NEWS

pinbasic v1.2.2 (Release Date: 2018-11-18)

added argument nlminb_control to function qpin, pin_est and pin_est_core (see control argument for stats::nlminb)
Dataset BSfrequent is now simulated with the parameter set (0.2, 0.5, 2200, 2000, 800) instead of (0.2, 0.5, 1800, 1700, 600)

simulateBS now completely in C++
Method for generating sets of initial values in pin_confint function is no longer tied to HAC. Other available methods can be chosen via the method argument of the function. (Defaults to "HAC")
additional boolean flag posterior to enable computing posterior probabilities of conditions of trading days for pin_est, pin_est_core and qpin functions
qpin function returns now list of list by default with slots res and posterior: res is relevant for ggplot.qpin

pin_confint for ncores = 1; seed is set once at the beginning, hence results differ slightly from them of earlier version

Code clean-up for pin_confint and thus less dependencies; ncores = 1 by default now
Confidence interval computations invoked by pin_est_core, pin_est or qpin use only 1 CPU core by default now
Likelihood factorizations are re-implemented with Rcpp
simulateBS function is re-implemented with Rcpp

posterior: calculates posterior probabilities for trading days’ conditions
ggplot.posterior: Method for ggplot function for handling objects with class ‘posterior’ (Results from posterior function)

pin_confint: using iter function from iterators package to iterate over list of simulated datasets if executed in parallel which gains little speed-up in execution time
qpin_plot changed to ggplot.qpin: Method for ggplot function for handling objects with class ‘qpin’ (Results from qpin function)

simulateBS for simulating daily buys and sells data
pin_confint: computes confidence intervals for the probability of informed trading

pin_est_core, pin_est and qpin gained two new arguments: confint and ci_control
updated plotting structure for qpin_plot, facets are now grouped by probability parameters, intensity parameters and the probability of informed trading
initial_vals together with method = "HAC_Ref" now returns a number of sets of initial values depending on num_clust argument, not only one set
Vignette was added