Changes in version 0.1.3 (2024-02-28)                  

  - remove the dependence on pscl and icenReg packages
  - add stringr to imported packages
  - add survival::survreg and stats::optim to imported functions
  - use the Brent method for optimization to fit an interval-censored
    exponential distribution to the time from randomization to the first
    drug dispensing visit
  - check the number of unique combinations of time and k1 in f_fit_ti
  - use the VarCorr function from the nlme package to obtain the
    variance of random effects from the lme function call
  - use data from all kit types to inform the common time model
  - add the pred_pp_only parameter to f_drug_demand to make
    protocol-based predictions only
  - ensure that df and visitview have all the required columns and none
    of the required columns have missing values
  - combine dosing_pred_df and dosing_pred_pp in the f_drug_demand
    output
  - handle cases where all patients in a treatment arm discontinued
    before the cutoff
  - replace round with formatC to retain the zeros after the decimal
    point
  - rename fit_xx to xx_fit, where xx = k0, t0, t1, ki, ti, di to be
    consistent with enroll_fit and event_fit naming convention
  - add kit and kit_name to handle cases when a drug has different kit
    types depending on the site
  - add the prior probability of different kit types within a drug at
    the design stage
  - streamline the examples through the use of function calls
  - update the initial parameter values in f_fit_ki for zero-inflated
    Poisson model
  - update the p.fit calculation in f_fit_ti when using the least
    absolute deviations model
  - convert kit_name to a factor to ensure the correct order when
    creating a plot
  - add kit_name as the sub plot title in f_dispensing_models.R
  - add the vf_kit parameter to f_dose_draw_1 to get the kit information
    for each subject in each simulation draw
  - add the f_ongoing_new function to prepare the dosing data sets to
    impute for ongoing and new subjects
  - add kit_description_df to the output of f_dose_observed
  - add the rdirichlet function to generate cell probabilities from the
    Dirichlet distribution
  - remove the f_treatment_by_drug_df function
  - replace the treatment_by_drug matrix with the treatment_by_drug_df
    data frame
  - add kit_description_df, treatment_by_drug_df, and dosing_schedule_df
    to the f_drug_demand output

                 Changes in version 0.1.2 (2023-12-21)                  

  - add least absolute deviations (LAD) regression as an option for
    modelling the gap time between drug dispensing visits and rename the
    original linear model as least squares (LS)
  - use more descriptive names for drug dispensing models

                 Changes in version 0.1.1 (2023-10-31)                  

  - add a reference for parametric analysis of interval-censored
    survival data
  - only keep one record per subject and drug dispensing day when using
    a common time model
  - rename f_dosing_draw, f_dosing_draw_1, and f_dosing_draw_t_1 to
    f_dose_draw, f_dose_draw_1, and f_dose_draw_t_1, respectively
  - remove the vf_new parameter of the f_dose_draw_1 function
  - remove the nreps parameter from the f_drug_demand function
  - add the f_dose_observed function and incorporate it in the
    f_drug_demand function
  - modify the condition "Vi + Ti <= D(i)" to "Vi + Ti < D(i)" in the
    f_dose_ongoing_cpp and f_dose_new_cpp functions
  - change "as.numeric(exp(attr(a$apVar, "Pars")))" to
    "exp(as.numeric(attr(a$apVar, "Pars")))" in the f_fit_di function to
    avoid the error for non-numeric argument to mathematical function
  - simplify the condition for common_time_model to
    "length(unique(target_days)) == 1"
  - add dosing_summary_t0 to the output of the f_drug_demand function
  - replace mutate and slice(n()) with summarise in the f_dose_observed
    and f_dose_draw functions to improve efficiency
  - plot gap time t0 instead of t0 + 1 in the f_fit_t0 function
  - plot the rounded value of di based on probability calculations
    alongside the observed value of di in the f_fit_di function
  - replace the residual plot with the fitted gap time bar chart in the
    f_fit_ti function
  - redefine row_id for vf1 if common_time_model is true in the
    f_dispensing_models function
  - use df and visitview to derive treatment_by_drug_df for real-time
    drug demand forecasting
  - update the examples of the f_fit_t0, f_fit_ki, and f_fit_ti
    functions
  - add trialsdt and cutoffdt to the output of the f_dose_observed and
    f_drug_demand functions
  - move the arrange operation of dosing_subject_newi out of the
    f_dose_draw_1 function into the f_dose_draw function to improve
    efficiency
  - combine the two summarise operation of dosing_summary_newi in the
    f_dose_draw_1 function to improve efficiency
  - replace the zero-inflated negative binomial distribution with the
    negative binomial distribution in the f_fit_ki function to avoid
    convergence issues
  - print cum_dispense_plot if showplot is TRUE in the f_dose_observed
    function
  - add colors = "Set2" to cum_dispense_plot, bar_t0_plot, bar_ti_plot,
    and bar_di_plot in the f_dose_observed function
  - remove the custom legend of cum_dispense_plot in the f_dose_observed
    and f_drug_demand functions
  - add parameter l to the f_dose_draw_1 function to improve efficiency
  - add structure and more details to the function parameters and output
    descriptions
  - combine the dosing_subject_t and dosing_summary_t steps in the
    f_dose_observed function to improve efficiency
  - replace target_days with dosing_schedule_df in the argument for the
    f_dispensing_models function
  - drop the creation of the status variable and use table instead of
    survfit for observed data summary in the f_fit_t0 function
  - add the handling of one observation case in the f_fit_ti function
  - remove the creation of the unames1 variable in the f_dose_draw
    function

                 Changes in version 0.1.0 (2023-10-23)                  

  - initial release