All packages

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ddplot — 0.0.1

Create D3 Based SVG Graphics

DDPM — 0.1.0

Data Sets for Discrete Probability Models

DDPNA — 0.3.3

Disease-Drived Differential Proteins Co-Expression Network Analysis

DDRTree — 0.1.5

Learning Principal Graphs with DDRTree

ddsPLS — 1.2.1

Data-Driven Sparse Partial Least Squares

ddst — 1.4

Data Driven Smooth Tests

ddtlcm — 0.2.1

Latent Class Analysis with Dirichlet Diffusion Tree Process Prior

deadband — 0.1.0

Statistical Deadband Algorithms Comparison

deal — 1.2-42

Learning Bayesian Networks with Mixed Variables

deaR — 1.4.1

Conventional and Fuzzy Data Envelopment Analysis

debar — 0.1.1

A Post-Clustering Denoiser for COI-5P Barcode Data

DEBBI — 0.1.0

Differential Evolution-Based Bayesian Inference

debest — 0.1.0

Duration Estimation for Biomarker Enrichment Studies and Trials

DebiasInfer — 0.2

Efficient Inference on High-Dimensional Linear Model with Missing Outcomes

deBif — 0.1.8

Bifurcation Analysis of Ordinary Differential Equation Systems

deBInfer — 0.4.4

Bayesian Inference for Differential Equations

debkeepr — 0.1.1

Analysis of Non-Decimal Currencies and Double-Entry Bookkeeping

DeBoinR — 1.0

Box-Plots and Outlier Detection for Probability Density Functions

debugme — 1.2.0

Debug R Packages

debugr — 0.0.1

Debug Tool to Watch Objects/Expressions While Running an R Script

DeCAFS — 3.3.3

Detecting Changes in Autocorrelated and Fluctuating Signals

DECIDE — 1.3

DEComposition of Indirect and Direct Effects

decido — 0.3.0

Bindings for 'Mapbox' Ear Cutting Triangulation Library

decision — 0.1.0

Statistical Decision Analysis

decisionSupport — 1.114

Quantitative Support of Decision Making under Uncertainty

deckgl — 0.3.0

An R Interface to 'deck.gl'

declared — 0.24

Functions for Declared Missing Values

DeclareDesign — 1.0.10

Declare and Diagnose Research Designs

decode — 1.2

Differential Co-Expression and Differential Expression Analysis

decoder — 1.2.2

Decode Coded Variables to Plain Text and the Other Way Around

decompDL — 0.1.0

Decomposition Based Deep Learning Models for Time Series Forecasting

decomposedPSF — 0.2

Time Series Prediction with PSF and Decomposition Methods (EMD and EEMD)

DecomposeR — 1.0.6

Empirical Mode Decomposition for Cyclostratigraphy

decompr — 6.4.0

Global Value Chain Decomposition

decon — 1.3-4

Deconvolution Estimation in Measurement Error Models

deconstructSigs — 1.8.0

Identifies Signatures Present in a Tumor Sample

deconvolveR — 1.2-1

Empirical Bayes Estimation Strategies

decor — 1.0.2

Retrieve Code Decorations

DecorateR — 0.1.2

Fit and Deploy DECORATE Trees

decorators — 0.3.0

Extend the Behaviour of a Function without Explicitly Modifying it

Deducer — 0.7-9

A Data Analysis GUI for R

deducorrect — 1.3.7

Deductive Correction, Deductive Imputation, and Deterministic Correction

deductive — 1.0.0

Data Correction and Imputation Using Deductive Methods

DeductiveR — 1.0.0

Deductive Rational Method

deduped — 0.2.0

Making "Deduplicated" Functions

dedupewider — 0.1.0

Deduplication Across Multiple Columns

deep — 0.1.0

A Neural Networks Framework

deepdep — 0.4.3

Visualise and Explore the Deep Dependencies of R Packages

deepdive — 1.0.4

Deep Learning for General Purpose

deepgmm — 0.2.1

Deep Gaussian Mixture Models

deepgp — 1.1.2

Bayesian Deep Gaussian Processes using MCMC

deeplr — 2.0.1

Interface to the 'DeepL' Translation API

deepMOU — 0.1.1

Clustering of Short Texts by Mixture of Unigrams and Its Deep Extensions

deepnet — 0.2.1

Deep Learning Toolkit in R

deepNN — 1.2

Deep Learning

deepredeff — 0.1.1

Deep Learning Prediction of Effectors

deepregression — 1.0.0

Fitting Deep Distributional Regression

deepRstudio — 0.0.9

Seamless Language Translation in 'RStudio' using 'DeepL' API and 'Rstudioapi'

deeptime — 1.1.1

Plotting Tools for Anyone Working in Deep Time

deeptrafo — 0.1-1

Fitting Deep Conditional Transformation Models

DEET — 1.0.11

Differential Expression Enrichment Tool

DEEVD — 1.2.3

Density Estimation by Extreme Value Distributions

default — 1.0.0

Change the Default Arguments in R Functions

defineOptions — 0.9

Define and Parse Command Line Options

defineR — 0.0.4

Creates Define XML Documents

deFit — 0.2.1

Fitting Differential Equations to Time Series Data

deflateBR — 1.1.2

Deflate Nominal Brazilian Reais

deflist — 0.2.0

Deferred List - A Read-Only List-Like Object with Deferred Access

deforestable — 3.1.1

Classify RGB Images into Forest or Non-Forest

deform — 1.0.0

Spatial Deformation and Dimension Expansion Gaussian Processes

deformula — 0.1.2

Integration of One-Dimensional Functions with Double Exponential Formulas

degday — 0.4.0

Compute Degree Days

DEGRE — 0.2.0

Inferring Differentially Expressed Genes using Generalized Linear Mixed Models

degreenet — 1.3-5

Models for Skewed Count Distributions Relevant to Networks

degross — 0.9.0

Density Estimation from GROuped Summary Statistics

dejaVu — 0.3.0

Multiple Imputation for Recurrent Events

Delaporte — 8.4.0

Statistical Functions for the Delaporte Distribution

delayed — 0.5.0

A Framework for Parallelizing Dependent Tasks

DelayedEffect.Design — 1.1.3

Sample Size and Power Calculations using the APPLE, SEPPLE, APPLE+ and SEPPLE+ Methods

deldir — 2.0-4

Delaunay Triangulation and Dirichlet (Voronoi) Tessellation

Delta — 0.2.0.3

Measure of Agreement Between Two Raters

deltaccd — 1.0.2

Quantify Rhythmic Gene Co-Expression Relative to a Reference

DeltaMAN — 0.5.0

Delta Measurement of Agreement for Nominal Data

deltaPlotR — 1.6

Identification of Dichotomous Differential Item Functioning (DIF) using Angoff's Delta Plot Method

DELTD — 2.6.8

Kernel Density Estimation using Lifetime Distributions

DEM — 0.0.0.2

The Distributed EM Algorithms in Multivariate Gaussian Mixture Models

dematel — 0.1.0

Decision Making Trial and Evaluation Laboratory Technique in R

demic — 2.0.0

Dynamic Estimator of Microbial Communities

deming — 1.4

Deming, Theil-Sen, Passing-Bablock and Total Least Squares Regression

DemoDecomp — 1.0.1

Decompose Demographic Functions

demodelr — 1.0.1

Simulating Differential Equations with Data

demogR — 0.6.0

Analysis of Age-Structured Demographic Models

DemografixeR — 0.1.1

Extrapolate Gender, Age and Nationality of a Name

demoGraphic — 0.1.0

Providing Demographic Table with the P-Value, Standardized Mean Difference Value

DemographicTable — 0.1.8

Creating Demographic Table

demography — 2.0

Forecasting Mortality, Fertility, Migration and Population Data

demoKde — 1.0.1

Kernel Density Estimation for Demonstration Purposes

DemoKin — 1.0.3

Estimate Population Kin Distribution

demoShiny — 0.1

Runs a 'Shiny' App as Demo or Lists All Demo 'Shiny' Apps

DEMOVA — 1.0

DEvelopment (of Multi-Linear QSPR/QSAR) MOdels VAlidated using Test Set

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