Showing 90 of total 90 results (show query)
tidyverse
ggplot2:Create Elegant Data Visualisations Using the Grammar of Graphics
A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
Maintained by Thomas Lin Pedersen. Last updated 4 days ago.
data-visualisationvisualisation
6.6k stars 25.10 score 645k scripts 7.6k dependents
robjhyndman
forecast:Forecasting Functions for Time Series and Linear Models
Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.
Maintained by Rob Hyndman. Last updated 7 months ago.
forecastforecastingopenblascpp
1.1k stars 17.46 score 16k scripts 240 dependents
tidymodels
parsnip:A Common API to Modeling and Analysis Functions
A common interface is provided to allow users to specify a model without having to remember the different argument names across different functions or computational engines (e.g. 'R', 'Spark', 'Stan', 'H2O', etc).
Maintained by Max Kuhn. Last updated 17 days ago.
612 stars 16.37 score 3.4k scripts 69 dependentstidymodels
tune:Tidy Tuning Tools
The ability to tune models is important. 'tune' contains functions and classes to be used in conjunction with other 'tidymodels' packages for finding reasonable values of hyper-parameters in models, pre-processing methods, and post-processing steps.
Maintained by Max Kuhn. Last updated 24 days ago.
293 stars 14.27 score 756 scripts 39 dependentstidymodels
corrr:Correlations in R
A tool for exploring correlations. It makes it possible to easily perform routine tasks when exploring correlation matrices such as ignoring the diagonal, focusing on the correlations of certain variables against others, or rearranging and visualizing the matrix in terms of the strength of the correlations.
Maintained by Max Kuhn. Last updated 1 years ago.
593 stars 13.82 score 2.9k scripts 7 dependents
dieghernan
tidyterra:'tidyverse' Methods and 'ggplot2' Helpers for 'terra' Objects
Extension of the 'tidyverse' for 'SpatRaster' and 'SpatVector' objects of the 'terra' package. It includes also new 'geom_' functions that provide a convenient way of visualizing 'terra' objects with 'ggplot2'.
Maintained by Diego Hernangómez. Last updated 4 days ago.
terraggplot-extensionr-spatialrspatial
190 stars 13.59 score 1.9k scripts 25 dependents
tidyverts
feasts:Feature Extraction and Statistics for Time Series
Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.
Maintained by Mitchell OHara-Wild. Last updated 5 months ago.
300 stars 12.38 score 1.4k scripts 7 dependents
bioc
ggbio:Visualization tools for genomic data
The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries.
Maintained by Michael Lawrence. Last updated 5 months ago.
111 stars 12.26 score 734 scripts 17 dependentstidyverts
fabletools:Core Tools for Packages in the 'fable' Framework
Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.
Maintained by Mitchell OHara-Wild. Last updated 2 months ago.
91 stars 12.18 score 396 scripts 18 dependentstidymodels
workflowsets:Create a Collection of 'tidymodels' Workflows
A workflow is a combination of a model and preprocessors (e.g, a formula, recipe, etc.) (Kuhn and Silge (2021) <https://www.tmwr.org/>). In order to try different combinations of these, an object can be created that contains many workflows. There are functions to create workflows en masse as well as training them and visualizing the results.
Maintained by Simon Couch. Last updated 5 months ago.
94 stars 12.04 score 294 scripts 19 dependentstidymodels
stacks:Tidy Model Stacking
Model stacking is an ensemble technique that involves training a model to combine the outputs of many diverse statistical models, and has been shown to improve predictive performance in a variety of settings. 'stacks' implements a grammar for 'tidymodels'-aligned model stacking.
Maintained by Simon Couch. Last updated 5 months ago.
298 stars 11.46 score 840 scriptsbioc
ggcyto:Visualize Cytometry data with ggplot
With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot.
Maintained by Mike Jiang. Last updated 5 months ago.
immunooncologyflowcytometrycellbasedassaysinfrastructurevisualization
58 stars 11.25 score 362 scripts 5 dependents
bcgov
ssdtools:Species Sensitivity Distributions
Species sensitivity distributions are cumulative probability distributions which are fitted to toxicity concentrations for different species as described by Posthuma et al.(2001) <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to fit distributions such as the gamma, log-logistic, log-normal and log-normal log-normal mixture. Multiple distributions can be averaged using Akaike Information Criteria. Confidence intervals on hazard concentrations and proportions are produced by bootstrapping.
Maintained by Joe Thorley. Last updated 1 months ago.
ecotoxicologyenvspecies-sensitivity-distributioncpp
33 stars 10.33 score 111 scripts 5 dependents
dardisco
survMisc:Miscellaneous Functions for Survival Data
A collection of functions to help in the analysis of right-censored survival data. These extend the methods available in package:survival.
Maintained by Chris Dardis. Last updated 5 years ago.
1 stars 9.55 score 218 scripts 57 dependents
mlr-org
mlr3viz:Visualizations for 'mlr3'
Visualization package of the 'mlr3' ecosystem. It features plots for mlr3 objects such as tasks, learners, predictions, benchmark results, tuning instances and filters via the 'autoplot()' generic of 'ggplot2'. The package draws plots with the 'viridis' color palette and applies the minimal theme. Visualizations include barplots, boxplots, histograms, ROC curves, and Precision-Recall curves.
Maintained by Marc Becker. Last updated 5 months ago.
ggplot2mlr3visualizationvisualizations
45 stars 9.45 score 364 scripts 4 dependents
animint
animint2:Animated Interactive Grammar of Graphics
Functions are provided for defining animated, interactive data visualizations in R code, and rendering on a web page. The 2018 Journal of Computational and Graphical Statistics paper, <doi:10.1080/10618600.2018.1513367> describes the concepts implemented.
Maintained by Toby Hocking. Last updated 1 months ago.
64 stars 8.84 score 173 scriptstidymodels
tidyposterior:Bayesian Analysis to Compare Models using Resampling Statistics
Bayesian analysis used here to answer the question: "when looking at resampling results, are the differences between models 'real'?" To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) <https://jmlr.org/papers/v18/16-305.html>.
Maintained by Max Kuhn. Last updated 5 months ago.
102 stars 8.44 score 273 scriptsmlr-org
mlr3verse:Easily Install and Load the 'mlr3' Package Family
The 'mlr3' package family is a set of packages for machine-learning purposes built in a modular fashion. This wrapper package is aimed to simplify the installation and loading of the core 'mlr3' packages. Get more information about the 'mlr3' project at <https://mlr3book.mlr-org.com/>.
Maintained by Marc Becker. Last updated 2 months ago.
55 stars 8.32 score 720 scripts 1 dependentstidymodels
spatialsample:Spatial Resampling Infrastructure
Functions and classes for spatial resampling to use with the 'rsample' package, such as spatial cross-validation (Brenning, 2012) <doi:10.1109/IGARSS.2012.6352393>. The scope of 'rsample' and 'spatialsample' is to provide the basic building blocks for creating and analyzing resamples of a spatial data set, but neither package includes functions for modeling or computing statistics. The resampled spatial data sets created by 'spatialsample' do not contain much overhead in memory.
Maintained by Michael Mahoney. Last updated 6 months ago.
73 stars 8.19 score 118 scripts 2 dependents
hesim-dev
hesim:Health Economic Simulation Modeling and Decision Analysis
A modular and computationally efficient R package for parameterizing, simulating, and analyzing health economic simulation models. The package supports cohort discrete time state transition models (Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>, N-state partitioned survival models (Glasziou et al. 1990) <doi:10.1002/sim.4780091106>, and individual-level continuous time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>, encompassing both Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is quantified with standard graphical and tabular summaries of a probabilistic sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>, <doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table make individual-patient simulation, probabilistic sensitivity analysis, and incorporation of patient heterogeneity fast.
Maintained by Devin Incerti. Last updated 6 months ago.
health-economic-evaluationmicrosimulationsimulation-modelingcpp
67 stars 8.12 score 41 scripts
ifellows
OpenStreetMap:Access to Open Street Map Raster Images
Accesses high resolution raster maps using the OpenStreetMap protocol. Dozens of road, satellite, and topographic map servers are directly supported, including Apple, Mapnik, Bing, and stamen. Additionally raster maps may be constructed using custom tile servers. Maps can be plotted using either base graphics, or ggplot2. This package is not affiliated with the OpenStreetMap.org mapping project.
Maintained by Ian Fellows. Last updated 1 years ago.
11 stars 8.09 score 498 scripts 4 dependentsmlr-org
mlr3spatiotempcv:Spatiotemporal Resampling Methods for 'mlr3'
Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.
Maintained by Patrick Schratz. Last updated 4 months ago.
cross-validationmlr3resamplingresampling-methodsspatialtemporal
50 stars 8.09 score 123 scripts
ericmarcon
entropart:Entropy Partitioning to Measure Diversity
Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>. 'entropart' provides functions to calculate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity. Estimation-bias corrections are available.
Maintained by Eric Marcon. Last updated 2 months ago.
biodiversitydiversityentropy-partitioningestimatormeasurespecies
9 stars 7.81 score 115 scripts 1 dependentsbioc
hermes:Preprocessing, analyzing, and reporting of RNA-seq data
Provides classes and functions for quality control, filtering, normalization and differential expression analysis of pre-processed `RNA-seq` data. Data can be imported from `SummarizedExperiment` as well as `matrix` objects and can be annotated from `BioMart`. Filtering for genes without too low expression or containing required annotations, as well as filtering for samples with sufficient correlation to other samples or total number of reads is supported. The standard normalization methods including cpm, rpkm and tpm can be used, and 'DESeq2` as well as voom differential expression analyses are available.
Maintained by Daniel Sabanés Bové. Last updated 5 months ago.
rnaseqdifferentialexpressionnormalizationpreprocessingqualitycontrolrna-seqstatistical-engineering
11 stars 7.77 score 48 scripts 1 dependents
shikokuchuo
ichimoku:Visualization and Tools for Ichimoku Kinko Hyo Strategies
An implementation of 'Ichimoku Kinko Hyo', also commonly known as 'cloud charts'. Static and interactive visualizations with tools for creating, backtesting and development of quantitative 'ichimoku' strategies. As described in Sasaki (1996, ISBN:4925152009), the technique is a refinement on candlestick charting, originating from Japan and now in widespread use in technical analysis worldwide. Translating as 'one-glance equilibrium chart', it allows the price action and market structure of financial securities to be determined 'at-a-glance'. Incorporates an interface with the OANDA fxTrade API <https://developer.oanda.com/> for retrieving historical and live streaming price data for major currencies, metals, commodities, government bonds and stock indices.
Maintained by Charlie Gao. Last updated 14 days ago.
ichimokuichimoku-cloudoandaquantitative-finance
31 stars 7.73 score 34 scriptstidymodels
brulee:High-Level Modeling Functions with 'torch'
Provides high-level modeling functions to define and train models using the 'torch' R package. Models include linear, logistic, and multinomial regression as well as multilayer perceptrons.
Maintained by Max Kuhn. Last updated 9 days ago.
69 stars 7.56 score 214 scripts
idem-lab
conmat:Builds Contact Matrices using GAMs and Population Data
Builds contact matrices using GAMs and population data. This package incorporates data that is copyright Commonwealth of Australia (Australian Electoral Commission and Australian Bureau of Statistics) 2020.
Maintained by Nicholas Tierney. Last updated 19 days ago.
contact-matricesinfectious-diseasespopulation-datapublic-health
19 stars 7.21 score 47 scriptsrobjhyndman
vital:Tidy Analysis Tools for Mortality, Fertility, Migration and Population Data
Analysing vital statistics based on tools consistent with the tidyverse. Tools are provided for data visualization, life table calculations, computing net migration numbers, Lee-Carter modelling; functional data modelling and forecasting.
Maintained by Rob Hyndman. Last updated 1 days ago.
28 stars 7.20 score 18 scripts
mskcc-epi-bio
tidycmprsk:Competing Risks Estimation
Provides an intuitive interface for working with the competing risk endpoints. The package wraps the 'cmprsk' package, and exports functions for univariate cumulative incidence estimates and competing risk regression. Methods follow those introduced in Fine and Gray (1999) <doi:10.1002/sim.7501>.
Maintained by Daniel D. Sjoberg. Last updated 7 months ago.
23 stars 7.06 score 157 scripts 1 dependents
asael697
bayesforecast:Bayesian Time Series Modeling with Stan
Fit Bayesian time series models using 'Stan' for full Bayesian inference. A wide range of distributions and models are supported, allowing users to fit Seasonal ARIMA, ARIMAX, Dynamic Harmonic Regression, GARCH, t-student innovation GARCH models, asymmetric GARCH, Random Walks, stochastic volatility models for univariate time series. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their beliefs. Model fit can easily be assessed and compared with typical visualization methods, information criteria such as loglik, AIC, BIC WAIC, Bayes factor and leave-one-out cross-validation methods. References: Hyndman (2017) <doi:10.18637/jss.v027.i03>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.
Maintained by Asael Alonzo Matamoros. Last updated 1 years ago.
bayesian-inferenceforecasting-modelsmcmcstantime-series-analysiscpp
45 stars 6.92 score 62 scriptstidymodels
agua:'tidymodels' Integration with 'h2o'
Create and evaluate models using 'tidymodels' and 'h2o' <https://h2o.ai/>. The package enables users to specify 'h2o' as an engine for several modeling methods.
Maintained by Qiushi Yan. Last updated 10 months ago.
22 stars 6.88 score 80 scripts
kvasilopoulos
exuber:Econometric Analysis of Explosive Time Series
Testing for and dating periods of explosive dynamics (exuberance) in time series using the univariate and panel recursive unit root tests proposed by Phillips et al. (2015) <doi:10.1111/iere.12132> and Pavlidis et al. (2016) <doi:10.1007/s11146-015-9531-2>.The recursive least-squares algorithm utilizes the matrix inversion lemma to avoid matrix inversion which results in significant speed improvements. Simulation of a variety of periodically-collapsing bubble processes. Details can be found in Vasilopoulos et al. (2022) <doi:10.18637/jss.v103.i10>.
Maintained by Kostas Vasilopoulos. Last updated 1 years ago.
dickey-fullerexplosive-dynamicssimulationtime-seriesopenblascpp
29 stars 6.83 score 77 scriptsericmarcon
dbmss:Distance-Based Measures of Spatial Structures
Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, following Marcon, Traissac, Puech, and Lang (2015) <doi:10.18637/jss.v067.c03>. Includes classical functions (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). Relies on 'spatstat' for some core calculation.
Maintained by Eric Marcon. Last updated 1 months ago.
concentrationeconomic-geographyspatial-structuresspecializationcpp
9 stars 6.53 score 42 scripts 1 dependents
yulab-smu
tidydr:Unify Dimensionality Reduction Results
Dimensionality reduction (DR) is widely used in many domain for analyzing and visualizing high-dimensional data. 'tidydr' provides uniform output and is compatible with multiple methods, including 'prcomp', 'mds', 'Rtsne'. etc.
Maintained by Guangchuang Yu. Last updated 1 years ago.
14 stars 6.47 score 71 scripts 1 dependents
nlmixr2
nonmem2rx:Converts 'NONMEM' Models to 'rxode2'
'NONMEM' has been a tool for running nonlinear mixed effects models since the 80s and is still used today (Bauer 2019 <doi:10.1002/psp4.12404>). This tool allows you to convert 'NONMEM' models to 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) and with simple models 'nlmixr2' syntax (Fidler et al (2019) <doi:10.1002/psp4.12445>). The 'nlmixr2' syntax requires the residual specification to be included and it is not always translated. If available, the 'rxode2' model will read in the 'NONMEM' data and compare the simulation for the population model ('PRED') individual model ('IPRED') and residual model ('IWRES') to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'NONMEM' and can convert the objects converted from 'nonmem2rx' to a full 'nlmixr2' fit.
Maintained by Matthew Fidler. Last updated 4 months ago.
nlmixr2nonmempharmacometricsrxode2cpp
12 stars 6.46 score 23 scripts 1 dependents
ygeunkim
bvhar:Bayesian Vector Heterogeneous Autoregressive Modeling
Tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.
Maintained by Young Geun Kim. Last updated 29 days ago.
bayesianbayesian-econometricsbvareigenforecastingharpybind11pythonrcppeigentime-seriesvector-autoregressioncppopenmp
6 stars 6.42 score 25 scripts
mattheaphy
actxps:Create Actuarial Experience Studies: Prepare Data, Summarize Results, and Create Reports
Experience studies are used by actuaries to explore historical experience across blocks of business and to inform assumption setting activities. This package provides functions for preparing data, creating studies, visualizing results, and beginning assumption development. Experience study methods, including exposure calculations, are described in: Atkinson & McGarry (2016) "Experience Study Calculations" <https://www.soa.org/49378a/globalassets/assets/files/research/experience-study-calculations.pdf>. The limited fluctuation credibility method used by the 'exp_stats()' function is described in: Herzog (1999, ISBN:1-56698-374-6) "Introduction to Credibility Theory".
Maintained by Matt Heaphy. Last updated 3 months ago.
14 stars 6.38 score 23 scripts
ropensci
predictNMB:Evaluate Clinical Prediction Models by Net Monetary Benefit
Estimates when and where a model-guided treatment strategy may outperform a treat-all or treat-none approach by Monte Carlo simulation and evaluation of the Net Monetary Benefit. Details can be viewed in Parsons et al. (2023) <doi:10.21105/joss.05328>.
Maintained by Rex Parsons. Last updated 8 months ago.
10 stars 6.23 score 17 scripts
pachadotdev
capybara:Fast and Memory Efficient Fitting of Linear Models with High-Dimensional Fixed Effects
Fast and user-friendly estimation of generalized linear models with multiple fixed effects and cluster the standard errors. The method to obtain the estimated fixed-effects coefficients is based on Stammann (2018) <doi:10.48550/arXiv.1707.01815> and Gaure (2013) <doi:10.1016/j.csda.2013.03.024>.
Maintained by Mauricio Vargas Sepulveda. Last updated 4 days ago.
cpp11econometricslinear-modelsopenblascppopenmp
13 stars 6.07 score
mharinga
insurancerating:Analytic Insurance Rating Techniques
Functions to build, evaluate, and visualize insurance rating models. It simplifies the process of modeling premiums, and allows to analyze insurance risk factors effectively. The package employs a data-driven strategy for constructing insurance tariff classes, drawing on the work of Antonio and Valdez (2012) <doi:10.1007/s10182-011-0152-7>.
Maintained by Martin Haringa. Last updated 5 months ago.
actuarialactuarial-scienceinsurancepricing
70 stars 5.89 score 28 scripts
evolecolgroup
tidypopgen:Tidy Population Genetics
We provide a tidy grammar of population genetics, facilitating the manipulation and analysis of data on biallelic single nucleotide polymorphisms (SNPs). `tidypopgen` scales to very large genetic datasets by storing genotypes on disk, and performing operations on them in chunks, without ever loading all data in memory.
Maintained by Andrea Manica. Last updated 7 days ago.
4 stars 5.84 score 8 scripts
gavinsimpson
ggvegan:'ggplot2' Plots for the 'vegan' Package
Functions to produce ggplot2-based plots of objects produced by functions in the vegan package.
Maintained by Gavin L. Simpson. Last updated 12 days ago.
115 stars 5.79 score 271 scriptsropensci
GLMMcosinor:Fit a Cosinor Model Using a Generalized Mixed Modeling Framework
Allows users to fit a cosinor model using the 'glmmTMB' framework. This extends on existing cosinor modeling packages, including 'cosinor' and 'circacompare', by including a wide range of available link functions and the capability to fit mixed models. The cosinor model is described by Cornelissen (2014) <doi:10.1186/1742-4682-11-16>.
Maintained by Rex Parsons. Last updated 5 months ago.
1 stars 5.77 score 22 scripts
sylvainschmitt
rcontroll:Individual-Based Forest Growth Simulator 'TROLL'
'TROLL' is coded in C++ and it typically simulates hundreds of thousands of individuals over hundreds of years. The 'rcontroll' R package is a wrapper of 'TROLL'. 'rcontroll' includes functions that generate inputs for simulations and run simulations. Finally, it is possible to analyse the 'TROLL' outputs through tables, figures, and maps taking advantage of other R visualisation packages. 'rcontroll' also offers the possibility to generate a virtual LiDAR point cloud that corresponds to a snapshot of the simulated forest.
Maintained by Sylvain Schmitt. Last updated 6 months ago.
5 stars 5.76 score 19 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.
17 stars 5.74 score 18 scripts
berrij
profoc:Probabilistic Forecast Combination Using CRPS Learning
Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <doi:10.48550/arXiv.2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <doi:10.48550/arXiv.1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.
Maintained by Jonathan Berrisch. Last updated 6 months ago.
14 stars 5.74 score 13 scripts
sestelo
npregfast:Nonparametric Estimation of Regression Models with Factor-by-Curve Interactions
A method for obtaining nonparametric estimates of regression models with or without factor-by-curve interactions using local polynomial kernel smoothers or splines. Additionally, a parametric model (allometric model) can be estimated.
Maintained by Marta Sestelo. Last updated 3 months ago.
allometricbarnaclecritical-pointscurve-interactionsfactor-by-curvefortraninteractionnonparametricregression-modelstesting
5 stars 5.73 score 89 scripts 2 dependents
akai01
caretForecast:Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms
Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively.
Maintained by Resul Akay. Last updated 2 years ago.
caretconformal-predictiondata-scienceeconometricsforecastforecastingforecasting-modelsmachine-learningmacroeconometricsmicroeconometricstime-seriestime-series-forcastingtime-series-prediction
25 stars 5.62 score 28 scripts 4 dependents
ucd-serg
serocalculator:Estimating Infection Rates from Serological Data
Translates antibody levels measured in cross-sectional population samples into estimates of the frequency with which seroconversions (infections) occur in the sampled populations. Replaces the previous `seroincidence` package.
Maintained by Kristina Lai. Last updated 4 days ago.
epidemiologyincidence-estimationseroepidemiology
6 stars 5.61 score 13 scripts
mpjashby
sfhotspot:Hot-Spot Analysis with Simple Features
Identify and understand clusters of points (typically representing the locations of places or events) stored in simple-features (SF) objects. This is useful for analysing, for example, hot-spots of crime events. The package emphasises producing results from point SF data in a single step using reasonable default values for all other arguments, to aid rapid data analysis by users who are starting out. Functions available include kernel density estimation (for details, see Yip (2020) <doi:10.22224/gistbok/2020.1.12>), analysis of spatial association (Getis and Ord (1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>) and hot-spot classification (Chainey (2020) ISBN:158948584X).
Maintained by Matt Ashby. Last updated 1 months ago.
hotspothotspotshotspots-analysismappingmapping-tools
12 stars 5.56 score 30 scripts
noramvillanueva
clustcurv:Determining Groups in Multiples Curves
A method for determining groups in multiple curves with an automatic selection of their number based on k-means or k-medians algorithms. The selection of the optimal number is provided by bootstrap methods. The methodology can be applied both in regression and survival framework. Implemented methods are: Grouping multiple survival curves described by Villanueva et al. (2018) <doi:10.1002/sim.8016>.
Maintained by Nora M. Villanueva. Last updated 5 months ago.
clusteringdata-analyticsmachinelearningmultiple-curvesnonparametric-statisticsnumber-of-clustersregressionsurvival-analysis
3 stars 5.53 score 38 scripts
vpnsctl
mixpoissonreg:Mixed Poisson Regression for Overdispersed Count Data
Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.
Maintained by Alexandre B. Simas. Last updated 4 years ago.
count-datadiagnosticsinfluence-analysislocal-influencenegative-binomial-regressionpoisson-inverse-gaussian-regression
3 stars 5.44 score 23 scripts
inesortega
neuralGAM:Interpretable Neural Network Based on Generalized Additive Models
Neural network framework based on Generalized Additive Models from Hastie & Tibshirani (1990, ISBN:9780412343902), which trains a different neural network to estimate the contribution of each feature to the response variable. The networks are trained independently leveraging the local scoring and backfitting algorithms to ensure that the Generalized Additive Model converges and it is additive. The resultant Neural Network is a highly accurate and interpretable deep learning model, which can be used for high-risk AI practices where decision-making should be based on accountable and interpretable algorithms.
Maintained by Ines Ortega-Fernandez. Last updated 7 months ago.
deep-neural-networksexplainable-aigamganngeneralized-additive-modelsgeneralized-additive-neural-networkself-explanatory-mlxai
2 stars 5.44 score 40 scripts
mcanouil
NACHO:NanoString Quality Control Dashboard
NanoString nCounter data are gene expression assays where there is no need for the use of enzymes or amplification protocols and work with fluorescent barcodes (Geiss et al. (2018) <doi:10.1038/nbt1385>). Each barcode is assigned a messenger-RNA/micro-RNA (mRNA/miRNA) which after bonding with its target can be counted. As a result each count of a specific barcode represents the presence of its target mRNA/miRNA. 'NACHO' (NAnoString quality Control dasHbOard) is able to analyse the exported NanoString nCounter data and facilitates the user in performing a quality control. 'NACHO' does this by visualising quality control metrics, expression of control genes, principal components and sample specific size factors in an interactive web application.
Maintained by Mickaël Canouil. Last updated 1 years ago.
mirnamrnananostringnormalisationquality-controlshiny
8 stars 5.41 score 32 scripts
gitdemont
IFC:Tools for Imaging Flow Cytometry
Contains several tools to treat imaging flow cytometry data from 'ImageStream®' and 'FlowSight®' cytometers ('Amnis®' 'Cytek®'). Provides an easy and simple way to read and write .fcs, .rif, .cif and .daf files. Information such as masks, features, regions and populations set within these files can be retrieved for each single cell. In addition, raw data such as images stored can also be accessed. Users, may hopefully increase their productivity thanks to dedicated functions to extract, visualize, manipulate and export 'IFC' data. Toy data example can be installed through the 'IFCdata' package of approximately 32 MB, which is available in a 'drat' repository <https://gitdemont.github.io/IFCdata/>. See file 'COPYRIGHTS' and file 'AUTHORS' for a list of copyright holders and authors.
Maintained by Yohann Demont. Last updated 22 days ago.
cytometrycytometry-dataflowflow-cytometryflow-cytometry-analysisflow-cytometry-dataflow-cytometry-filesifcimageimaging-flow-cytometryimaging-flow-cytometry-datamicroscopycpp
4 stars 5.34 score 12 scripts
openplantpathology
hagis:Analysis of Plant Pathogen Pathotype Complexities, Distributions and Diversity
Analysis of plant pathogen pathotype survey data. Functions provided calculate distribution of susceptibilities, distribution of complexities with statistics, pathotype frequency distribution, as well as diversity indices for pathotypes. This package is meant to be a direct replacement for Herrmann, Löwer and Schachtel's (1999) <doi:10.1046/j.1365-3059.1999.00325.x> Habgood-Gilmour Spreadsheet, 'HaGiS', previously used for pathotype analysis.
Maintained by Adam H. Sparks. Last updated 23 days ago.
plant-pathologypathotypepathogen-surveyvirulence analysisdifferential setassessment scalepathotype-complexitiesplant-diseasepopulation-diversities
1 stars 5.26 score 8 scripts
biometryhub
biometryassist:Functions to Assist Design and Analysis of Agronomic Experiments
Provides functions to aid in the design and analysis of agronomic and agricultural experiments through easy access to documentation and helper functions, especially for users who are learning these concepts. While not required for most functionality, this package enhances the `asreml` package which provides a computationally efficient algorithm for fitting mixed models using Residual Maximum Likelihood. It is a commercial package that can be purchased as 'asreml-R' from 'VSNi' <https://vsni.co.uk/>, who will supply a zip file for local installation/updating (see <https://asreml.kb.vsni.co.uk/>).
Maintained by Sam Rogers. Last updated 10 months ago.
biometryexperimental-designteaching
8 stars 5.20 score 8 scripts
rudeboybert
forestecology:Fitting and Assessing Neighborhood Models of the Effect of Interspecific Competition on the Growth of Trees
Code for fitting and assessing models for the growth of trees. In particular for the Bayesian neighborhood competition linear regression model of Allen (2020): methods for model fitting and generating fitted/predicted values, evaluating the effect of competitor species identity using permutation tests, and evaluating model performance using spatial cross-validation.
Maintained by Albert Y. Kim. Last updated 3 years ago.
12 stars 5.12 score 11 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 dependentssevvandi
dobin:Dimension Reduction for Outlier Detection
A dimension reduction technique for outlier detection. DOBIN: a Distance based Outlier BasIs using Neighbours, constructs a set of basis vectors for outlier detection. This is not an outlier detection method; rather it is a pre-processing method for outlier detection. It brings outliers to the fore-front using fewer basis vectors (Kandanaarachchi, Hyndman 2020) <doi:10.1080/10618600.2020.1807353>.
Maintained by Sevvandi Kandanaarachchi. Last updated 3 years ago.
detectiondimensionoutlierreduction
13 stars 4.89 score 12 scriptsericmarcon
divent:Entropy Partitioning to Measure Diversity
Measurement and partitioning of diversity, based on Tsallis entropy, following Marcon and Herault (2015) <doi:10.18637/jss.v067.i08>. 'divent' provides functions to estimate alpha, beta and gamma diversity of communities, including phylogenetic and functional diversity.
Maintained by Eric Marcon. Last updated 13 hours ago.
1 stars 4.81 score 1 scripts
wahani
saeSim:Simulation Tools for Small Area Estimation
Tools for the simulation of data in the context of small area estimation. Combine all steps of your simulation - from data generation over drawing samples to model fitting - in one object. This enables easy modification and combination of different scenarios. You can store your results in a folder or start the simulation in parallel.
Maintained by Sebastian Warnholz. Last updated 3 years ago.
3 stars 4.72 score 35 scripts
evalclass
prcbench:Testing Workbench for Precision-Recall Curves
A testing workbench to evaluate tools that calculate precision-recall curves. Saito and Rehmsmeier (2015) <doi:10.1371/journal.pone.0118432>.
Maintained by Takaya Saito. Last updated 2 years ago.
5 stars 4.72 score 21 scriptssevvandi
stxplore:Exploration of Spatio-Temporal Data
A set of statistical tools for spatio-temporal data exploration. Includes simple plotting functions, covariance calculations and computations similar to principal component analysis for spatio-temporal data. Can use both dataframes and stars objects for all plots and computations. For more details refer 'Spatio-Temporal Statistics with R' (Christopher K. Wikle, Andrew Zammit-Mangion, Noel Cressie, 2019, ISBN:9781138711136).
Maintained by Sevvandi Kandanaarachchi. Last updated 2 years ago.
5 stars 4.70 score 7 scripts
tbalan
frailtyEM:Fitting Frailty Models with the EM Algorithm
Contains functions for fitting shared frailty models with a semi-parametric baseline hazard with the Expectation-Maximization algorithm. Supported data formats include clustered failures with left truncation and recurrent events in gap-time or Andersen-Gill format. Several frailty distributions, such as the the gamma, positive stable and the Power Variance Family are supported.
Maintained by Theodor Adrian Balan. Last updated 6 years ago.
frailtyheterogeneitysurvivalcpp
3 stars 4.69 score 33 scripts
bonsook
ycevo:Nonparametric Estimation of the Yield Curve Evolution
Nonparametric estimation of discount functions and yield curves from transaction data of coupon paying bonds. Koo, B., La Vecchia, D., & Linton, O. B. (2021) <doi:10.1016/j.jeconom.2020.04.014> describe an application of this package using the Center for Research in Security Prices (CRSP) Bond Data and document its implementation.
Maintained by Yangzhuoran Fin Yang. Last updated 9 months ago.
7 stars 4.45 score 3 scriptsnlmixr2
monolix2rx:Converts 'Monolix' Models to 'rxode2'
'Monolix' is a tool for running mixed effects model using 'saem'. This tool allows you to convert 'Monolix' models to 'rxode2' (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) using the form compatible with 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>). If available, the 'rxode2' model will read in the 'Monolix' data and compare the simulation for the population model individual model and residual model to immediately show how well the translation is performing. This saves the model development time for people who are creating an 'rxode2' model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a 'rxode2' model. This is complementary to the 'babelmixr2' package that translates 'nlmixr2' models to 'Monolix' and can convert the objects converted from 'monolix2rx' to a full 'nlmixr2' fit. While not required, you can get/install the 'lixoftConnectors' package in the 'Monolix' installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When 'lixoftConnectors' is available, 'Monolix' can be used to load its model library instead manually setting up text files (which only works with old versions of 'Monolix').
Maintained by Matthew Fidler. Last updated 4 months ago.
monolixnlmixr2pharmacometricsrxode2cpp
1 stars 4.40 score 14 scripts 1 dependents
forestgeo
fgeo.plot:Plot ForestGEO Data
To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to plot ForestGEO data. To learn more about ForestGEO visit <https://forestgeo.si.edu/>.
Maintained by Mauro Lepore. Last updated 3 years ago.
4 stars 4.26 score 5 scripts 1 dependents
manuhuth
coconots:Convolution-Closed Models for Count Time Series
Useful tools for fitting, validating, and forecasting of practical convolution-closed time series models for low counts are provided. Marginal distributions of the data can be modelled via Poisson and Generalized Poisson innovations. Regression effects can be incorporated through time varying innovation rates. The models are described in Jung and Tremayne (2011) <doi:10.1111/j.1467-9892.2010.00697.x> and the model assessment tools are presented in Czado et al. (2009) <doi:10.1111/j.1541-0420.2009.01191.x> and, Tsay (1992) <doi:10.2307/2347612>.
Maintained by Manuel Huth. Last updated 1 days ago.
3 stars 4.26 score 4 scripts
rdinnager
phyf:Phylogenetic Flow Objects for Easy Manipulation and Modelling of Data on Phylogenetic Trees and Graphs
The {phyf} package implements a tibble and vctrs based object for storing phylogenetic trees along with data. It is fast and flexible and directly produces data structures useful for phylogenetic modelling in the {fibre} package.
Maintained by Russell Dinnage. Last updated 7 months ago.
1 stars 4.20 score 53 scripts 1 dependentsbioc
ggtreeDendro:Drawing 'dendrogram' using 'ggtree'
Offers a set of 'autoplot' methods to visualize tree-like structures (e.g., hierarchical clustering and classification/regression trees) using 'ggtree'. You can adjust graphical parameters using grammar of graphic syntax and integrate external data to the tree.
Maintained by Guangchuang Yu. Last updated 5 months ago.
clusteringclassificationdecisiontreephylogeneticsvisualization
4.18 score 10 scriptssevvandi
airt:Evaluation of Algorithm Collections Using Item Response Theory
An evaluation framework for algorithm portfolios using Item Response Theory (IRT). We use continuous and polytomous IRT models to evaluate algorithms and introduce algorithm characteristics such as stability, effectiveness and anomalousness (Kandanaarachchi, Smith-Miles 2020) <doi:10.13140/RG.2.2.11363.09760>.
Maintained by Sevvandi Kandanaarachchi. Last updated 1 years ago.
4.18 score 8 scripts 1 dependents
fradenti
intRinsic:Likelihood-Based Intrinsic Dimension Estimators
Provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the 'TWO-NN' and 'Gride' estimators and the 'Hidalgo' Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the 'TWO-NN' and 'Hidalgo' models. References: Denti (2023, <doi:10.18637/jss.v106.i09>); Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>); Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).
Maintained by Francesco Denti. Last updated 7 months ago.
13 stars 4.11 score 10 scripts
kaigu1990
mcradds:Processing and Analyzing of Diagnostics Trials
Provides methods and functions to analyze the quantitative or qualitative performance for diagnostic assays, and outliers detection, reader precision and reference range are discussed. Most of the methods and algorithms refer to CLSI (Clinical & Laboratory Standards Institute) recommendations and NMPA (National Medical Products Administration) guidelines. In additional, relevant plots are constructed by 'ggplot2'.
Maintained by Kai Gu. Last updated 7 months ago.
1 stars 4.00 score 7 scripts
aijordan
reliabilitydiag:Reliability Diagrams Using Isotonic Regression
Checking the reliability of predictions via the CORP approach, which generates provably statistically 'C'onsistent, 'O'ptimally binned, and 'R'eproducible reliability diagrams using the 'P'ool-adjacent-violators algorithm. See Dimitriadis, Gneiting, Jordan (2021) <doi:10.1073/pnas.2016191118>.
Maintained by Alexander I. Jordan. Last updated 3 years ago.
8 stars 3.88 score 19 scripts
marius-cp
calibrationband:Calibration Bands
Package to assess the calibration of probabilistic classifiers using confidence bands for monotonic functions. Besides testing the classical goodness-of-fit null hypothesis of perfect calibration, the confidence bands calculated within that package facilitate inverted goodness-of-fit tests whose rejection allows for a sought-after conclusion of a sufficiently well-calibrated model. The package creates flexible graphical tools to perform these tests. For construction details see also Dimitriadis, Dümbgen, Henzi, Puke, Ziegel (2022) <arXiv:2203.04065>.
Maintained by Marius Puke. Last updated 3 years ago.
11 stars 3.74 score 10 scripts
ssi-dk
aeddo:Automated and Early Detection of Disease Outbreaks
A powerful tool for automating the early detection of disease outbreaks in time series data. 'aeddo' employs advanced statistical methods, including hierarchical models, in an innovative manner to effectively characterize outbreak signals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to disease outbreaks in a timely fashion. For a detailed reference on hierarchical models, consult Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.
Maintained by Lasse Engbo Christiansen. Last updated 1 years ago.
1 stars 3.70 score 2 scriptsasael697
nortsTest:Assessing Normality of Stationary Process
Despite that several tests for normality in stationary processes have been proposed in the literature, consistent implementations of these tests in programming languages are limited. Seven normality test are implemented. The asymptotic Lobato & Velasco's, asymptotic Epps, Psaradakis and Vávra, Lobato & Velasco's and Epps sieve bootstrap approximations, El bouch et al., and the random projections tests for univariate stationary process. Some other diagnostics such as, unit root test for stationarity, seasonal tests for seasonality, and arch effect test for volatility; are also performed. Additionally, the El bouch test performs normality tests for bivariate time series. The package also offers residual diagnostic for linear time series models developed in several packages.
Maintained by Asael Alonzo Matamoros. Last updated 1 years ago.
3 stars 3.69 score 33 scriptsakai01
ngboostForecast:Probabilistic Time Series Forecasting
Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.
Maintained by Resul Akay. Last updated 3 years ago.
forecastingmachine-learningngboostngboost-forecastprobabilistic-forecastspythonsklearntime-series
7 stars 3.69 score 14 scripts
alfrzlp
saens:Small Area Estimation with Cluster Information for Estimation of Non-Sampled Areas
Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) <doi:10.9790/5728-10121519>.
Maintained by Ridson Al Farizal P. Last updated 4 months ago.
2 stars 3.62 score 14 scriptsaijordan
triptych:Diagnostic Graphics to Evaluate Forecast Performance
Overall predictive performance is measured by a mean score (or loss), which decomposes into miscalibration, discrimination, and uncertainty components. The main focus is visualization of these distinct and complementary aspects in joint displays. See Dimitriadis, Gneiting, Jordan, Vogel (2024) <doi:10.1016/j.ijforecast.2023.09.007>.
Maintained by Alexander I. Jordan. Last updated 10 months ago.
3.28 score 19 scripts
certe-medical-epidemiology
certestats:A Certe R Package for Statistical Modelling
A Certe R Package for early-warning, applying statistical modelling (such as creating machine learning models), QC rules and distribution analysis. This package is part of the 'certedata' universe.
Maintained by Matthijs S. Berends. Last updated 5 months ago.
3.02 score 1 scripts 1 dependents
tmsalab
edina:Bayesian Estimation of an Exploratory Deterministic Input, Noisy and Gate Model
Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) <doi:10.1007/s11336-017-9579-4>.
Maintained by James Joseph Balamuta. Last updated 5 years ago.
cognitive-diagnostic-modelscppdinaecdmitem-response-theorypsychometricsrcpparmadilloopenblascpp
2 stars 3.00 score 1 scriptssestelo
survidm:Inference and Prediction in an Illness-Death Model
Newly developed methods for the estimation of several probabilities in an illness-death model. The package can be used to obtain nonparametric and semiparametric estimates for: transition probabilities, occupation probabilities, cumulative incidence function and the sojourn time distributions. Additionally, it is possible to fit proportional hazards regression models in each transition of the Illness-Death Model. Several auxiliary functions are also provided which can be used for marginal estimation of the survival functions.
Maintained by Marta Sestelo. Last updated 4 years ago.
1 stars 2.95 score 18 scripts
tuomaseerola
movementsync:Analysis and Visualisation of Musical Audio and Video Movement Synchrony Data
Analysis and visualisation of synchrony, interaction, and joint movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and Tarsitani (2021) <doi:10.17605/OSF.IO/KS325>, while example analyses can be found in Clayton, Jakubowski, and Eerola (2019) <doi:10.1177/1029864919844809>. Additionally, wavelet analysis techniques have been applied to examine movement-related musical interactions, as shown in Eerola et al. (2018) <doi:10.1098/rsos.171520>.
Maintained by Tuomas Eerola. Last updated 1 years ago.
1 stars 2.85 score 14 scripts
adrincont
BMTAR:Bayesian Approach for MTAR Models with Missing Data
Implements parameter estimation using a Bayesian approach for Multivariate Threshold Autoregressive (MTAR) models with missing data using Markov Chain Monte Carlo methods. Performs the simulation of MTAR processes (mtarsim()), estimation of matrix parameters and the threshold values (mtarns()), identification of the autoregressive orders using Bayesian variable selection (mtarstr()), identification of the number of regimes using Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data, coefficients and covariance matrices conditional on the autoregressive orders, the threshold values and the number of regimes (mtarmissing()). Calderon and Nieto (2017) <doi:10.1080/03610926.2014.990758>.
Maintained by Andrey Duvan Rincon Torres. Last updated 3 years ago.
1 stars 2.70 score 2 scriptsalfrzlp
saeHB.unit:Basic Unit Level Model using Hierarchical Bayesian Approach
Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) <doi:10.1080/01621459.1988.10478561>.
Maintained by Ridson Al Farizal P. Last updated 1 years ago.
1 stars 2.70 score 1 scripts
