fastDummies: Fast Creation of Dummy (Binary) Columns and Rows from Categorical Variables

Canonical link: https://rscholar.com/pkg/fastDummies

Jacob Kaplan • Benjamin Schlegel • View description and downloads View dependencies GitHub project

2017 Published

1.7.4 Version

0 Citations

2 Authors

Referenced by ⇅	Year
CMMs: Compositional Mediation Model (Version 1.0.0)	2022
ClusterVAR: Fitting Latent Class Vector-Autoregressive (VAR) Models (Version 0.0.8)	2024
GenMarkov: Multivariate Markov Chains (Version 0.2.0)	2021
MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus (Version 1.1.1)	2010
NeuralSens: Sensitivity Analysis of Neural Networks (Version 1.1.3)	2019
RCTrep: Validation of Estimates of Treatment Effects in Observational Data (Version 1.2.0)	2023
ReSurv: Machine Learning Models for Predicting Claim Counts (Version 1.0.0)	2024
ResIN: Response Item Networks (Version 2.0.0)	2023
RobinCar: Robust Inference for Covariate Adjustment in Randomized Clinical Trials (Version 0.3.2)	2024
Seurat: Tools for Single Cell Genomics (Version 5.1.0)	2017
Statsomat: Shiny Apps for Automated Data Analysis and Automated Interpretation (Version 1.1.0)	2021
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
bigDM: Scalable Bayesian Disease Mapping Models for High-Dimensional Data (Version 0.5.5)	2022
bnpa: Bayesian Networks & Path Analysis (Version 0.3.0)	2017
causalweight: Estimation Methods for Causal Inference Based on Inverse Probability Weighting (Version 1.1.1)	2018
cbcTools: Choice-Based Conjoint Experiment Design Generation and Power Evaluation in R (Version 0.5.0)	2023
codez: Seq2Seq Encoder-Decoder Model for Time-Feature Analysis Based on Tensorflow (Version 1.0.0)	2022
conText: 'a la Carte' on Text (ConText) Embedding Regression (Version 1.4.3)	2021
countts: Thomson Sampling for Zero-Inflated Count Outcomes (Version 0.1.0)	2023
dagitty: Graphical Analysis of Structural Causal Models (Version 0.3-4)	2016
deepdive: Deep Learning for General Purpose (Version 1.0.4)	2020
drf: Distributional Random Forests (Version 1.1.0)	2020
framecleaner: Clean Data Frames (Version 0.2.1)	2021
fusedMGM: Implementation of Fused MGM to Infer 2-Class Networks (Version 0.1.2)	2023
getspanel: General-to-Specific Modelling of Panel Data (Version 0.2.0)	2023
gscaLCA: Generalized Structure Component Analysis- Latent Class Analysis & Latent Class Regression (Version 0.0.5)	2019
jenga: Fast Extrapolation of Time Features using K-Nearest Neighbors (Version 1.3.0)	2021
logitr: Logit Models w/Preference & WTP Space Utility Parameterizations (Version 1.1.2)	2021
multifear: Multiverse Analyses for Conditioning Data (Version 0.1.3)	2021
naive: Empirical Extrapolation of Time Feature Patterns (Version 1.2.3)	2022
nlive: Automated Estimation of Sigmoidal and Piecewise Linear Mixed Models (Version 0.6.0)	2022
psyntur: Helper Tools for Teaching Statistical Data Analysis (Version 0.1.0)	2021
reslr: Modelling Relative Sea Level Data (Version 0.1.1)	2023
scpi: Prediction Intervals for Synthetic Control Methods with Multiple Treated Units and Staggered Adoption (Version 2.2.6)	2022
segen: Sequence Generalization Through Similarity Network (Version 1.1.0)	2022
smdi: Perform Structural Missing Data Investigations (Version 0.3.1)	2023
spinner: An Implementation of Graph Net Architecture Based on 'torch' (Version 1.1.0)	2023
spooky: Time Feature Extrapolation Using Spectral Analysis and Jack-Knife Resampling (Version 1.4.0)	2022
tetragon: Automatic Sequence Prediction by Expansion of the Distance Matrix (Version 1.3.0)	2021
tidycomm: Data Modification and Analysis for Communication Research (Version 0.4.1)	2019
tidyvpc: VPC Percentiles and Prediction Intervals (Version 1.5.2)	2020

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.