Referenced by ⇅
Year
cattonum : Encode Categorical Features (Version 0.0.5)
2018
DriveML : Self-Drive Machine Learning Projects (Version 0.1.5)
2020
dynfeature : Feature Importance for Dynamic Processes (Version 1.0.1)
2021
ensModelVis : Visualisations for Model Ensembles (Version 0.1.0)
2022
healthcareai : Tools for Healthcare Machine Learning (Version 2.5.1)
2017
modelplotr : Plots to Evaluate the Business Performance of Predictive Models (Version 1.1.0)
2019
piRF : Prediction Intervals for Random Forests (Version 0.1.0)
2020
SPOT : Sequential Parameter Optimization Toolbox (Version 2.11.14)
2010
tbma : Tree-Based Moving Average Forecasting Model (Version 0.1.0)
2020
treemisc : Data Sets and Functions to Accompany "Tree-Based Methods for
Statistical Learning in R" (Version 0.0.1)
2022
concrete : Continuous-Time Competing Risks Estimation using Targeted
Minimum Loss-Based Estimation (TMLE) (Version 1.0.5)
2023
viruslearner : Ensemble Learning for HIV-Related Metrics (Version 0.0.2)
2024
mllrnrs : R6-Based ML Learners for 'mlexperiments' (Version 0.0.3)
2023
mlmts : Machine Learning Algorithms for Multivariate Time Series (Version 1.1.1)
2022
mlr : Machine Learning in R (Version 2.19.1)
2013
mlr3fairness : Fairness Auditing and Debiasing for 'mlr3' (Version 0.3.2)
2022
mlr3learners : Recommended Learners for 'mlr3' (Version 0.6.0)
2019
mlr3mbo : Flexible Bayesian Optimization (Version 0.2.2)
2022
mlr3shiny : Machine Learning in 'shiny' with 'mlr3' (Version 0.3.0)
2020
mlr3spatial : Support for Spatial Objects Within the 'mlr3' Ecosystem (Version 0.5.0)
2021
mlr3summary : Model and Learner Summaries for 'mlr3' (Version 0.1.0)
2024
mlr3tuningspaces : Search Spaces for 'mlr3' (Version 0.5.0)
2021
mlrCPO : Composable Preprocessing Operators and Pipelines for Machine Learning (Version 0.3.7-7)
2018
mlr3viz : Visualizations for 'mlr3' (Version 0.8.0)
2020
mlrintermbo : Model-Based Optimization for 'mlr3' Through 'mlrMBO' (Version 0.5.0)
2021
ADAPTS : Automated Deconvolution Augmentation of Profiles for Tissue Specific Cells (Version 1.0.22)
2019
AmpGram : Prediction of Antimicrobial Peptides (Version 1.0)
2020
AmyloGram : Prediction of Amyloid Proteins (Version 1.1)
2016
Bodi : Boosting Diversity in Regression Ensembles (Version 0.1.0)
2022
Boruta : Wrapper Algorithm for All Relevant Feature Selection (Version 8.0.0)
2009
C443 : See a Forest for the Trees (Version 3.4.0)
2018
CALIBERrfimpute : Multiple Imputation Using MICE and Random Forest (Version 1.0-7)
2013
CancerGram : Prediction of Anticancer Peptides (Version 1.0.0)
2020
CaseBasedReasoning : Case Based Reasoning (Version 0.3)
2018
CausalGPS : Matching on Generalized Propensity Scores with Continuous Exposures (Version 0.4.2)
2021
CompositionalML : Machine Learning with Compositional Data (Version 1.0)
2024
CornerstoneR : Collection of Scripts for Interface Between 'Cornerstone' and 'R' (Version 2.0.2)
2018
CoxAIPW : Doubly Robust Inference for Cox Marginal Structural Model with Informative Censoring (Version 0.0.3)
2023
DALEX : moDel Agnostic Language for Exploration and eXplanation (Version 2.4.3)
2018
DALEXtra : Extension for 'DALEX' Package (Version 2.3.0)
2019
DoubleML : Double Machine Learning in R (Version 1.0.0)
2020
ENMTools : Analysis of Niche Evolution using Niche and Distribution Models (Version 1.1.2)
2020
GRSxE : Testing Gene-Environment Interactions Through Genetic Risk Scores (Version 1.0.1)
2022
GenericML : Generic Machine Learning Inference (Version 0.2.2)
2021
HPLB : High-Probability Lower Bounds for the Total Variance Distance (Version 1.0.0)
2020
Infusion : Inference Using Simulation (Version 2.1.0)
2016
Iscores : Proper Scoring Rules for Missing Value Imputation (Version 1.1.0)
2021
MDEI : Implementing the Method of Direct Estimation and Inference (Version 1.0)
2023
MLDataR : Collection of Machine Learning Datasets for Supervised Machine Learning (Version 1.0.1)
2022
MLFS : Machine Learning Forest Simulator (Version 0.4.2)
2022
MSiP : 'MassSpectrometry' Interaction Prediction (Version 1.3.7)
2021
MachineShop : Machine Learning Models and Tools (Version 3.7.0)
2018
OOBCurve : Out of Bag Learning Curve (Version 0.3)
2017
OSTE : Optimal Survival Trees Ensemble (Version 1.0)
2021
OptHoldoutSize : Estimation of Optimal Size for a Holdout Set for Updating a Predictive Score (Version 0.1.0.0)
2022
PKLMtest : Classification Based MCAR Test (Version 1.0.1)
2021
PieGlyph : Axis Invariant Scatter Pie Plots (Version 0.1.0)
2023
RFlocalfdr : Significance Level for Random Forest Impurity Importance Scores (Version 0.8.5)
2023
RFpredInterval : Prediction Intervals with Random Forests and Boosted Forests (Version 1.0.8)
2021
RaSEn : Random Subspace Ensemble Classification and Variable Screening (Version 3.0.0)
2020
RfEmpImp : Multiple Imputation using Chained Random Forests (Version 2.1.8)
2020
RobinCar : Robust Estimation and Inference in Covariate-Adaptive Randomization (Version 0.2.0)
2024
SAiVE : Functions Used for SAiVE Group Research, Collaborations, and Publications (Version 1.0.4)
2024
SCORPIUS : Inferring Developmental Chronologies from Single-Cell RNA Sequencing Data (Version 1.0.9)
2018
SISIR : Select Intervals Suited for Functional Regression (Version 0.2.2)
2020
SPARRAfairness : Analysis of Differential Behaviour of SPARRA Score Across Demographic Groups (Version 0.0.0.1)
2023
SSLR : Semi-Supervised Classification, Regression and Clustering Methods (Version 0.9.3.3)
2020
SpatialML : Spatial Machine Learning (Version 0.1.7)
2019
StratifiedMedicine : Stratified Medicine (Version 1.0.5)
2019
SuperLearner : Super Learner Prediction (Version 2.0-29)
2011
TSCI : Tools for Causal Inference with Possibly Invalid Instrumental Variables (Version 3.0.4)
2022
TangledFeatures : Feature Selection in Highly Correlated Spaces (Version 0.1.1)
2023
VIM : Visualization and Imputation of Missing Values (Version 6.2.2)
2012
VIMPS : Calculate Variable Importance with Knock Off Variables (Version 1.0)
2024
VSURF : Variable Selection Using Random Forests (Version 1.2.0)
2013
abcrf : Approximate Bayesian Computation via Random Forests (Version 1.9)
2015
alookr : Model Classifier for Binary Classification (Version 0.3.9)
2020
arf : Adversarial Random Forests (Version 0.2.0)
2022
arenar : Arena for the Exploration and Comparison of any ML Models (Version 0.2.0)
2020
batchtools : Tools for Computation on Batch Systems (Version 0.9.17)
2016
breakDown : Model Agnostic Explainers for Individual Predictions (Version 0.2.2)
2018
butcher : Model Butcher (Version 0.3.4)
2019
causalweight : Estimation Methods for Causal Inference Based on Inverse Probability Weighting (Version 1.1.0)
2018
cdgd : Causal Decomposition of Group Disparities (Version 0.3.5)
2023
collinear : Seamless Multicollinearity Management (Version 1.1.1)
2023
comets : Covariance Measure Tests for Conditional Independence (Version 0.0-1)
2024
confcons : Confidence and Consistency of Predictive Distribution Models (Version 0.3.1)
2024
corrgrapher : Explore Correlations Between Variables in a Machine Learning Model (Version 1.0.4)
2020
cpi : Conditional Predictive Impact (Version 0.1.4)
2022
crossurr : Cross-Fitting for Doubly Robust Evaluation of High-Dimensional Surrogate Markers (Version 1.0.6)
2022
ddecompose : Detailed Distributional Decomposition (Version 1.0.0)
2024
ddml : Double/Debiased Machine Learning (Version 0.2.0)
2023
discSurv : Discrete Time Survival Analysis (Version 2.0.0)
2015
dlookr : Tools for Data Diagnosis, Exploration, Transformation (Version 0.6.3)
2018
drifter : Concept Drift and Concept Shift Detection for Predictive Models (Version 0.2.1)
2019
drpop : Efficient and Doubly Robust Population Size Estimation (Version 0.0.3)
2021
dynwrap : Representing and Inferring Single-Cell Trajectories (Version 1.2.4)
2019
enmSdmX : Species Distribution Modeling and Ecological Niche Modeling (Version 1.1.2)
2022
explainer : Machine Learning Model Explainer (Version 1.0.0)
2023
fairadapt : Fair Data Adaptation with Quantile Preservation (Version 0.2.7)
2019
fairmodels : Flexible Tool for Bias Detection, Visualization, and Mitigation (Version 1.2.1)
2020
familiar : End-to-End Automated Machine Learning and Model Evaluation (Version 1.4.6)
2022
fastshap : Fast Approximate Shapley Values (Version 0.1.1)
2019
finetune : Additional Functions for Model Tuning (Version 1.2.0)
2020
flevr : Flexible, Ensemble-Based Variable Selection with Potentially Missing Data (Version 0.0.4)
2023
flowml : A Backend for a 'nextflow' Pipeline that Performs Machine-Learning-Based Modeling of Biomedical Data (Version 0.1.3)
2023
fmeffects : Model-Agnostic Interpretations with Forward Marginal Effects (Version 0.1.2)
2023
forestControl : Approximate False Positive Rate Control in Selection Frequency for Random Forest (Version 0.2.2)
2018
gapclosing : Estimate Gaps Under an Intervention (Version 1.0.2)
2021
geomod : A Computer Program for Geotechnical Investigations (Version 0.1.0)
2023
hpiR : House Price Indexes (Version 0.3.2)
2018
htmldf : Simple Scraping and Tidy Webpage Summaries (Version 0.6.0)
2020
hypoRF : Random Forest Two-Sample Tests (Version 1.0.0)
2021
iBreakDown : Model Agnostic Instance Level Variable Attributions (Version 2.1.2)
2019
ibawds : Functions and Datasets for the Data Science Course at IBAW (Version 0.6.0)
2021
iml : Interpretable Machine Learning (Version 0.11.2)
2018
influential : Identification and Classification of the Most Influential Nodes (Version 2.2.9)
2020
ingredients : Effects and Importances of Model Ingredients (Version 2.3.0)
2019
innsight : Get the Insights of Your Neural Network (Version 0.3.0)
2021
knockoff : The Knockoff Filter for Controlled Variable Selection (Version 0.3.6)
2014
lime : Local Interpretable Model-Agnostic Explanations (Version 0.5.3)
2017
lmtp : Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies (Version 1.3.3)
2020
mcboost : Multi-Calibration Boosting (Version 0.4.3)
2021
memoria : Quantifying Ecological Memory in Palaeoecological Datasets and Other Long Time-Series (Version 1.0.0)
2019
metaforest : Exploring Heterogeneity in Meta-Analysis using Random Forests (Version 0.1.4)
2017
meteo : RFSI & STRK Interpolation for Meteo and Environmental Variables (Version 2.0-3)
2015
micd : Multiple Imputation in Causal Graph Discovery (Version 1.1.1)
2022
miceRanger : Multiple Imputation by Chained Equations with Random Forests (Version 1.5.0)
2020
mice : Multivariate Imputation by Chained Equations (Version 3.16.0)
2006
miesmuschel : Mixed Integer Evolution Strategies (Version 0.0.3)
2022
missForestPredict : Missing Value Imputation using Random Forest for Prediction Settings (Version 1.0)
2023
missRanger : Fast Imputation of Missing Values (Version 2.4.0)
2017
mlsurvlrnrs : R6-Based ML Survival Learners for 'mlexperiments' (Version 0.0.3)
2023
modelDown : Make Static HTML Website for Predictive Models (Version 1.1)
2019
modelStudio : Interactive Studio for Explanatory Model Analysis (Version 3.1.2)
2019
multiclassPairs : Build MultiClass Pair-Based Classifiers using TSPs or RF (Version 0.4.3)
2020
nestedcv : Nested Cross-Validation with 'glmnet' and 'caret' (Version 0.7.8)
2022
nlpred : Estimators of Non-Linear Cross-Validated Risks Optimized for Small Samples (Version 1.0.1)
2019
ocf : Ordered Correlation Forest (Version 1.0.0)
2023
orf : Ordered Random Forests (Version 0.1.4)
2019
outForest : Multivariate Outlier Detection and Replacement (Version 1.0.1)
2020
parsnip : A Common API to Modeling and Analysis Functions (Version 1.2.1)
2018
pdp : Partial Dependence Plots (Version 0.8.1)
2016
polle : Policy Learning (Version 1.3)
2022
poolVIM : Gene-Based Association Tests using the Actual Impurity Reduction (AIR) Variable Importance (Version 1.0.0)
2018
purge : Purge Training Data from Models (Version 0.2.1)
2015
qeML : Quick and Easy Machine Learning Tools (Version 1.1)
2023
quantregRanger : Quantile Regression Forests for 'ranger' (Version 1.0)
2017
r2pmml : Convert R Models to PMML (Version 0.27.1)
2019
radiant.model : Model Menu for Radiant: Business Analytics using R and Shiny (Version 1.6.3)
2016
randomForestExplainer : Explaining and Visualizing Random Forests in Terms of Variable Importance (Version 0.10.1)
2017
rfVarImpOOB : Unbiased Variable Importance for Random Forests (Version 1.0.3)
2020
rfinterval : Predictive Inference for Random Forests (Version 1.0.0)
2019
rfvimptest : Sequential Permutation Testing of Random Forest Variable Importance Measures (Version 0.1.3)
2022
riskRegression : Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks (Version 2023.12.21)
2011
rmweather : Tools to Conduct Meteorological Normalisation and Counterfactual Modelling for Air Quality Data (Version 0.2.5)
2018
sambia : A Collection of Techniques Correcting for Sample Selection Bias (Version 0.1.0)
2018
sense : Automatic Stacked Ensemble for Regression Tasks (Version 1.0.0)
2021
seqimpute : Imputation of Missing Data in Sequence Analysis (Version 2.0.0)
2022
shapr : Prediction Explanation with Dependence-Aware Shapley Values (Version 0.2.2)
2020
simPop : Simulation of Complex Synthetic Data Information (Version 2.1.3)
2014
sirus : Stable and Interpretable RUle Set (Version 0.3.3)
2019
soilassessment : Assessment Models for Agriculture Soil Conditions and Crop Suitability (Version 0.2.6)
2019
solitude : An Implementation of Isolation Forest (Version 1.1.3)
2018
spFSR : Feature Selection and Ranking via Simultaneous Perturbation Stochastic Approximation (Version 2.0.4)
2018
spatialRF : Easy Spatial Modeling with Random Forest (Version 1.1.4)
2021
sperrorest : Perform Spatial Error Estimation and Variable Importance Assessment (Version 3.0.5)
2012
spm : Spatial Predictive Modeling (Version 1.2.2)
2017
spmodel : Spatial Statistical Modeling and Prediction (Version 0.6.0)
2022
stablelearner : Stability Assessment of Statistical Learning Methods (Version 0.1-5)
2018
stacks : Tidy Model Stacking (Version 1.0.4)
2020
subscreen : Systematic Screening of Study Data for Subgroup Effects (Version 3.0.7)
2017
superMICE : SuperLearner Method for MICE (Version 1.1.1)
2022
superml : Build Machine Learning Models Like Using Python's Scikit-Learn Library in R (Version 0.5.7)
2018
survex : Explainable Machine Learning in Survival Analysis (Version 1.2.0)
2022
synthpop : Generating Synthetic Versions of Sensitive Microdata for Statistical Disclosure Control (Version 1.8-0)
2014
text : Analyses of Text using Transformers Models from HuggingFace, Natural Language Processing and Machine Learning (Version 1.2.1)
2020
text2sdg : Detecting UN Sustainable Development Goals in Text (Version 1.1.1)
2021
tidyAML : Automatic Machine Learning with 'tidymodels' (Version 0.0.5)
2023
tidypredict : Run Predictions Inside the Database (Version 0.5)
2018
tidysdm : Species Distribution Models with Tidymodels (Version 0.9.4)
2023
tramicp : Model-Based Causal Feature Selection for General Response Types (Version 0.0-2)
2023
tree.interpreter : Random Forest Prediction Decomposition and Feature Importance Measure (Version 0.1.1)
2019
treeshap : Compute SHAP Values for Your Tree-Based Models Using the 'TreeSHAP' Algorithm (Version 0.3.1)
2023
triplot : Explaining Correlated Features in Machine Learning Models (Version 1.3.0)
2020
tsensembler : Dynamic Ensembles for Time Series Forecasting (Version 0.1.0)
2017
tuneRanger : Tune Random Forest of the 'ranger' Package (Version 0.7)
2018
txshift : Efficient Estimation of the Causal Effects of Stochastic Interventions (Version 0.3.8)
2020
vaccine : Statistical Tools for Immune Correlates Analysis of Vaccine Clinical Trial Data (Version 1.2.1)
2023
varImp : RF Variable Importance for Arbitrary Measures (Version 0.4)
2018
vetiver : Version, Share, Deploy, and Monitor Models (Version 0.2.5)
2021
vimp : Perform Inference on Algorithm-Agnostic Variable Importance (Version 2.3.3)
2018
vip : Variable Importance Plots (Version 0.4.1)
2018
vivid : Variable Importance and Variable Interaction Displays (Version 0.2.8)
2021
worcs : Workflow for Open Reproducible Code in Science (Version 0.1.14)
2020
Showing 1-195 of 195
RPKG Scholar presents a tabulation of an author's contribution in the development of R packages stored in the Comprehensive R Archive Network (CRAN).
Within this site, we consider package dependencies (suggests,imports,depends,enhances) as citations because we believe that using one's package to develop another is tantamount to citing the author of the package being imported, suggested or enhanced.
