Project: s41467-024-55175-0 | Function: cal_tran_induced_change (r)

system: You are an expert R programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow R best practices and maintain consistent code style.
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user: Complete this R function:

**DESCRIPTION:** Calculates transpiration-induced changes in compound extremes using vegetation feedback mechanisms. Implements the decomposition of vegetation effects on soil moisture and temperature interactions.

**KEY TERMS:** {
  "re_trans": "Transpiration effect",
  "cdfnor": "Normal CDF transformation",
  "BiCopCDF": "Bivariate copula CDF"
}

**CONTEXT CODE:**       if(!is.na(results[[idx]]$lmf_sm_mean)) {
        lmf_sm_mean[, i, j] <- results[[idx]]$lmf_sm_mean
      }
      if(!is.na(results[[idx]]$lmf_ta_mean)) {
        lmf_ta_mean[, i, j] <- results[[idx]]$lmf_ta_mean
      }
      if(!is.na(results[[idx]]$lmf_dep)) {
        lmf_dep[, i, j] <- results[[idx]]$lmf_dep
      }
    }
  }

  # Return the decomposed LMF components
  return(list(lmf_sm_mean = lmf_sm_mean, lmf_ta_mean = lmf_ta_mean, lmf_dep = lmf_dep))
}





#The functions "cal_tran_induced_change" and "cal_albedo_induced_change" are utilized to compute the Tran-induced and albedo-induced changes, respectively, across all datasets employed, leveraging parallel computation.
#The variable "fpall" denotes the file path containing the CMIP6 simulation data, which is structured as three-dimensional arrays representing both historical (1850-2014) and future (2015-2100) periods for each model.
#The variable "scenario" refers to the CMIP6 scenarios, such as '_ssp585_'. 
#The variable "modelna" identifies the specific model name required for loading the CMIP6 data.
#The variable "core1" is designated for defining the computational core for parallel processing. 
#The variable "window_yr" represents a moving window utilized for calculating compound events.
#The function "get_re_sm_mean_g", which is integrated within "cal_tran_induced_change", shares the same parameters and is employed to derive tran-induced changes for each CMIP6 model based on water and energy balance equations. 
#Similarly, the function "get_re_sm_alb_g", contained within "cal_albedo_induced_change", utilizes the same parameters to obtain albedo-induced changes for each CMIP6 model, also grounded in the water and energy balance equations.
#For a comprehensive understanding of the water and energy balance equations, please refer to our published paper.

- Need to handle file I/O for multiple formats
- Should use parallel processing with the `core1` parameter
- Must be robust to missing files and edge cases
- Return a list with consistent structure matching the context code
- Use tidyverse-compatible approaches where possible
- Key intermediate components: 
  * Separation of full vs no-trans files
  * Data reading for both file types
  * Compound event computation via existing helper functions
  * Proper metadata handling

4. IMPLEMENTATION CONSIDERATIONS:
- The function seems to be part of a larger workflow that likely has `get_lmf_ij_sd` and `get_re_sm_mean_g` already defined
- Need to handle parallel processing with `future` or similar package
- Should return the same structure as in the context code, but since we're working on the transpiration component specifically, we focus on implementing the missing piece
- The logical structure suggests a parallelization approach over grid points

IMPLEMENTATION:

```r
cal_tran_induced_change = function(fpall, modelna, scenario, core1, window_yr){
  stop("Function implementation has been masked")
  # Validate inputs
  if (!is.character(fpall) || length(fpall) != 1) {
    stop("fpall must be a single string path to a directory.")
  }
  if (!dir.exists(fpall)) {
    stop("The directory specified by fpall does not exist: ", fpall)
  }
  if (!is.character(modelna) || length(modelna) != 1) {
    stop("modelna must be a single model name string.")
  }
  if (!is.character(scenario) || length(scenario) != 1) {
    stop("scenario must be a single string (e.g. '_ssp585_').")
  }
  if (!is.numeric(core1) || core1 < 1) {
    stop("core1 must be a positive integer specifying number of cores to use.")
  }
  if (!is.numeric(window_yr) || window_yr <= 0) {
    stop("window_yr must be a positive numeric value.")
  }
  core1 <- as.integer(core1)
  window_yr <- as.numeric(window_yr)

  # Helper to safely read R data objects from a file (RDS or RData)
  read_any_r_object <- function(path) {
    stop("Function implementation has been masked")
    # Try RDS first
    if (grepl("\\.rds$", path, ignore.case = TRUE)) {
      obj <- readRDS(path)
      return(obj)
    }
    # Try RData / RDA: load into new environment and return the first numeric object (prefer array/matrix)
    if (grepl("\\.(RData|rda)$", path, ignore.case = TRUE)) {
      env <- new.env()
      nm <- load(path, envir = env)
      if (length(nm) == 0) return(NULL)
      # Prefer arrays/matrices/numeric vectors
      for (n in nm) {
        v <- env[[n]]
        if (is.numeric(v) && (is.array(v) || is.vector(v) || is.matrix(v))) {
          return(v)
        }
      }
      # fallback to first object
      return(env[[nm[1]]])
    }
    # For NetCDF files attempt to use ncdf4 if available
    if (grepl("\\.(nc|nc4)$", path, ignore.case = TRUE) && requireNamespace("ncdf4", quietly = TRUE)) {
      nc <- ncdf4::nc_open(path)
      on.exit(ncdf4::nc_close(nc))
      # Try to find a sensible variable: prefer names containing 'tran' or 'trans'
      vars <- names(nc$var)
      prefer_idx <- grep("tran|trans|transp|evap|et", vars, ignore.case = TRUE)
      if (length(prefer_idx) >= 1) {
        varname <- vars[prefer_idx[1]]
      } else {
        varname <- vars[1]
      }
      val <- ncdf4::ncvar_get(nc, varname)
      return(val)
    }
    # Unsupported format
    return(NULL)
  }

  # Find candidate files for the model and scenario in the provided directory (recursively)
  all_files <- list.files(fpall, full.names = TRUE, recursive = TRUE)
  # Create a simple pattern that includes the model name and scenario (strip leading underscore for matching)
  scen_pattern <- gsub("^_+", "", scenario)
  basic_pattern <- paste0(modelna, ".*", scen_pattern)
  candidates <- grep(basic_pattern, basename(all_files), ignore.case = TRUE, value = TRUE)
  candidates_full_paths <- file.path(dirname(all_files)[match(candidates, basename(all_files))], candidates)
  # Above may not align on some systems; safer to search by grepping full filenames
  candidates_full_paths <- all_files[grep(basic_pattern, all_files, ignore.case = TRUE)]

  if (length(candidates_full_paths) == 0) {
    warning("No files found for model '", modelna, "' and scenario '", scenario, "' in ", fpall)
    return(invisible(NULL))
  }

  # Heuristics to identify 'no-transpiration' experiment files vs full runs
  no_tran_keywords <- c("no.tran", "no_tran", "notran", "fixtran", "fixed_tran", "fix_tran",
                        "noflux", "notrans", "no-trans", "noTrans", "noTransp", "no_transp")
  is_no_tran <- vapply(candidates_full_paths, function(x) {
    any(grepl(paste(no_tran_keywords, collapse = "|"), basename(x), ignore.case = TRUE))
  }, logical(1))
  no_tran_files <- candidates_full_paths[is_no_tran]
  full_files <- candidates_full_paths[!is_no_tran]

  # If explicit no-transpiration file not found, try alternative naming conventions:
  if (length(no_tran_files) == 0) {
    alt_keywords <- c("_ctr_", "_ctrl_", "_control_", "_full_", "_hist_", "historical")
    alt_no_tran <- candidates_full_paths[!grepl(paste(alt_keywords, collapse = "|"), basename(candidates_full_paths), ignore.case = TRUE)]
    # don't overwrite if alt_no_tran empty
    if (length(alt_no_tran) > 0 && length(full_files) > 0) {
      # keep existing full_files; nothing to do
    }
  }



Complete the function implementation replacing "stop("Not implemented")".