| Type: | Package |
| Title: | Pattern Identification for Dyadic Sequences Using Transition Matrices |
| Version: | 0.1.0 |
| Description: | Provides methods for analyzing dyadic interaction sequences using transition matrices within the Actor-Partner Interdependence Model. The package supports the computation of empirical transition counts, maximum likelihood estimation of transition probabilities and identification of interaction patterns in univariate and bivariate dyadic interaction sequences. |
| License: | MIT + file LICENSE |
| URL: | https://github.com/BoellenruecherM/dyadicMarkov-public |
| BugReports: | https://github.com/BoellenruecherM/dyadicMarkov-public/issues |
| Encoding: | UTF-8 |
| Language: | en-US |
| Depends: | R (≥ 4.1.0) |
| Suggests: | testthat (≥ 3.0.0), knitr, rmarkdown |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.3.3 |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | no |
| Packaged: | 2026-03-11 16:44:22 UTC; bolle |
| Author: | Mattia Böllenrücher [aut, cre, cph], Mégane Bollenrucher [aut], Jean-Philippe Antonietti [aut] |
| Maintainer: | Mattia Böllenrücher <mattia.boellenruecher@student.unisg.ch> |
| Repository: | CRAN |
| Date/Publication: | 2026-03-16 19:50:08 UTC |
Classify the bivariate dependence case
Description
Classifies the bivariate case as "trivial", "univariate",
"partial", or "complete" using two chi-squared tests against
constrained models (states = 2 only).
Usage
bivariateCase(empirical, alpha = 0.05)
Arguments
empirical |
An empirical bivariate count matrix (must be 16x2; states = 2). |
alpha |
A single number in (0, 1) giving the significance level. |
Value
A list with components testUnivariate, testPartial, and case.
Examples
chainFM_V1 <- c(1L, 2L, 1L, 2L, 2L, 1L)
chainSM_V1 <- c(2L, 1L, 2L, 1L, 1L, 2L)
chainFM_V2 <- c(1L, 1L, 2L, 2L, 1L, 2L)
chainSM_V2 <- c(2L, 2L, 1L, 1L, 2L, 1L)
emp <- countEmpBivariate(chainFM_V1, chainSM_V1, chainFM_V2, chainSM_V2, states = 2L)
bivariateCase(emp, alpha = 0.05)
Select the best complete bivariate pattern by AIC
Description
Compares complete bivariate patterns (C, D1–D4, E1–E4) using AIC and returns the selected pattern.
Usage
completePattern(empirical)
Arguments
empirical |
An empirical bivariate count matrix (must be 16x2; states = 2). |
Details
Requires a bivariate empirical count matrix for states = 2
(output of countEmpBivariate).
Value
A list with components aic (a data frame with columns
pattern, matrix, aic) and pattern (the selected
pattern label).
Examples
chainFM_V1 <- c(1L, 2L, 1L, 2L, 2L, 1L)
chainSM_V1 <- c(2L, 1L, 2L, 1L, 1L, 2L)
chainFM_V2 <- c(1L, 1L, 2L, 2L, 1L, 2L)
chainSM_V2 <- c(2L, 2L, 1L, 1L, 2L, 1L)
emp <- countEmpBivariate(chainFM_V1, chainSM_V1, chainFM_V2, chainSM_V2, states = 2L)
completePattern(emp)
Empirical transition counts for dyadic Markov chains
Description
Computes empirical transition counts for a dyadic Markov process from two observed state sequences (FM and SM). Rows correspond to dyad states (FM, SM) and columns to the next FM state.
Usage
countEmp(chainFM, chainSM, states)
Arguments
chainFM |
Vector of observed states for the first member (FM). |
chainSM |
Vector of observed states for the second member (SM). |
states |
A single integer >= 2 giving the number of states. |
Value
An integer matrix with states^2 rows and states columns.
Examples
chainFM <- c(1L, 2L, 1L, 2L, 2L, 1L)
chainSM <- c(2L, 1L, 2L, 1L, 1L, 2L)
countEmp(chainFM, chainSM, states = 2L)
Empirical transition counts for the bivariate dyadic model
Description
Computes empirical transition counts for the bivariate dyadic model (two variables).
The current implementation supports states = 2 only.
Usage
countEmpBivariate(chainFM_V1, chainSM_V1, chainFM_V2, chainSM_V2, states = 2L)
Arguments
chainFM_V1, chainSM_V1 |
Vectors of observed states for variable 1 (FM and SM). |
chainFM_V2, chainSM_V2 |
Vectors of observed states for variable 2 (FM and SM). |
states |
A single integer. Currently only |
Value
An integer matrix of counts with 16 rows and 2 columns (when states = 2).
Examples
chainFM_V1 <- c(1L, 2L, 1L, 2L, 2L, 1L)
chainSM_V1 <- c(2L, 1L, 2L, 1L, 1L, 2L)
chainFM_V2 <- c(1L, 1L, 2L, 2L, 1L, 2L)
chainSM_V2 <- c(2L, 2L, 1L, 1L, 2L, 1L)
emp <- countEmpBivariate(chainFM_V1, chainSM_V1, chainFM_V2, chainSM_V2, states = 2L)
dim(emp)
Maximum likelihood estimation from empirical counts
Description
Estimates transition probabilities by maximum likelihood from an empirical
count matrix returned by countEmp (or related counters).
Usage
mleEstimation(empirical)
Arguments
empirical |
An empirical transition count matrix (typically from |
Value
A numeric matrix of MLE transition probabilities with the same dimensions as empirical.
Examples
chainFM <- c(1L, 2L, 1L, 2L, 2L, 1L)
chainSM <- c(2L, 1L, 2L, 1L, 1L, 2L)
emp <- countEmp(chainFM, chainSM, states = 2L)
mleEstimation(emp)
Select the best partial bivariate pattern by AIC
Description
Compares the partial bivariate patterns B1/B2/B3 using AIC and returns the selected pattern.
Usage
partialPattern(empirical)
Arguments
empirical |
An empirical bivariate count matrix (must be 16x2; states = 2). |
Details
Requires a bivariate empirical count matrix for states = 2
(output of countEmpBivariate).
Value
A list with components aic (a data frame) and pattern
(the selected pattern label).
Examples
chainFM_V1 <- c(1L, 2L, 1L, 2L, 2L, 1L)
chainSM_V1 <- c(2L, 1L, 2L, 1L, 1L, 2L)
chainFM_V2 <- c(1L, 1L, 2L, 2L, 1L, 2L)
chainSM_V2 <- c(2L, 2L, 1L, 1L, 2L, 1L)
emp <- countEmpBivariate(chainFM_V1, chainSM_V1, chainFM_V2, chainSM_V2, states = 2L)
partialPattern(emp)
Univariate pattern classification for dyadic Markov chains
Description
Computes empirical transition counts, fits the unrestricted model by maximum likelihood, and performs chi-squared goodness-of-fit tests against Actor-only (AM) and Partner-only (PM) constrained models to classify the univariate dyadic pattern.
Usage
univariatePattern(chainFM, chainSM, states, alpha = 0.05)
Arguments
chainFM |
Vector of observed states for the first member (FM). |
chainSM |
Vector of observed states for the second member (SM). |
states |
A single integer >= 2 giving the number of states. |
alpha |
A single number in (0, 1) giving the significance level. |
Value
A list with two htest objects (TEST.AM, TEST.PM)
and a string pattern.
Examples
chainFM <- c(1L, 2L, 1L, 2L, 2L, 1L)
chainSM <- c(2L, 1L, 2L, 1L, 1L, 2L)
univariatePattern(chainFM, chainSM, states = 2L, alpha = 0.05)