merge main to dev#89

Merged

rid4stat merged 30 commits intodevfrom

Feb 20, 2026

Collaborator

rid4stat commented Feb 20, 2026

No description provided.

Ridwan Olaniran and others added 30 commits

November 10, 2025 23:53


          Added ML models to model_generators

5f7ac36


          git merge progress-bar

cc0e4d9


          Implemented lasso for all outcomes (binary, continuous, survival)

1d511d6


          fixed input_validation check for simulate_continuous

f302359


          updated print.pmsims

1fd0e83


          updated model


          update code for validation study

2507d30


          updated engine for validation study

ecb3388


          updated engine for validation study

9f06be0


          Turn on silent model in mlpwr for validation study

eadf9bf


          updated engine

f37b3c3


          updated engine

7fd1054


          updated engine

f2015be


          updated engine

a74c5af


          update the bounds for mlpwr for tight bounds case

03dd5bb


          updated engine

3203e6a


          updated engine

0d3fecf


          updated engine

8a0b2da


          updated engine for failed grids in binary outcomes

ee8efd7


          update engine

4db8002


          Merge branch 'main' into validation-dev

712ec19


          Merge branch 'pmsims-ml' into main

dd414d5


          Merge recent changes from main into pmsims-ml

ba46907

Attempt to pull recent changes from main into pmsims-ml


          updated code for equality across all branches

ffab368


          Merge branch 'validation-dev' of https://github.com/pmsims-package/pm…

840de16

…sims into validation-dev


          updated output mlpwr-bs

19d139e


          Merge branch 'main' into validation-dev

b952267


          merge main into pmsims-ml

fb3dece

Merge remote-tracking branch 'origin/main' into pmsims-ml
# Please enter a commit message to explain why this merge is necessary,
# especially if it merges an updated upstream into a topic branch.
#
# Lines starting with '#' will be ignored, and an empty message aborts
# the commit.


          moved adaptive start values function to start_values.R

7e96ba4


          Merge pull request #87 from pmsims-package/pmsims-ml

c3613e2

Pmsims ml

rid4stat merged commit b35e7dc into dev

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pmsims/R/engines.R

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pmsims/R/engines.R

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           perf_n = as.numeric(ds$final$power) , 
        
           mlpwr_ds = list(data = ds$dat, fit = ds$fit, 
        
                           boundaries = ds$boundaries,  
        
                           final = ds$final,  
        
                           aggregate_fun = ds$aggregate_fun)

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start_n = start_values$start_min_sample_size,

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           start_min_sample_size <- start_values$min_sample_size 
        
           start_max_sample_size <- start_values$max_sample_size

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           core_names <- c("data_function", "model_function", "metric_function", "value_on_error", "test_data")

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           envs <- unique(list(environment(data_function), 
        
                               environment(model_function), 
        
                               environment(metric_function)))

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try(parallel::clusterExport(cl, varlist = objs, envir = e), silent = TRUE)

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           on.exit({ 
        
             try(parallel::stopCluster(cl), silent = TRUE) 
        
             try(doParallel::stopImplicitCluster(), silent = TRUE) 
        
           }, add = TRUE)

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           if (!"package:foreach" %in% search()) library(foreach, quietly = TRUE) 
        
           if (!"package:doParallel" %in% search()) library(doParallel, quietly = TRUE) 
        
           # Use foreach %dopar% on the already-registered cluster 
        
           vals <- foreach::foreach(i = seq_len(n_reps_per), .combine = c) %dopar% { 
        
             # Each worker will call single_run; single_run closes over data_function, etc. 
        
             tryCatch( 
        
               { 
        
                 dat <- data_function(n) 
        
                 fit <- model_function(dat) 
        
                 metric_function(test_data, fit, attr(model_function, "model")) 
        
               }, 
        
               error = function(e) value_on_error 
        
             )

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} else {

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(!budget && (p_hi - p_lo) >= tol && iter < max_iter)

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           test_n, 
        
           n_reps_total, 
        
           n_reps_per, 
        
           se_final, 
        
           min_sample_size, 
        
           max_sample_size, 
        
           target_performance, 
        
           c_statistic, 
        
           mean_or_assurance, 
        
           verbose, 
        
           data_function, 
        
           model_function, 
        
           metric_function, 
        
           value_on_error

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start_n = start_values$start_min_sample_size,

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pmsims/R/engines.R

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           mlpwrbs_max_sample_size <- ifelse((mlpwrbs_max_sample_size -  
        
                                               mlpwrbs_min_sample_size) < 5, 
        
                                             round(mlpwrbs_min_sample_size * 1.2), 
        
                                             mlpwrbs_max_sample_size)

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           perf_n = as.numeric(ds$final$power) , 
        
           mlpwr_ds = list(data = ds$dat, fit = ds$fit, 
        
                           boundaries = ds$boundaries,  
        
                           final = ds$final,  
        
                           aggregate_fun = ds$aggregate_fun)

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pmsims/R/engines.R

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}

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pmsims/R/input_validation.R

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           if (!is.null(expected_performance) && metric_lower %in% c("auc","r2","cindex")) {

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pmsims/R/input_validation.R

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"Requested minimum acceptable", metric_lower, "exceeds the expected",

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pmsims/R/metric_generators.R

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if (model %in% c("lm","glm")) {

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           glmnet_type <- switch(type, 
        
                                 response = "response", 
        
                                 link = "link", 
        
                                 lp = "link", 
        
                                 stop("Type '", type, "' not supported for lasso.")) 
        
           preds <- as.numeric(predict(fit, newx = x_mat, s = s_val, type = glmnet_type))

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pmsims/R/metric_generators.R

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if (!inherits(pr, "try-error")) return(pr)

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           stop("rf (ranger) prediction type not supported or unknown prediction structure.")

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           p <- pmin(pmax(as.numeric(preds), .Machine$double.eps), 1 - .Machine$double.eps)

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pmsims/R/metric_generators.R

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           stop("xgboost: direct survival probability matrix is not available from xgboost predictions. Consider using type = 'lp' and mapping to survival via a baseline if needed.")

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bs <- mean((y - y_hat) ^ 2)

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return(1 - mean((y - y_hat) ^ 2) / mean((y - mean(y)) ^ 2))

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pmsims/R/metric_generators.R

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mse <- sum((y_hat - y) ^ 2) / n

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pmsims/R/metric_generators.R

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           cf <- try(survival::concordancefit(y_surv, -1 * as.numeric(y_hat)), silent = TRUE)

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           cf <- try(stats::coef(survival::coxph(y_surv ~ as.numeric(y_hat))), silent = TRUE)

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           try({ 
        
             pred_surv <- predict_custom(x, NULL, fit, model, type = "survival") 
        
           }, silent = TRUE)

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           pred_surv <- try(survival:::predict.coxph(fit, data, type = "survival"), silent = TRUE)

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           if (!inherits(pr, "try-error") && !is.null(pr$predictions) && is.matrix(pr$predictions)) {

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           warning("survival_calib_slope_free: predicted survival probabilities not available for model '", model, "'. Returning NaN.")

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if (is.null(eval_time)) eval_time <- max(data$time[data$event == 1]) * 0.9999

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           pred_surv_at_time <- pmin(pmax(pred_surv_at_time, .Machine$double.eps), 1 - .Machine$double.eps)

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           fit_slope <- try(suppressWarnings(stats::glm( 
        
             y_obs ~ y_hat, 
        
             weights = w, 
        
             family = stats::binomial() 
        
           )), silent = TRUE)

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           if (class(try(survival::concordancefit(y_surv, y_hat), silent = TRUE))[1] == "try-error") {

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}

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           xgboost = function(d, nrounds = 100, params = list(objective = "binary:logistic", eval_metric = "logloss")) {

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num.threads = nthreads

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           xgboost = function(d, nrounds = 100, params = list(objective = "reg:squarederror", eval_metric = "rmse")) {

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x <- as.matrix(d[, setdiff(colnames(d), c("time", "event")), drop = FALSE])

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           xgboost = function(d, nrounds = 100, params = list(objective = "survival:cox", eval_metric = "cox-nloglik")) {

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x <- as.matrix(d[, setdiff(colnames(d), c("time", "event")), drop = FALSE])

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dtrain <- xgboost::xgb.DMatrix(data = x, label = label_time, weight = event)

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pmsims/R/model_generators.R

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           cv.ranger_tune <- function(data, formula, 
        
                                      type = c("regression", "classification", "survival"), 
        
                                      tune.parameters = c("mtry", "min.node.size"), 
        
                                      num.trees = 1000, 
        
                                      iters = 70, 
        
                                      iters.warmup = 30, 
        
                                      time.budget = NULL, 
        
                                      num.threads = parallel::detectCores() - 1, 
        
                                      measure = NULL, 
        
                                      build.final.model = TRUE, 
        
                                      show.info = TRUE, 
        
                                      seed = 123, 
        
                                      ...) {

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           stop("Please install required packages: ", paste(missing_pkgs, collapse = ", "))

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           if (length(surv_vars) < 2) stop("For survival, specify Surv(time, status) on LHS of formula.") 
        
           time_col <- surv_vars[1]; status_col <- surv_vars[2]

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if (!is.numeric(data[[resp]])) warning("Regression target is not numeric.")

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             if (length(task$task.desc$class.levels) == 2) measure_obj <- list(mlr::auc) 
        
             else measure_obj <- list(mlr::acc) 
        
           } else { # survival

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pmsims/R/model_generators.R

Line 255 in c3613e2

if (is.null(mm)) stop("Unknown mlr measure: ", mn)

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pmsims/R/model_generators.R

Line 260 in c3613e2

    
           } else if (is.list(measure) && all(sapply(measure, function(x) inherits(x, "Measure")))) {

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pmsims/R/model_generators.R

Line 263 in c3613e2

    
           stop("`measure` must be NULL, a character name, an mlr::Measure, or a list of Measures.")

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pmsims/R/model_generators.R

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pmsims/R/model_generators.R

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pmsims/R/model_generators.R

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	if (length(extra_args) > 0) tune_args <- c(tune_args, extra_args)

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pmsims/R/model_generators.R

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pmsims/R/model_generators.R

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	if (build.final.model && !is.null(tune_res$model)) out$model <- tune_res$model

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pmsims/R/model_generators.R

Lines 308 to 310 in c3613e2

    
           cat("Recommended parameters:\n"); print(x$recommended.pars) 
        
           cat("\nTop tuning results (first 6 rows):\n"); print(head(x$results)) 
        
           if (!is.null(x$model)) cat("\nFinal model attached as $model\n")

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pmsims/R/print.R

Line 4 in c3613e2

if (!inherits(x, "pmsims")) stop("Object is not of class 'pmsims'")

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pmsims/R/print.R

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has_cli <- requireNamespace("cli", quietly = TRUE)

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pmsims/R/print.R

Line 10 in c3613e2

has_tools <- requireNamespace("tools", quietly = TRUE)

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pmsims/R/print.R

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           bold   <- function(txt) if (has_crayon) crayon::bold(txt)   else txt 
        
           cyan   <- function(txt) if (has_crayon) crayon::cyan(txt)   else txt 
        
           blue   <- function(txt) if (has_crayon) crayon::blue(txt)   else txt 
        
           dimc   <- function(txt) if (has_crayon) crayon::silver(txt) else txt

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pmsims/R/print.R

Lines 22 to 23 in c3613e2

    
           if (is.null(title)) cli::cat_rule(width = scr_width) 
        
           else cli::cat_rule(center = as.character(title), width = scr_width)

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pmsims/R/print.R

Line 25 in c3613e2

if (!is.null(title)) cat(title, "\n", sep = "")

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pmsims/R/print.R

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           plural <- function(n, unit) sprintf("%d %s", n, if (n == 1) unit else paste0(unit, "s"))

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pmsims/R/print.R

Lines 34 to 36 in c3613e2

    
           secs <- if (inherits(simt, "difftime")) as.numeric(simt, units = "secs") 
        
           else if (is.numeric(simt)) as.numeric(simt) else return("<NA>") 
        
           if (is.na(secs)) return("<NA>")

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pmsims/R/print.R

Lines 38 to 40 in c3613e2

    
           d  <- secs %/% 86400; rem <- secs %% 86400 
        
           h  <- rem  %/% 3600 ; rem <- rem %% 3600 
        
           m  <- rem  %/% 60   ; s   <- rem %% 60

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pmsims/R/print.R

Lines 42 to 45 in c3613e2

    
           if (d > 0) parts <- c(parts, plural(d, "day")) 
        
           if (h > 0) parts <- c(parts, plural(h, "hour")) 
        
           if (m > 0) parts <- c(parts, plural(m, "minute")) 
        
           if (s > 0 || length(parts) == 0) parts <- c(parts, plural(s, "second"))

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pmsims/R/print.R

Lines 50 to 52 in c3613e2

    
           if (!is_present(v)) return("<NA>") 
        
           if (is.numeric(v) && length(v) == 1L) formatC(v, format = "f", digits = digits) 
        
           else paste(v, collapse = ", ")

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pmsims/R/print.R

Line 55 in c3613e2

if (!is_present(v)) return("<NA>")

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pmsims/R/print.R

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if (!is_present(metric)) return(NULL)

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pmsims/R/print.R

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           "cstatistic"  = "C-statistic", 
        
           "brier"       = "Brier score", 
        
           "rmse"        = "RMSE", 
        
           {label <- gsub("_", " ", metric) 
        
           if (has_tools) tools::toTitleCase(label) else label}

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pmsims/R/print.R

Lines 74 to 82 in c3613e2

    
           moa        <- x$mean_or_assurance %||% "mean" 
        
           model      <- x$model %||% NA_character_ 
        
           target     <- x$target_performance %||% x$minimum_acceptable_performance %||% NA_real_ 
        
           metric     <- x$metric %||% NA_character_ 
        
           metric_2   <- x$metric_2 %||% NA_character_ 
        
           min_n      <- x$min_n %||% NA 
        
           perf_at    <- x$perf_n %||% NA 
        
           perf2_at   <- x$metric_2_at_n %||% NA 
        
           simtime    <- x$simulation_time %||% NA

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pmsims/R/print.R

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r2 <- x$r2

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pmsims/R/print.R

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pmsims/R/print.R

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           "Outcome"                      = x$outcome, 
        
           "Predictor type"               = x$predictor_type, 
        
           "Number of predictors"         = x$parameters, 
        
           "Noise predictors"             = x$noise_parameters, 
        
           "Prevalence"                   = x$prevalence, 
        
           "Baseline hazard"              = x$baseline_hazard, 
        
           "Censoring rate"               = x$censoring_rate

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pmsims/R/print.R

Lines 98 to 104 in c3613e2

    
           if (is_present(cstatistic)) 
        
             inputs[["Expected large-sample performance"]] <- paste0("C-statistic ('cstatistic') = ", fmt_num(cstatistic, 3)) 
        
           if (is_present(r2)) 
        
             inputs[["Expected large-sample performance"]] <- paste0("R² ('r2') = ", fmt_num(r2, 3)) 
        
           if (is_present(metric) || is_present(target)) 
        
             inputs[["Target for chosen performance metric"]] <- paste0(pretty_metric(metric), " = ", fmt_num(target, 3)) 
        
           inputs[["Model"]]           <- model

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pmsims/R/print.R

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pmsims/R/print.R

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           "Final minimum sample size"  = bold(fmt_int(min_n)), 
        
           "Estimated performance at N" = paste0(fmt_num(perf_at, 3), 
        
                                                 " (", pretty_metric(metric), 
        
                                                 " = ", fmt_num(target, 3), ")"), 
        
           "Estimated other metric at N" = paste0(fmt_num(perf2_at, 3), 
        
                                                 " (", pretty_metric(metric_2), ")"), 
        
           "Model"                      = model, 
        
           "Mode"                       = if (tolower(moa) == "assurance") "Assurance" else "Mean", 
        
           "Running time"               = fmt_duration(simtime)

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pmsims/R/print.R

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pmsims/R/print.R

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           cat("    ", dimc(if (tolower(moa) == "assurance") 
        
             italic("Assurance mode ensures the target metric is met with high probability across repeated datasets.") 
        
             else 
        
             italic("Mean mode ensures the target metric is met on average across datasets.")), "\n", sep = "")

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pmsims/R/print.R

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}

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pmsims/R/simulate_wrappers.R

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           signal_parameters,                  # Predictors 
        
           noise_parameters = 0, 
        
           predictor_type = "continuous", 
        
           binary_predictor_prevalence = NULL, 
        
           outcome_prevalence,                 # Outcome 
        
           large_sample_cstatistic, 
        
           model = "glm",                      # Model 
        
           metric = "calibration_slope",       # Performance 
        
           minimum_acceptable_performance, 
        
           n_reps_total = 1000,                # Engine control 
        
           mean_or_assurance = "assurance", 
        
           ...

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pmsims/R/simulate_wrappers.R

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pmsims/R/simulate_wrappers.R

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pmsims/R/simulate_wrappers.R

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           signal_parameters, 
        
           noise_parameters = 0, 
        
           predictor_type = "continuous", 
        
           binary_predictor_prevalence = NULL, 
        
           large_sample_rsquared, 
        
           model = "lm", 
        
           metric = "calibration_slope", 
        
           minimum_acceptable_performance, 
        
           n_reps_total = 1000, 
        
           mean_or_assurance = "assurance", 
        
           ...

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pmsims/R/simulate_wrappers.R

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	expected_performance = large_sample_rsquared)

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pmsims/R/simulate_wrappers.R

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pmsims/R/simulate_wrappers.R

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           signal_parameters, 
        
           noise_parameters = 0, 
        
           predictor_type = "continuous", 
        
           binary_predictor_prevalence = NULL, 
        
           large_sample_cindex, 
        
           baseline_hazard = 1, 
        
           censoring_rate, 
        
           model = "coxph", 
        
           metric = "calibration_slope", 
        
           minimum_acceptable_performance, 
        
           n_reps_total = 1000, 
        
           mean_or_assurance = "assurance", 
        
           ...

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pmsims/R/simulate_wrappers.R

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pmsims/R/simulate_wrappers.R

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pmsims/R/simulate_wrappers.R

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}

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pmsims/R/start_values.R

Lines 3 to 5 in c3613e2

    
           #' @param results  
        
           #' @param p  
        
           #' @param mean

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pmsims/R/start_values.R

Lines 48 to 52 in c3613e2

    
           output, 
        
           aggregate_fun, 
        
           var_bootstrap, 
        
           target, 
        
           ci_q = 0.975

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pmsims/R/start_values.R

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pmsims/R/start_values.R

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           #' @param data_function  
        
           #' @param model_function  
        
           #' @param metric_function  
        
           #' @param value_on_error  
        
           #' @param start_n  
        
           #' @param test_n  
        
           #' @param n_reps_per  
        
           #' @param n_reps_total  
        
           #' @param target_performance  
        
           #' @param threshold  
        
           #' @param mean_or_assurance  
        
           #' @param c_statistic  
        
           #' @param parallel  
        
           #' @param cores  
        
           #' @param verbose

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pmsims/R/start_values.R

Lines 137 to 151 in c3613e2

    
           data_function, 
        
           model_function, 
        
           metric_function, 
        
           value_on_error, 
        
           start_n, 
        
           test_n, 
        
           n_reps_per, 
        
           n_reps_total, 
        
           target_performance, 
        
           threshold = 0.01, 
        
           mean_or_assurance = "mean", 
        
           c_statistic = NULL, 
        
           parallel = FALSE, 
        
           cores = 20, 
        
           verbose = FALSE

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pmsims/R/start_values.R

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pmsims/R/start_values.R

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           vals <- foreach::foreach(i = 1:n_reps_per, .combine = c) %dopar% { 
        
             single_run(n) 
        
           }

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pmsims/R/start_values.R

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	if (n_new == n_current) break

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pmsims/R/start_values.R

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           #' @param data_function  
        
           #' @param metric_function  
        
           #' @param target_performance  
        
           #' @param c_statistic  
        
           #' @param mean_or_assurance

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pmsims/R/start_values.R

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           data_function, 
        
           metric_function, 
        
           target_performance, 
        
           c_statistic = NULL, 
        
           mean_or_assurance = c("mean", "assurance")

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if (!is.numeric(baseline_prob) ||

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is.na(baseline_prob)) {

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	if (baseline_prob <= 0.2 &&

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	mean_or_assurance == "assurance") {

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pmsims/R/start_values.R

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           npar, 
        
           prevalence = NULL, 
        
           c_stat = NULL, 
        
           calib_slope = NULL, 
        
           epv_value = NULL, 
        
           outcome_type = c("binary", "survival", "continuous")

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pmsims/R/start_values.R

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R/engines.R

Comment on lines +8 to +22

    
                  test_n,

                  n_reps_total,

                  n_reps_per,

                  se_final,

                  min_sample_size,

                  max_sample_size,

                  target_performance,

                  c_statistic,

                  mean_or_assurance,

                  n_init,

                  verbose,

                  data_function,

                  model_function,

                  metric_function,

                  value_on_error

Contributor

github-actions bot Feb 20, 2026

[air] _{reported by reviewdog 🐶}

Suggested change

      
                test_n,
          
                n_reps_total,
          
                n_reps_per,
          
                se_final,
          
                min_sample_size,
          
                max_sample_size,
          
                target_performance,
          
                c_statistic,
          
                mean_or_assurance,
          
                n_init,
          
                verbose,
          
                data_function,
          
                model_function,
          
                metric_function,
          
                value_on_error
          
              test_n,
          
              n_reps_total,
          
              n_reps_per,
          
              se_final,
          
              min_sample_size,
          
              max_sample_size,
          
              target_performance,
          
              c_statistic,
          
              mean_or_assurance,
          
              n_init,
          
              verbose,
          
              data_function,
          
              model_function,
          
              metric_function,
          
              value_on_error

R/engines.R

+                  metric_function,
+                  value_on_error
               ) {

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github-actions bot Feb 20, 2026

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Suggested change

R/engines.R

		@@ -434,8 +31,25 @@ calculate_mlpwr <- function(
		mean_or_assurance = mean_or_assurance
		)

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R/engines.R

-                start_max_sample_size <- start_values$start_max_sample_size
+                # Adaptive starting values search
+                cat("Estimating first stage... (Adaptive starting value search algorithm)\n")
+                  start_values <- calculate_adaptive_bounds(

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Suggested change

      
                start_values <- calculate_adaptive_bounds(
          
              start_values <- calculate_adaptive_bounds(

R/engines.R

+                  model_function = model_function,
+                  metric_function = metric_function,
+                  value_on_error = NA,
+                  start_n =  start_values$start_min_sample_size,

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Suggested change

      
                start_n =  start_values$start_min_sample_size,
          
                start_n = start_values$start_min_sample_size,

R/engines.R

+                    utils::setTxtProgressBar(pb_txt, evaluations_used)
+                  }
+                }

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R/engines.R

+                ns <- asNamespace("mlpwr")
+                orig_print_progress <- get("print_progress", envir = ns)
+                assignInNamespace("print_progress", patched_print_progress, ns)

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R/engines.R

Comment on lines +195 to +196

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R/engines.R

		silent = FALSE
		)

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Suggested change

R/engines.R

		results[i, seq(1, length(perfs[[i]]$y), 1)] <- perfs[[i]]$y
		}

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