population Evolution

In silico experimental evolution offers a cost-and-time effective means to test evolutionary hypotheses. Existing evolutionary simulation tools focus on simulations in a limited experimental framework, and tend to report on only the results presumed of interest by the tools designer. The R-package for Simulated Haploid Asexual Population Evolution ('rSHAPE') addresses these concerns by implementing a robust simulation framework that outputs complete population demographic and genomic information for in silico evolving communities. Allowing more than 60 parameters to be specified, 'rSHAPE' simulates evolution across discrete time-steps for an evolving community of haploid asexual populations with binary state genomes. These settings are for the current state of 'rSHAPE' and future steps will be to increase the breadth of evolutionary conditions permitted. At present, most effort was placed into permitting varied growth models to be simulated (such as constant size, exponential growth, and logistic growth) as well as various fitness landscape models to reflect the evolutionary landscape (e.g.: Additive, House of Cards - Stuart Kauffman and Simon Levin (1987) <doi:10.1016/S0022-5193(87)80029-2>, NK - Stuart A. Kauffman and Edward D. Weinberger (1989) <doi:10.1016/S0022-5193(89)80019-0>, Rough Mount Fuji - Neidhart, Johannes and Szendro, Ivan G and Krug, Joachim (2014) <doi:10.1534/genetics.114.167668>). This package includes numerous functions though users will only need defineSHAPE(), runSHAPE(), shapeExperiment() and summariseExperiment(). All other functions are called by these main functions and are likely only to be on interest for someone wishing to develop 'rSHAPE'. Simulation results will be stored in files which are exported to the directory referenced by the shape_workDir option (defaults to tempdir() but do change this by passing a folderpath argument for workDir when calling defineSHAPE() if you plan to make use of your results beyond your current session). 'rSHAPE' will generate numerous replicate simulations for your defined range of experimental parameters. The experiment will be built under the experimental working directory (i.e.: referenced by the option shape_workDir set using defineSHAPE() ) where individual replicate simulation results will be stored as well as processed results which I have made in an effort to facilitate analyses by automating collection and processing of the potentially thousands of files which will be created. On that note, 'rSHAPE' implements a robust and flexible framework with highly detailed output at the cost of computational efficiency and potentially requiring significant disk space (generally gigabytes but up to tera-bytes for very large simulation efforts). So, while 'rSHAPE' offers a single framework in which we can simulate evolution and directly compare the impacts of a wide range of parameters, it is not as quick to run as other in silico simulation tools which focus on a single scenario with limited output. There you have it, 'rSHAPE' offers you a less restrictive in silico evolutionary playground than other tools and I hope you enjoy testing your hypotheses.

Maintained by Jonathan Dench. Last updated 6 years ago.

1.00 score

cran

BayesCR:Bayesian Analysis of Censored Regression Models Under Scale Mixture of Skew Normal Distributions

Propose a parametric fit for censored linear regression models based on SMSN distributions, from a Bayesian perspective. Also, generates SMSN random variables.

Maintained by Aldo M. Garay. Last updated 8 years ago.

1.00 score

simonepadoan

ExtremeRisks:Extreme Risk Measures

A set of procedures for estimating risks related to extreme events via risk measures such as Expectile, Value-at-Risk, etc. is provided. Estimation methods for univariate independent observations and temporal dependent observations are available. The methodology is extended to the case of independent multidimensional observations. The statistical inference is performed through parametric and non-parametric estimators. Inferential procedures such as confidence intervals, confidence regions and hypothesis testing are obtained by exploiting the asymptotic theory. Adapts the methodologies derived in Padoan and Stupfler (2020) <arxiv:2004.04078>, Padoan and Stupfler (2020) <arxiv:2007.08944>, Daouia et al. (2018) <doi:10.1111/rssb.12254>, Drees (2000) <doi:10.1214/aoap/1019487617>, Drees (2003) <doi:10.3150/bj/1066223272>, de Haan and Ferreira (2006) <doi:10.1007/0-387-34471-3>, de Haan et al. (2016) <doi:10.1007/s00780-015-0287-6>.

Maintained by Simone Padoan. Last updated 5 years ago.

1 stars 1.00 score 3 scripts

joalor93

OBASpatial:Objective Bayesian Analysis for Spatial Regression Models

It makes an objective Bayesian analysis of the spatial regression model using both the normal (NSR) and student-T (TSR) distributions. The functions provided give prior and posterior objective densities and allow default Bayesian estimation of the model regression parameters. Details can be found in Ordonez et al. (2020) <arXiv:2004.04341>.

Maintained by Alejandro Ordonez. Last updated 3 years ago.

1.00 score

cran

metafuse:Fused Lasso Approach in Regression Coefficient Clustering

Fused lasso method to cluster and estimate regression coefficients of the same covariate across different data sets when a large number of independent data sets are combined. Package supports Gaussian, binomial, Poisson and Cox PH models.

Maintained by Lu Tang. Last updated 8 years ago.

1.00 score

cran

flood:Statistical Methods for the (Regional) Analysis of Flood Frequency

Includes several statistical methods for the estimation of parameters and high quantiles of river flow distributions. The focus is on regional estimation based on homogeneity assumptions and computed from multivariate observations (multiple measurement stations). For details see Kinsvater et al. (2017) <arXiv:1701.06455>.

Maintained by Friederike Deiters. Last updated 8 years ago.

1.00 score

maxtailt

extremeIndex:Forecast Verification for Extreme Events

An index measuring the amount of information brought by forecasts for extreme events, subject to calibration, is computed. This index is originally designed for weather or climate forecasts, but it may be used in other forecasting contexts. This is the implementation of the index in Taillardat et al. (2019) <arXiv:1905.04022>.

Maintained by Maxime Taillardat. Last updated 3 years ago.

1 stars 1.00 score

jahmadkhan

DEEVD:Density Estimation by Extreme Value Distributions

Provides mean squared error (MSE) and plot the kernel densities related to extreme value distributions with their estimated values. By using Gumbel and Weibull Kernel. See Salha et al. (2014) <doi:10.4236/ojs.2014.48061> and Khan and Akbar (2021) <doi:10.4236/ojs.2021.112018 >.

Maintained by Javaria Ahmad Khan. Last updated 3 years ago.

1.00 score

edovi

evtclass:Extreme Value Theory for Open Set Classification - GPD and GEV Classifiers

Two classifiers for open set recognition and novelty detection based on extreme value theory. The first classifier is based on the generalized Pareto distribution (GPD) and the second classifier is based on the generalized extreme value (GEV) distribution. For details, see Vignotto, E., & Engelke, S. (2018) <arXiv:1808.09902>.

Maintained by Edoardo Vignotto. Last updated 6 years ago.

1 stars 1.00 score 4 scripts

leilamarvian

PreProcessRecordLinkage:Preprocessing Record Linkage

In this record linkage package, data preprocessing has been meticulously executed to cover a wide range of datasets, ensuring that variable names are standardized using synonyms. This approach facilitates seamless data integration and analysis across various datasets. While users have the flexibility to modify variable names, the system intelligently ensures that changes are only permitted when they do not compromise data consistency or essential variable essence.

Maintained by Leila Marvian Mashhad. Last updated 2 years ago.

1.00 score

cran

flexmsm:A General Framework for Flexible Multi-State Survival Modelling

A general estimation framework for multi-state Markov processes with flexible specification of the transition intensities. The log-transition intensities can be specified through Generalised Additive Models which allow for virtually any type of covariate effect. Elementary specifications such as time-homogeneous processes and simple parametric forms are also supported. There are no limitations on the type of process one can assume, with both forward and backward transitions allowed and virtually any number of states.

Maintained by Alessia Eletti. Last updated 8 months ago.

1.00 score

cran

EnviroPRA2:Environmental Probabilistic Risk Assessment Tools

It contains functions for dose calculation for different routes, fitting data to probability distributions, random number generation (Monte Carlo simulation) and calculation of systemic and carcinogenic risks. For more information see the publication: Barrio-Parra et al. (2019) "Human-health probabilistic risk assessment: the role of exposure factors in an urban garden scenario" <doi:10.1016/j.landurbplan.2019.02.005>.

Maintained by Fernando Barrio-Parra. Last updated 1 years ago.

1.00 score

leilamarvian

GISINTEGRATION:GIS Integration

Designed to facilitate the preprocessing and linking of GIS (Geographic Information System) databases <https://www.sciencedirect.com/topics/computer-science/gis-database>, the R package 'GISINTEGRATION' offers a robust solution for efficiently preparing GIS data for advanced spatial analyses. This package excels in simplifying intrica procedures like data cleaning, normalization, and format conversion, ensuring that the data are optimally primed for precise and thorough analysis.

Maintained by Leila Marvian Mashhad. Last updated 1 years ago.

1.00 score

cran

PredictionR:Prediction for Future Data from any Continuous Distribution

Functions to get prediction intervals and prediction points of future observations from any continuous distribution.

Maintained by Hadeer A. Ghonem. Last updated 5 years ago.

1.00 score

pigian

xpect:Probabilistic Time Series Forecasting with XGBoost and Conformal Inference

Implements a probabilistic approach to time series forecasting combining XGBoost regression with conformal inference methods. The package provides functionality for generating predictive distributions, evaluating uncertainty, and optimizing hyperparameters using Bayesian, coarse-to-fine, or random search strategies.

Maintained by Giancarlo Vercellino. Last updated 6 days ago.

1.00 score

willzywiec

criticality:Modeling Fissile Material Operations in Nuclear Facilities

A collection of functions for modeling fissile material operations in nuclear facilities, based on Zywiec et al (2021) <doi:10.1016/j.ress.2020.107322>.

Maintained by William Zywiec. Last updated 2 years ago.

1.00 score

cran

psgp:Projected Spatial Gaussian Process Methods

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

Maintained by Ben Ingram. Last updated 1 years ago.

openblas cpp

1.00 score

casua1statistician

BRBVS:Variable Selection and Ranking in Copula Survival Models Affected by General Censoring Scheme

Performs variable selection and ranking based on several measures for the class of copula survival model(s) in high dimensional domain. The package is based on the class of copula survival model(s) implemented in the 'GJRM' package.

Maintained by Danilo Petti. Last updated 9 months ago.

1.00 score

wolfgangrolke

simgof:Simultaneous Goodness-of-Fits Tests

Routine that allows the user to run several goodness-of-fit tests. It also combines the tests and returns a properly adjusted family-wise p value. Details can be found in <arXiv:2007.04727>.

Maintained by Wolfgang Rolke. Last updated 4 years ago.

1.00 score 1 scripts

allegropiano

GLDreg:Fit GLD Regression/Quantile/AFT Model to Data

Owing to the rich shapes of Generalised Lambda Distributions (GLDs), GLD standard/quantile/Accelerated Failure Time (AFT) regression is a competitive flexible model compared to standard/quantile/AFT regression. The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. For AFT model, it also eliminates the needs to try several different AFT models, owing to the flexible shapes of GLD. The goodness of fit of the proposed model can be assessed via QQ plots and Kolmogorov-Smirnov tests and data driven smooth test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data. References include the following: Su (2015) "Flexible Parametric Quantile Regression Model" <doi:10.1007/s11222-014-9457-1>, Su (2021) "Flexible parametric accelerated failure time model"<doi:10.1080/10543406.2021.1934854>.

Maintained by Steve Su. Last updated 1 years ago.

1 stars 1.00 score 8 scripts