Fixes data errors in numerical, factor and date variables, checks data quality and performs report tables from models and summaries.

ClustOfVar, ReporteRs, lme4, lmerTest, xtable, beeswarm, lqmm,
ordinal

beeswarm, boot, lmerTest, ReporteRs, xtable
Fixes data errors in numerical, factor and date variables, checks data quality, performs exploratory analysis and creates report tables from models and summaries.

beeswarm, boot, lme4, lmerTest, ReporteRs, xtable

beeswarm, boot, brms, lme4, lmerTest, ReporteRs, xtable

beeswarm, boot, lme4, lmerTest, methods, ReporteRs, xtable
Tools for assessing data quality, performing exploratory analysis,
fixing data errors in numerical, factor and date variables and creating report
tables from models and summaries.

beeswarm, boot, flextable, lme4, lmerTest, methods, officer,
xtable

Semi-Automatic Preprocessing of Messy Data with Change Tracking
for Dataset Cleaning
beeswarm, boot, flextable, lme4, lmerTest, methods, officer,
stringdist, xtable
Tools for assessing data quality, performing exploratory analysis, and
semi-automatic preprocessing of messy data with change tracking for integral dataset cleaning.

Analyzes click streams based on Markov chains
This package allows modeling click streams with Markov chains. It supports to model click streams as zero-order, first-order or higher-order Markov chains.

Analyzes clickstreams based on Markov chains
This package allows modeling clickstreams with Markov chains. It supports to model clickstreams as zero-order, first-order or higher-order Markov chains.

Analyzes Clickstreams based on Markov Chains
A set of tools to read, analyze and write lists of click sequences on websites (i.e. clickstream). A click can be represented by a number, character or string. Clickstreams can be modeled as zero- (only computes occurrence probabilities), first- or higher-order Markov chains.

Analyzes Clickstreams Based on Markov Chains
igraph, plyr, Rsolnp, data.table, arules

A set of tools to read, analyze and write lists of click sequences on websites (i.e., clickstream). A click can be represented by a number, character or string. Clickstreams can be modeled as zero- (only computes occurrence probabilities), first- or higher-order Markov chains.
R (>= 3.0.1), methods, igraph, stats, utils
plyr, Rsolnp, data.table, arules, linprog