Merge pull request #2 from bsiepe/devel

Update to version 0.2.0

Author: bsiepe

.gitignore

@@ -2,3 +2,6 @@
 .Rhistory
 .RData
 .Ruserdata
+
+# local testing files
+tempfiles/

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
 Version: 0.1.0
-Date: 2024-02-20 15:36:22 UTC
-SHA: 40df86c9c02e76044b2499df231d4ddc43ce4940
+Date: 2024-02-27 07:01:18 UTC
+SHA: 15721ee1ca7a92c51b130bafde6eb79d19e235da

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: tsnet
 Type: Package
 Title: Fitting, Comparing, and Visualizing Networks Based on Time Series Data
-Version: 0.1.0
+Version: 0.2.0
 Authors@R: 
   c(person("Björn S.", "Siepe", 
   email = "bjoernsiepe@gmail.com", 
@@ -22,13 +22,14 @@ Encoding: UTF-8
 LazyData: true
 URL: https://github.com/bsiepe/tsnet
 BugReports: https://github.com/bsiepe/tsnet/issues
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2
 Imports:
     cowplot,
     dplyr,
     ggdist,
     ggokabeito,
     ggplot2,
+    loo,
     methods,
     posterior,
     Rcpp (>= 0.12.0),

NAMESPACE

@@ -26,6 +26,12 @@ importFrom(ggdist,stat_slab)
 importFrom(ggdist,theme_ggdist)
 importFrom(ggokabeito,palette_okabe_ito)
 importFrom(ggokabeito,scale_fill_okabe_ito)
+importFrom(loo,loo)
+importFrom(loo,loo_model_weights)
+importFrom(loo,pareto_k_values)
+importFrom(loo,psis)
+importFrom(loo,relative_eff)
+importFrom(loo,weights.importance_sampling)
 importFrom(posterior,as_draws_matrix)
 importFrom(rlang,.data)
 importFrom(rstan,extract)

NEWS.md

@@ -1,3 +1,6 @@
+# tsnet 0.2.0
+* Fixed documentation issues (e.g., wrong display of LaTex equations)
+
 # tsnet 0.1.0
 
 * Initial CRAN submission.

R/centrality.R

@@ -5,17 +5,18 @@
 #' a network, while density describes the networks' overall connectedness.
 #' Specifically, it computes the in-strength, out-strength, contemporaneous
 #' strength, temporal network density, and contemporaneous network density. The
-#' result can then be visualized using [plot_centrality()].
+#' result can then be visualized using \code{\link{plot_centrality}}.
 #'
 #' @param fitobj Fitted model object for a Bayesian GVAR model. This can be
-#'   `tsnet_fit` object (obtained from [stan_gvar()]), a BGGM object (obtained
-#'   from [BGGM::var_estimate()]), or extracted posterior samples (obtained from
-#'   [stan_fit_convert()).
+#' `tsnet_fit` object (obtained from \code{\link{stan_gvar}},
+#' a BGGM object (obtained from \code{\link[BGGM]{var_estimate}} in \code{BGGM}),
+#' or extracted posterior samples (obtained from \code{\link{stan_fit_convert}}).
 #' @param burnin An integer specifying the number of initial samples to discard
-#'   as burn-in. Default is 0.
+#'   as burn-in. Default is \code{0}.
 #' @param remove_ar A logical value specifying whether to remove the
-#'   autoregressive effects for centrality calculation. Default is TRUE. This is
-#'   only relevant for the calculation of temporal centrality/density measures.
+#'   autoregressive effects for centrality calculation. Default is \code{TRUE}.
+#'   This is only relevant for the calculation of temporal centrality/density
+#'   measures.
 #'
 #' @return A list containing the following centrality measures:
 #' \itemize{
@@ -106,11 +107,11 @@ get_centrality <- function(fitobj,
 #' centrality measures.
 #'
 #' @param obj An object containing the centrality measures obtained from
-#'   [get_centrality()].
+#'   \code{\link{get_centrality}}.
 #' @param plot_type A character string specifying the type of plot. Accepts
-#'   "tiefighter" or "density". Default is "tiefighter".
+#'   "tiefighter" or "density". Default is \code{"tiefighter"}.
 #' @param cis A numeric value specifying the credible interval. Must be between
-#'   0 and 1 (exclusive). Default is 0.95.
+#'   0 and 1 (exclusive). Default is \code{0.95}.
 #'
 #' @return A ggplot object visualizing the centrality measures. For a
 #'   "tiefighter" plot, each point represents the mean centrality measure for a

R/check_eigen.R

@@ -2,17 +2,17 @@
 #'
 #' This function checks the eigenvalues of the Beta matrix (containing the
 #' temporal coefficients) to assure that the model is stationary. It uses the
-#' same check as the `graphicalVAR` package. The function calculates the
+#' same check as the \code{graphicalVAR} package. The function calculates the
 #' eigenvalues of the Beta matrix and checks if the sum of the squares of the
 #' real and imaginary parts of the eigenvalues is less than 1. If it is, the VAR
 #' model is considered stable.
 #'
 #' @param fitobj A fitted Bayesian GVAR object. This can be a tsnet_fit object
-#'   (obtained from [stan_gvar()]), a BGGM object (obtained from
-#'   [BGGM::var_estimate()]), or extracted posterior samples (obtained from
-#'   [stan_fit_convert()).
+#'   (obtained from \code{\link{stan_gvar}}, a \code{BGGM} object (obtained from
+#'   \code{\link[BGGM]{var_estimate}}), or extracted posterior samples
+#'   (obtained from \code{\link{stan_fit_convert}}.
 #' @param verbose Logical. If TRUE, a verbal summary of the results is printed.
-#'   Default is TRUE.
+#'   Default is \code{TRUE}.
 #' @examples
 #'  data(fit_data)
 #'  fitobj <- fit_data[[1]]

R/compare_gvar.R

@@ -10,39 +10,39 @@
 #'   <doi:10.31234/osf.io/uwfjc>.
 #'
 #' @param fit_a Fitted model object for Model A. This can be a tsnet_fit object
-#'   (obtained from [stan_gvar()]), a BGGM object (obtained from
-#'   [BGGM::var_estimate()]), or extracted posterior samples (obtained from
-#'   [stan_fit_convert()).
+#'   (obtained from \code{\link{stan_gvar}}, a \code{BGGM} object (obtained from
+#'   \code{\link[BGGM]{var_estimate}}, or extracted posterior samples (obtained from
+#'   \code{\link{stan_fit_convert}}).
 #' @param fit_b Fitted model object for Model B. This can be a tsnet_fit object
-#'   (obtained from [stan_gvar()]), a BGGM object (obtained from
-#'   [BGGM::var_estimate()]), or extracted posterior samples (obtained from
-#'   [stan_fit_convert()).
-#' @param cutoff The percentage level of the test (default: 5\%) as integer.
-#' @param dec_rule The decision rule to be used. Currently supports default "or"
+#'   (obtained from \code{\link{stan_gvar}}, a \code{BGGM} object (obtained from
+#'   \code{\link[BGGM]{var_estimate}}, or extracted posterior samples (obtained from
+#'   \code{\link{stan_fit_convert}}).
+#' @param cutoff The percentage level of the test (default: \code{5}) as integer.
+#' @param dec_rule The decision rule to be used. Currently supports default \code{"or"}
 #'   (comparing against two reference distributions) and "comb" (combining the
 #'   reference distributions). The use of "or" is recommended, as "comb" is less
 #'   stable.
 #' @param n_draws The number of draws to use for reference distributions
-#'   (default: 1000).
+#'   (default: \code{1000}).
 #' @param comp The distance metric to use. Should be one of "frob" (Frobenius
 #'   norm), "maxdiff" (maximum  difference), or "l1" (L1 norm) (default:
-#'   "frob"). The use of the Frobenius norm is recommended.
+#'   \code{"frob"}). The use of the Frobenius norm is recommended.
 #' @param return_all Logical indicating whether to return all distributions
-#'   (default: FALSE). Has to be set to TRUE for plotting the results.
+#'   (default: \code{FALSE}). Has to be set to TRUE for plotting the results.
 #' @param sampling_method Draw sequential pairs of samples from the posterior,
 #'   with certain distance between them ("sequential") or randomly from two
 #'   halves of the posterior ("random"). The "random" method is preferred to
 #'   account for potential autocorrelation between subsequent samples. Default:
-#'   "random".
+#'   \code{"random"}.
 #' @param indices A list of "beta" and "pcor" indices specifying which elements
-#'   of the matrices to consider when calculating distances. If NULL (default),
+#'   of the matrices to consider when calculating distances. If \code{NULL} (default),
 #'   all elements of both matrices are considered. If provided, only the
 #'   elements at these indices are considered. If only one of the matrices
 #'   should have indices, the other one should be NULL. This can be useful if
 #'   you want to calculate distances based on a subset of the elements in the
 #'   matrices.
-#' @param burnin The number of burn-in iterations to discard (default: 0).
-#' @return A list (of class "compare_gvar") containing the results of the
+#' @param burnin The number of burn-in iterations to discard (default: \code{0}).
+#' @return A list (of class \code{compare_gvar}) containing the results of the
 #'   comparison. The list includes:
 #'
 #'   \item{sig_beta}{Binary decision on whether there is a significant difference between the temporal networks of A and B}

R/data.R

@@ -1,15 +1,15 @@
 #' Simulated Time Series Dataset
 #'
 #' This dataset contains a simulated time series dataset for two individuals
-#' generated using the `graphicalVAR` package. The dataset is useful for testing
+#' generated using the \code{graphicalVAR} package. The dataset is useful for testing
 #' and demonstrating the functionality of the package.
 #'
 #' @format ## `ts_data` A data frame with 500 rows and 7 columns.
 #' \describe{
 #'   \item{id}{A character string identifier for the individual. There are two unique ids, representing two individuals.}
 #'   \item{V1-V6}{These columns represent six different variables in the time series data.}
 #'   }
-#' @source Simulated using the [graphicalVAR::graphicalVARsim()] function.
+#' @source Simulated using the \code{\link[graphicalVAR]{graphicalVARsim}} function in the \code{graphicalVAR} package.
 #'
 #' @usage data(ts_data)
 #'
@@ -28,19 +28,19 @@
 #' Example Posterior Samples
 #'
 #' This dataset contains posterior samples of beta coefficients and partial correlations for two individuals.
-#' It was generated by fitting a GVAR model using [stan_gvar()] with three variables from the [ts_data] dataset.
+#' It was generated by fitting a GVAR model using \code{\link{stan_gvar}} with three variables from the \code{\link{ts_data}} dataset.
 #'
 #' @format ## `fit_data`
 #' A list with two elements, each containing posterior samples for one individual.
 #'
 #' @usage data(fit_data)
 #'
-#' @source The data is generated using the [stan_gvar()] function on subsets
-#' of the [ts_data] time series data.
+#' @source The data is generated using the \code{\link{stan_gvar}} function on subsets
+#' of the \code{\link{ts_data}} time series data.
 #'
 #' @details
 #' The list contains two elements, each containing posterior samples for one individual.
-#' The samples were extracted using the [stan_fit_convert()] function.
+#' The samples were extracted using the \code{\link{stan_fit_convert}} function.
 #' For each individual, the list elements contain the posterior means of the beta coefficients
 #' ("beta_mu") and the posterior means of the partial correlations ("pcor_mu").
 #' The "fit" element contains all 1000 posterior samples of the beta coefficients and partial correlations.