All packages

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paws.cost.management — 0.5.0

'Amazon Web Services' Cost Management Services

paws.customer.engagement — 0.5.0

'Amazon Web Services' Customer Engagement Services

paws.database — 0.5.0

'Amazon Web Services' Database Services

paws.developer.tools — 0.5.0

'Amazon Web Services' Developer Tools Services

paws.end.user.computing — 0.5.0

'Amazon Web Services' End User Computing Services

paws.machine.learning — 0.5.0

'Amazon Web Services' Machine Learning Services

paws.management — 0.5.0

'Amazon Web Services' Management & Governance Services

paws.networking — 0.5.0

'Amazon Web Services' Networking & Content Delivery Services

paws.security.identity — 0.5.0

'Amazon Web Services' Security, Identity, & Compliance Services

paws.storage — 0.5.0

'Amazon Web Services' Storage Services

pawscore — 1.0.3

Pain Assessment at Withdrawal Speeds (PAWS)

pbANOVA — 0.1.0

Parametric Bootstrap for ANOVA Models

pbapply — 1.7-2

Adding Progress Bar to '*apply' Functions

pbatR — 2.2-17

Pedigree/Family-Based Genetic Association Tests Analysis and Power

pbbd — 1.0.0

Position Balanced and Nearly Position Balanced Block Designs

pbcc — 0.0.4

Percentile-Based Control Chart

PBD — 1.4

Protracted Birth-Death Model of Diversification

pbdMPI — 0.5-1

R Interface to MPI for HPC Clusters (Programming with Big Data Project)

pbdSLAP — 0.3-5

Programming with Big Data -- Scalable Linear Algebra Packages

pbdZMQ — 0.3-11

Programming with Big Data -- Interface to 'ZeroMQ'

PBIBD — 1.3

Partially Balanced Incomplete Block Designs

PBImisc — 1.0

A Set of Datasets Used in My Classes or in the Book 'Modele Liniowe i Mieszane w R, Wraz z Przykladami w Analizie Danych'

PBIR — 0.1-0

Estimating the Probability of Being in Response and Related Outcomes

pbivnorm — 0.6.0

Vectorized Bivariate Normal CDF

pbkrtest — 0.5.2

Parametric Bootstrap, Kenward-Roger and Satterthwaite Based Methods for Test in Mixed Models

pbm — 1.2.1

Protein Binding Models

pbmcapply — 1.5.1

Tracking the Progress of Mc*pply with Progress Bar

PBNPA — 0.0.3

Permutation Based Non-Parametric Analysis of CRISPR Screen Data

pbo — 1.3.5

Probability of Backtest Overfitting

pbr — 0.0.2

Find a Cold One Near You

pBrackets — 1.0.1

Plot Brackets

pbs — 1.1

Periodic B Splines

PBSadmb — 1.1.6

ADMB for R Using Scripts or GUI

PBSddesolve — 1.13.4

Solver for Delay Differential Equations

PBSmapping — 2.73.4

Mapping Fisheries Data and Spatial Analysis Tools

PBSmodelling — 2.69.3

GUI Tools Made Easy: Interact with Models and Explore Data

PBtDesigns — 1.0.0

Partially Balanced t-Designs (PBtDesigns)

pbv — 0.5-47

Probabilities for Bivariate Normal Distribution

pcadapt — 4.3.5

Fast Principal Component Analysis for Outlier Detection

PCADSC — 0.8.0

Tools for Principal Component Analysis-Based Data Structure Comparisons

pcal — 1.0.0

Calibration of P-Values for Point Null Hypothesis Testing

pcaL1 — 1.5.7

L1-Norm PCA Methods

pcalg — 2.7-11

Methods for Graphical Models and Causal Inference

pCalibrate — 0.2-1

Bayesian Calibrations of p-Values

pcalls — 1.0

Pricing of Different Types of Call

PCAmatchR — 0.3.3

Match Cases to Controls Based on Genotype Principal Components

PCAmixdata — 3.1

Multivariate Analysis of Mixed Data

pcaone — 1.0.0

Randomized Singular Value Decomposition Algorithms with 'RcppEigen'

pcaPP — 2.0-4

Robust PCA by Projection Pursuit

pcatsAPIclientR — 1.1.0

'PCATS' API Client

PCBS — 0.1.0

Principle Component BiSulfite

pccc — 1.0.5

Pediatric Complex Chronic Conditions

PCDimension — 1.1.13

Finding the Number of Significant Principal Components

pcdpca — 0.4

Dynamic Principal Components for Periodically Correlated Functional Time Series

pcds — 0.1.8

Proximity Catch Digraphs and Their Applications

pcds.ugraph — 0.1.1

Underlying Graphs of Proximity Catch Digraphs and Their Applications

pcensmix — 1.2-1

Model Fitting to Progressively Censored Mixture Data

pcev — 2.2.2

Principal Component of Explained Variance

pcFactorStan — 1.5.4

Stan Models for the Paired Comparison Factor Model

PCFAM — 1.0

Computation of Ancestry Scores with Mixed Families and Unrelated Individuals

pcg — 1.1

Preconditioned Conjugate Gradient Algorithm for solving Ax=b

pcgen — 0.2.0

Reconstruction of Causal Networks for Data with Random Genetic Effects

PCGII — 1.1.2

Partial Correlation Graph with Information Incorporation

PCGSE — 0.5.0

Principal Component Gene Set Enrichment

pch — 2.1

Piecewise Constant Hazard Models for Censored and Truncated Data

pchc — 1.2

Bayesian Network Learning with the PCHC and Related Algorithms

PCICt — 0.5-4.4

Implementation of POSIXct Work-Alike for 365 and 360 Day Calendars

pcIRT — 0.2.4

IRT Models for Polytomous and Continuous Item Responses

PCL — 1.0

Proximal Causal Learning

pcLasso — 1.2

Principal Components Lasso

PCLassoReg — 1.0.0

Group Regression Models for Risk Protein Complex Identification

pcmabc — 1.1.3

Approximate Bayesian Computations for Phylogenetic Comparative Methods

PCMBase — 1.2.14

Simulation and Likelihood Calculation of Phylogenetic Comparative Models

PCMBaseCpp — 0.1.9

Fast Likelihood Calculation for Phylogenetic Comparative Models

PCMRS — 0.1-4

Model Response Styles in Partial Credit Models

pcnetmeta — 2.8

Patient-Centered Network Meta-Analysis

pco — 1.0.1

Panel Cointegration Tests

PCObw — 0.0.1

Bandwidth Selector with Penalized Comparison to Overfitting Criterion

pCODE — 0.9.4

Estimation of an Ordinary Differential Equation Model by Parameter Cascade Method

PCovR — 2.7.2

Principal Covariates Regression

PCPS — 1.0.7

Principal Coordinates of Phylogenetic Structure

pcr — 1.2.2

Analyzing Real-Time Quantitative PCR Data

PCRA — 1.2

Companion to Portfolio Construction and Risk Analysis

PCS — 1.3

Calculate the Probability of Correct Selection (PCS)

pcse — 1.9.1.1

Panel-Corrected Standard Error Estimation in R

PCSinR — 0.1.0

Parallel Constraint Satisfaction Networks in R

pcsstools — 0.1.2

Tools for Regression Using Pre-Computed Summary Statistics

pcSteiner — 1.0.0.1

Convenient Tool for Solving the Prize-Collecting Steiner Tree Problem

pct — 0.9.9

Propensity to Cycle Tool

pctax — 0.1.1

Professional Comprehensive Omics Data Analysis

pcts — 0.15.7

Periodically Correlated and Periodically Integrated Time Series

pcutils — 0.2.5

Some Useful Functions for Statistics and Visualization

pcv — 1.1.0

Procrustes Cross-Validation

pda — 1.2.7

Privacy-Preserving Distributed Algorithms

pdc — 1.0.3

Permutation Distribution Clustering

PDE — 1.4.9

Extract Tables and Sentences from PDFs with User Interface

pder — 1.0-2

Panel Data Econometrics with R

pdfCluster — 1.0-4

Cluster Analysis via Nonparametric Density Estimation

PDFEstimator — 4.5

Multivariate Nonparametric Probability Density Estimator

pdfetch — 0.2.9

Fetch Economic and Financial Time Series Data from Public Sources

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